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1
Putra, Muhammad Daffa Arviano, Tawang Sahro Winanto, Retno Hendrowati, Aji Primajaya, and Faisal Dharma Adhinata. "A Comparative Analysis of Transfer Learning Architecture Performance on Convolutional Neural Network Models with Diverse Datasets." Komputika : Jurnal Sistem Komputer 12, no. 1 (2023): 1–11. http://dx.doi.org/10.34010/komputika.v12i1.8626.
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Deep learning is a branch of machine learning with many highly successful applications. One application of deep learning is image classification using the Convolutional Neural Network (CNN) algorithm. Large image data is required to classify images with CNN to obtain satisfactory training results. However, this can be overcome with transfer learning architectural models, even with small image data. With transfer learning, the success rate of a model is likely to be higher. Since there are many transfer learning architecture models, it is necessary to compare each model's performance results to find the best-performing architecture. In this study, we conducted three experiments on different datasets to train models with various transfer learning architectures. We then performed a comprehensive comparative analysis for each experiment. The result is that the DenseNet-121 architecture is the best transfer learning architecture model for various datasets.
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Dr. Pradeep Laxkar and Dr. Nilesh Jain. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2907–16. https://doi.org/10.32628/cseit25112764.
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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud-based machine learning (ML) architectures, highlighting the scalability features of various cloud platforms such as AWS, Azure, and GCP. This study also discusses emerging technologies like serverless computing, automated machine learning AutoMLL), and microservices-based architectures that enhance the scalability of the cloud environment. Furthermore, challenges such as data security, talent gaps, and resource allocation inefficiencies are also considered. The paper concludes by evaluating innovative approaches that drive scalable ML in cloud environments, providing insights into the future landscape of cloud-based machine learning. In conclusion, this scalable cloud-based architecture provides a robust and flexible solution for organizations looking to implement machine learning and data analysis workflows. By leveraging distributed computing, containerization, and serverless technologies, the architecture can efficiently manage large datasets and complex models while maintaining cost-efficiency, security, and adaptability to future needs.
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Journal, of Global Research in Electronics and Communications. "A Review of Scalable Machine Learning Architectures in Cloud Environments: Challenges and Innovations." Journal of Global Research in Electronics and Communications 1, no. 4 (2025): 7–11. https://doi.org/10.5281/zenodo.15115138.
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As the demand for machine learning (ML) and data analysis grows across industries, the need for scalable and efficient cloud-based architectures becomes critical. The increase in of data generation, along with the increasing demand for advanced analytics and machine learning (ML), has make necessary the development of scalable architectures in cloud environments. Cloud computing provides a flexible and scalable solution, allowing organizations to efficiently process large datasets and deploy complex ML models without traditional hardware limitations. The review paper explores the various cloud-based machine learning (ML) architectures, highlighting the scalability features of various cloud platforms such as AWS, Azure, and GCP. This study also discusses emerging technologies like serverless computing, automated machine learning AutoMLL), and microservices-based architectures that enhance the scalability of the cloud environment. Furthermore, challenges such as data security, talent gaps, and resource allocation inefficiencies are also considered. The paper concludes by evaluating innovative approaches that drive scalable ML in cloud environments, providing insights into the future landscape of cloud-based machine learning. In conclusion, this scalable cloud-based architecture provides a robust and flexible solution for organizations looking to implement machine learning and data analysis workflows. By leveraging distributed computing, containerization, and serverless technologies, the architecture can efficiently manage large datasets and complex models while maintaining cost-efficiency, security, and adaptability to future needs.
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Pukach, Pavlo. "Analysis of framework networks for sign detection in deep learning models." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 12 (December 15, 2022): 169–76. http://dx.doi.org/10.23939/sisn2022.12.169.
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This paper analyzes and compares modern deep learning models for the classification of MRI images of the knee joint. An analysis of modern deep computer vision architectures for feature extraction from MRI images is presented. This analysis was used to create applied architectures of machine learning models. These models are aimed at automating the process of diagnosing knee injuries in medical devices and systems. This work is devoted to different types of feature detection framework networks for machine learning architectures that perform magnetic resonance imaging (MRI) image classification of the knee. The resulting models were evaluated on the MRNet validation dataset, calculating the metrics (ROC-AUC), prediction accuracy, F1 score, and Cohen’s K-Kappa. The results of this work also show that Cohen's Kappa metric is important for evaluating models on the MRNet architecture because it provides a deeper understanding of the classification decisions of each model.
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Meda, Shefqet, and Ervin Domazet. "Advanced computer architecture optimization for machine learning/deep learning." CRJ, no. 5 (July 31, 2024): 28–41. http://dx.doi.org/10.59380/crj.vi5.5108.
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Abstract The recent progress in Machine Learning (Géron, 2022) and particularly Deep Learning (Goodfellow, 2016) models exposed the limitations of traditional computer architectures. Modern algorithms demonstrate highly increased computational demands and data requirements that most existing architectures cannot handle efficiently. These demands result in training speed, inference latency, and power consumption bottlenecks, which is why advanced methods of computer architecture optimization are required to enable the development of ML/DL-dedicated efficient hardware platforms (Engineers, 2019). The optimization of computer architecture for applications of ML/DL becomes critical, due to the tremendous demand for efficient execution of complex computations by Neural Networks (Goodfellow, 2016). This paper reviewed the numerous approaches and methods utilized to optimize computer architecture for ML/DL workloads. The following sections contain substantial discussion concerning the hardware-level optimizations, enhancements of traditional software frameworks and their unique versions, and innovative explorations of architectures. In particular, we discussed hardware including specialized accelerators, which can improve the performance and efficiency of a computation system using various techniques, specifically describing accelerators like CPUs (multicore) (Hennessy, 2017), GPUs (Hwu, 2015) and TPUs (Contributors, 2017), parallelism in multicore architectures, data movement in hardware systems, especially techniques such as caching and sparsity, compression, and quantization, other special techniques and configurations, such as using specialized data formats, and measurement sparsity. Moreover, this paper provided a comprehensive analysis of current trends in software frameworks, Data Movement optimization strategies (A.Bienz, 2021), sparsity, quantization and compression methods, using ML for architecture exploration, and, DVFS (Hennessy, 2017),, which provides strategies for maximizing hardware utilization and power consumption during training, machine learning, dynamic voltage, and frequency scaling, runtime systems. Finally, the paper discussed research opportunity directions and the possibilities of computer architecture optimization influence in various industrial and academic areas of ML/DL technologies. The objective of implementing these optimization techniques is to largely minimize the current gap between the computational needs of ML/DL algorithms and the current hardware’s capability. This will lead to significant improvements in training times, enable real-time inference for various applications, and ultimately unlock the full potential of cutting-edge machine learning algorithms.
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Airlangga, Gregorius. "A Hybrid CNN-RNN Model for Enhanced Anemia Diagnosis: A Comparative Study of Machine Learning and Deep Learning Techniques." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 2 (2024): 366. http://dx.doi.org/10.24014/ijaidm.v7i2.29898.
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This study proposes a hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model for the accurate diagnosis of anemia types, leveraging the strengths of both architectures in capturing spatial and temporal patterns in Complete Blood Count (CBC) data. The research involves the development and evaluation of various models of single-architecture deep learning (DL) models, specifically Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Fully Convolutional Network (FCN). The models are trained and validated using stratified k-fold cross-validation to ensure robust performance. Key metrics such as test accuracy are utilized to provide a comprehensive assessment of each model's performance. The hybrid CNN-RNN model achieved the highest test accuracy of 90.27%, surpassing the CNN (89.88%), FCN (85.60%), MLP (79.77%), and RNN (73.54%) models. The hybrid model also demonstrated superior performance in cross-validation, with an accuracy of 87.31% ± 1.77%. Comparative analysis highlights the hybrid model's advantages over single-architecture DL models, particularly in handling imbalanced data and providing reliable classifications across all anemia types. The results underscore the potential of advanced DL architectures in medical diagnostics and suggest pathways for further refinements, such as incorporating attention mechanisms or additional feature engineering, to enhance model performance. This study contributes to the growing body of knowledge on AI-driven medical diagnostics and presents a viable tool for clinical decision support in anemia diagnosis
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Praveen, Kumar Sridhar. "A Case Study on the Diminishing Popularity of Encoder-Only Architectures in Machine Learning Models." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 4 (2024): 22–27. https://doi.org/10.35940/ijitee.D9827.13040324.
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<strong>Abstract:</strong> This paper examines the shift from encoder-only to decoder and encoder-decoder models in machine learning, highlighting the decline in popularity of encoder-only architectures. It explores the reasons behind this trend, such as the advancements in decoder models that offer superior generative capabilities, flexibility across various domains, and enhancements in unsupervised learning techniques. The study also discusses the role of prompting techniques in simplifying model architectures and enhancing model versatility. By analyzing the evolution, applications, and shifting preferences within the research community and industry, this paper aims to provide insights into the changing landscape of machine learning model architectures.
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Walid, Abdullah, and Salah Ahmad. "A novel hybrid deep learning model for price prediction." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3420–31. https://doi.org/10.11591/ijece.v13i3.pp3420-3431.
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Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNNLSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
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Babhulkar, Mr Shubham. "Application of Machine Learning for Emotion Classification." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1567–72. http://dx.doi.org/10.22214/ijraset.2021.36459.
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In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creat- ing a real-time vision system which accomplishes the tasks of face detection, gender classification and emotion classification simultaneously in one blended step using our proposed CNN architecture. After presenting the details of the training pro- cedure setup we proceed to evaluate on standard benchmark sets. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset. Along with this we also introduced the very recent real-time enabled guided back- propagation visualization technique. Guided back-propagation uncovers the dynamics of the weight changes and evaluates the learned features. We argue that the careful implementation of modern CNN architectures, the use of the current regu- larization methods and the visualization of previously hidden features are necessary in order to reduce the gap between slow performances and real-time architectures. Our system has been validated by its deployment on a Care-O-bot 3 robot used during RoboCup@Home competitions. All our code, demos and pre- trained architectures have been released under an open-source license in our public repository.
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Siddesh, Kumar B., and Naduvinamani Onkarappa. "Machine Learning in Power Electronics: Focusing on Convolutional Neural Networks." International Journal of Computational Engineering and Management (IJCEM), A Peer Reviewed Refereed Multidisciplinary Research Journal 9, no. 1 (2021): 112–17. https://doi.org/10.5281/zenodo.14899610.
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Deep Neural Networks (DNNs) have revolutionized various fields, but their resource-intensive nature poses significant challenges for deployment, especially on edge devices with limited power and area budgets. This dissertation focuses on the development of efficient and low-power Very-Large-Scale Integration (VLSI) architectures for DNN accelerators, addressing the key bottlenecks in DNN hardware implementation. One of the major challenges in DNN hardware is the high computational cost associated with Multiply-Accumulate (MAC) operations and non-linear Activation Functions (AFs). While CORDIC-based architectures offer area and power efficiency, they suffer from low throughput. To address this, we propose a pipelined CORDIC-based design for MAC and AF operations, aiming to significantly improve performance without compromising area and power efficiency. To optimize resource utilization and maintain accuracy, we investigate the concept of mutual exclusivity among CORDIC stages. By carefully analyzing the impact of reduced pipeline stages on accuracy, we identify the optimal trade-off between performance and precision. This research contributes to the advancement of DNN hardware acceleration by providing innovative solutions to the challenges of power consumption, area efficiency, and throughput. The proposed architectures enable the deployment of DNN models on resource-constrained devices, paving the way for a new era of AI-powered applications.
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Dayan, Peter. "Recurrent Sampling Models for the Helmholtz Machine." Neural Computation 11, no. 3 (1999): 653–77. http://dx.doi.org/10.1162/089976699300016610.
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Many recent analysis-by-synthesis density estimation models of cortical learning and processing have made the crucial simplifying assumption that units within a single layer are mutually independent given the states of units in the layer below or the layer above. In this article, we suggest using either a Markov random field or an alternative stochastic sampling architecture to capture explicitly particular forms of dependence within each layer. We develop the architectures in the context of real and binary Helmholtz machines. Recurrent sampling can be used to capture correlations within layers in the generative or the recognition models, and we also show how these can be combined.
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Alshamrani, Khalaf, Hassan Alshamrani, F. F. Alqahtani, and Ali H. Alshehri. "Automation of Cephalometrics Using Machine Learning Methods." Computational Intelligence and Neuroscience 2022 (June 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/3061154.
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Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient’s lateral radiograph of the face was utilised to construct a tracing from the tracing of lines on the lateral radiograph of the face of the soft and hard structures (skin and bone, respectively). Certain cephalometric locations and characteristic lines and angles are indicated after the tracing is completed to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study’s computing procedure to be completed faster. To correlate a probability map with an input image, they combine an Autoencoder architecture with convolutional neural networks and Inception layers. These innovative architectures were demonstrated. When many models were compared, it was observed that they all performed admirably in this task.
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Shakil, Farhan, Sadia Afrin, Abdullah Al Mamun, et al. "HYBRID MULTI-MODAL DETECTION FRAMEWORK FOR ADVANCED PERSISTENT THREATS IN CORPORATE NETWORKS USING MACHINE LEARNING AND DEEP LEARNING." International Journal of Computer Science & Information System 10, no. 02 (2025): 6–20. https://doi.org/10.55640/ijcsis/volume10issue02-02.
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This study addresses the challenge of detecting Advanced Persistent Threats (APTs) in corporate networks by developing a hybrid multi-modal detection framework. We combine traditional machine learning models, deep learning architectures, and transformer-based models to improve the detection of sophisticated and stealthy cyber threats. A comprehensive dataset, consisting of network traffic and event logs, was processed through rigorous data preprocessing, feature engineering, and model development. The results show that the hybrid ensemble model, integrating Gradient Boosting and Transformer-based architectures, outperforms all other models, achieving 98.7% accuracy, 98.3% precision, and 97.9% recall, while maintaining a false positive rate below 1%. The model demonstrated exceptional performance in real-world simulations, detecting over 98% of malicious activities. Our findings highlight the importance of combining the strengths of classical and advanced machine learning techniques for effective APT detection and mitigation, providing a reliable, scalable solution for real-time cybersecurity.
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Abdullah, Walid, and Ahmed Salah. "A novel hybrid deep learning model for price prediction." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3420. http://dx.doi.org/10.11591/ijece.v13i3.pp3420-3431.
Full textAbstract:
Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNN-LSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
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Khrisat, Mohammad S., Anwar Alabadi, Saleh Khawatreh, Majed Omar Al-Dwairi, and Ziad A. Alqadi. "Autoregressive prediction analysis using machine deep learning." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1509–15. https://doi.org/10.11591/ijeecs.v27.i3.pp1509-1515.
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Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear autoregressive methods were introduced, and then the machine deep learning approach was used to apply prediction based on a selected input data set. The mean square error was calculated for various artificial neural networks architecture to reach the optimal architecture, which minimized the error. Different artificial neural network (ANN) architectures were trained, tested, and validated using various regressive models, a recommendation was raised according to the obtained and analyzed experimental results. It was shown that using the concepts of machine deep learning will enhance the response of the prediction model.
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Boya Marqas, Ridwan, Saman M. Almufty, Renas R. Asaad, and Dr Tamara Saad mohamed. "Advancing AI: A Comprehensive Study of Novel Machine Learning Architectures." International Journal of Scientific World 11, no. 1 (2025): 48–85. https://doi.org/10.14419/kwb24564.
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The rapid evolution of machine learning (ML) and artificial intelligence (AI) has led to groundbreaking advancements in computational models, empowering applications across diverse domains. This paper provides an in-depth exploration of advanced ML architectures, including transformers, Graph Neural Networks (GNNs), capsule networks, spiking neural networks (SNNs), and hybrid models. These architectures address the limitations of traditional models like convolutional and recurrent neural networks, offering superior accuracy, scalability, and efficiency for complex data. Key applications are discussed, ranging from healthcare diagnostics and drug discovery to financial fraud detection, autonomous systems, and logistics optimization. Despite their potential, these architectures face challenges such as computational overhead, scalability, and interpretability, necessitating interdisciplinary solutions. The paper also outlines future directions in edge computing, explainable AI, quantum machine learning, and few-shot learning, emphasizing the transformative role of advanced ML architectures in reshaping AI’s future.
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Adivarekar1, Pravin P., Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan, and Ravi Rastogi. "Automated machine learning and neural architecture optimization." Scientific Temper 14, no. 04 (2023): 1345–51. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.42.
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Automated machine learning (AutoML) and neural architecture optimization (NAO) represent pivotal components in the landscape of machine learning and artificial intelligence. This paper extensively explores these domains, aiming to delineate their significance, methodologies, cutting-edge techniques, challenges, and emerging trends. AutoML streamlines and democratizes machine learning by automating intricate procedures, such as algorithm selection and hyperparameter tuning. Conversely, NAO automates the design of neural network architectures, a critical aspect for optimizing deep learning model performance. Both domains have made substantial advancements, significantly impacting research, industry practices, and societal applications. Through a series of experiments, classifier accuracy, NAO model selection based on hidden unit count, and learning curve analysis were investigated. The results underscored the efficacy of machine learning models, the substantial impact of architectural choices on test accuracy, and the significance of selecting an optimal number of training epochs for model convergence. These findings offer valuable insights into the potential and limitations of AutoML and NAO, emphasizing the transformative potential of automation and optimization within the machine learning field. Additionally, this study highlights the imperative for further research to explore synergies between AutoML and NAO, aiming to bridge the gap between model selection, architecture design, and hyperparameter tuning. Such endeavors hold promise in opening new frontiers in automated machine learning methodologies.
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Assagaf, Idrus, Agus Sukandi, and Abdul Azis Abdillah. "Machine Failure Detection using Deep Learning." Recent in Engineering Science and Technology 1, no. 03 (2023): 26–31. http://dx.doi.org/10.59511/riestech.v1i03.21.
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This article focuses on the application of deep learning methods for failure prediction. Failure prediction plays a crucial role in various industries to prevent unexpected equipment failures, minimize downtime, and improve maintenance strategies. Deep learning techniques, known for their ability to capture complex patterns and dependencies in data, are explored in this study. The research employs Multi-Layer Perceptron as deep learning architectures. This model is trained on AI4I 2020 Predictive Maintenance data to develop accurate failure prediction models. Data preprocessing involves cleaning, feature engineering, and normalization to ensure the quality and suitability of the data for deep learning models. The dataset is split into training and testing sets for model development and evaluation. Performance evaluation metrics such as accuracy, ROC, and AUC are utilized to assess the models' effectiveness in predicting failures. The experimental results demonstrate the effectiveness of deep learning methods in failure prediction. The models showcase high accuracy and outperform SVM approaches, particularly in capturing intricate patterns and temporal dependencies within the data. The utilization of Multi-Layer Perceptron architecture further enhances the models' ability to capture long-term dependencies. However, challenges such as the availability of diverse and high-quality data, the selection of appropriate architecture and hyperparameters, and the interpretability of deep learning models remain significant considerations. Interpretability remains a challenge due to the inherent complexity and black-box nature of deep learning models. In conclusion, deep learning method offer significant potential for accurate failure prediction. Their ability to capture complex patterns and temporal dependencies makes them well-suited for analyzing operational and sensor data. Future research should focus on addressing challenges related to data quality, interpretability, and model optimization to further enhance the application of deep learning in failure prediction.
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Sridhar, Praveen Kumar, Nitin Srinivasan, Adithyan Arun Kumar, Gowthamaraj Rajendran, and Kishore Kumar Perumalsamy. "A Case Study on the Diminishing Popularity of Encoder-Only Architectures in Machine Learning Models." International Journal of Innovative Technology and Exploring Engineering 13, no. 4 (2024): 22–27. http://dx.doi.org/10.35940/ijitee.d9827.13040324.
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This paper examines the shift from encoder-only to decoder and encoder-decoder models in machine learning, highlighting the decline in popularity of encoder-only architectures. It explores the reasons behind this trend, such as the advancements in decoder models that offer superior generative capabilities, flexibility across various domains, and enhancements in unsupervised learning techniques. The study also discusses the role of prompting techniques in simplifying model architectures and enhancing model versatility. By analyzing the evolution, applications, and shifting preferences within the research community and industry, this paper aims to provide insights into the changing landscape of machine learning model architectures.
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Bangar Raju Cherukuri and Senior Web Developer. "Scalable machine learning model deployment using serverless cloud architectures." World Journal of Advanced Engineering Technology and Sciences 5, no. 1 (2022): 087–101. https://doi.org/10.30574/wjaets.2022.5.1.0025.
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Implementations of the developed ML models are related to important questions of scale, resource, and management. This work investigates the use of serverless cloud models to address these issues and enhance and optimize the deployment, scalability, and maintenance of ML models. Reviewing main serverless platforms and their compatibility with different ML development and usage stages, the research compares the effectiveness, cost, and adaptability to the common application deployment practices. The study in this paper employs case and performance analysis to show and explain how serverless solutions can cut infrastructure costs and reduce the need for scaling and maintenance, among others. This paper outlines guidelines for implementing serverless technologies in ML applications and areas of concern that organizations might expect. Consequently, this research adds to the existing literature on deploying ML-based applications in the cloud while providing useful findings for developers and organizations interested in efficient, cost-effective solutions.
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Yerraginnela Shravani. "Enhanced Heart Disease Prediction Using Advanced Machine Learning and Deep Learning Models." Journal of Information Systems Engineering and Management 10, no. 5s (2025): 768–85. https://doi.org/10.52783/jisem.v10i5s.771.
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Heart disease prediction is a critical area in healthcare, where accurate and timely diagnosis can lead to better patient outcomes and reduced mortality rates. This study compares the performance of various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks, alongside advanced deep learning models such as Convolutional Neural Networks (CNN) and VGG16, a pre-trained deep learning architecture. The models are evaluated using precision, recall, F1-score, and accuracy, with accuracy being the primary metric for comparison. Experimental results demonstrate that while traditional machine learning models like Random Forest and Logistic Regression perform adequately, deep learning models, particularly CNN and VGG16, excel in predictive accuracy and other performance metrics. Among all models, CNN and VGG16 deliver superior results, with VGG16 slightly outperforming CNN in terms of precision and recall due to its ability to leverage pre-trained features and deeper architecture.The findings highlight the efficacy of deep learning techniques, especially VGG16, in heart disease prediction, emphasizing their ability to capture complex patterns and improve diagnostic accuracy. This study provides valuable insights into the potential of leveraging state-of-the-art deep learning architectures for enhancing predictive models in healthcare applications, setting the stage for future real-time diagnostic tools.
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Sundaram, Aishwarya, Hema Subramaniam, Siti Hafizah Ab Hamid, and Azmawaty Mohamad Nor. "An adaptive data-driven architecture for mental health care applications." PeerJ 12 (March 29, 2024): e17133. http://dx.doi.org/10.7717/peerj.17133.
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Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.
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Imran, Sheik, and Pradeep N. "A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images." International Journal of Health Sciences and Research 14, no. 1 (2024): 201–13. http://dx.doi.org/10.52403/ijhsr.20240124.
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Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, classification, and disease diagnosis. The ensemble models can be divided into those based on decision fusion strategies, bagging, boosting, stacking, negative correlation, explicit/implicit ensembles, homogeneous/heterogeneous ensembles, and explicit/implicit ensembles. In comparison to shallow or traditional, machine learning models and deep learning architectures are currently performing better. Also, a brief discussion of the various ensemble models used in CT images is provided. We wrap up this work by outlining a few possible avenues for further investigation. Key words: Computed Tomography, Ensemble, Deep learning, Machine Learning.
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Polson, Sarah, and Vadim Sokolov. "Kolmogorov GAM Networks Are All You Need!" Entropy 27, no. 6 (2025): 593. https://doi.org/10.3390/e27060593.
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Kolmogorov GAM (K-GAM) networks have been shown to be an efficient architecture for both training and inference. They are additive models with embeddings that are independent of the target function of interest. They provide an alternative to Transformer architectures. They are the machine learning version of Kolmogorov’s superposition theorem (KST), which provides an efficient representation of multivariate functions. Such representations are useful in machine learning for encoding dictionaries (a.k.a. “look-up” tables). KST theory also provides a representation based on translates of the Köppen function. The goal of our paper is to interpret this representation in a machine learning context for applications in artificial intelligence (AI). Our architecture is equivalent to a topological embedding, which is independent of the function, together with an additive layer that uses a generalized additive model (GAM). This provides a class of learning procedures with far fewer parameters than current deep learning algorithms. Implementation can be parallelizable, which makes our algorithms computationally attractive. To illustrate our methodology, we use the iris data from statistical learning. We also show that our additive model with non-linear embedding provides an alternative to Transformer architectures, which, from a statistical viewpoint, are kernel smoothers. Additive KAN models, therefore, provide a natural alternative to Transformers. Finally, we conclude with directions for future research.
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Chada, Govind. "Machine Learning Models for Abnormality Detection in Musculoskeletal Radiographs." Reports 2, no. 4 (2019): 26. http://dx.doi.org/10.3390/reports2040026.
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Increasing radiologist workloads and increasing primary care radiology services make it relevant to explore the use of artificial intelligence (AI) and particularly deep learning to provide diagnostic assistance to radiologists and primary care physicians in improving the quality of patient care. This study investigates new model architectures and deep transfer learning to improve the performance in detecting abnormalities of upper extremities while training with limited data. DenseNet-169, DenseNet-201, and InceptionResNetV2 deep learning models were implemented and evaluated on the humerus and finger radiographs from MURA, a large public dataset of musculoskeletal radiographs. These architectures were selected because of their high recognition accuracy in a benchmark study. The DenseNet-201 and InceptionResNetV2 models, employing deep transfer learning to optimize training on limited data, detected abnormalities in the humerus radiographs with 95% CI accuracies of 83–92% and high sensitivities greater than 0.9, allowing for these models to serve as useful initial screening tools to prioritize studies for expedited review. The performance in the case of finger radiographs was not as promising, possibly due to the limitations of large inter-radiologist variation. It is suggested that the causes of this variation be further explored using machine learning approaches, which may lead to appropriate remediation.
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Mamidi, Sundeep Reddy. "Securing Multi-Cloud Architectures: A Machine Learning Perspective." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (2024): 233–47. http://dx.doi.org/10.60087/jaigs.v2i1.160.
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Multi-cloud computing, the utilization of multiple cloud computing services in a single heterogeneous architecture, has gained significant traction in recent years due to its potential for enhancing flexibility, resilience, and performance. This paper provides an overview of multi-cloud computing, exploring its key concepts, advantages, challenges, and best practices. It examines the motivations behind adopting multi-cloud strategies, the various deployment models, management approaches, and emerging trends. Additionally, the paper discusses the implications of multi-cloud computing for security, interoperability, and vendor lock-in. Through a comprehensive analysis, this paper aims to offer insights into the complexities and opportunities associated with multi-cloud environments.
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Kumar, M. Rupesh, Susmitha Vekkot, S. Lalitha, et al. "Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures." Sensors 22, no. 23 (2022): 9311. http://dx.doi.org/10.3390/s22239311.
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Dementia affects the patient’s memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.
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PUKACH, Pavlo. "REVIEW AND ANALYSIS OF BASIC FEATURE DETECTION NETWORKS FOR CLASSIFICATION OF MRI IMAGES IN DEEP LEARNING MODELS." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6(1) (2022): 183–87. http://dx.doi.org/10.31891/2307-5732-2022-315-6-183-187.
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This paper presents an evaluation of modern deep learning models for the classification of MRI images of the knee joint. Among all the work related to this research, there have been several attempts to retrain the original MRNet model on more modern computer vision architectures. Also, no attempt has yet been reported to document the incremental improvement in MRNet prediction accuracy using newer computer vision architectures. This paper presents a comparative analysis of modern deep architectures of computer vision for extracting features from MRI images of the knee joint in the tasks of classification of injuries and anomalies of the knee. Such an analysis is needed, at least as a guide to creating applied architectures of machine learning models aimed at automated diagnosis of knee injuries in medical devices and systems. In the field of artificial intelligence, deep learning (DL) algorithms can be applied directly to many different musculoskeletal radiology tasks, including image reconstruction, synthetic imaging, tissue segmentation, and diagnosis and detection of musculoskeletal disease characteristics on radiographs, ultrasound , CT and MRI images. Ideally, such systems should also help radiologists focus on rare diseases as well as very complex abnormalities. At the same time, the task of automating the process of diagnosing typical injuries and anomalies is set. The level of confidence in the result of prediction should be similar to the conclusions of commissions of expert radiologists. To frame such a benchmarking analysis, this paper compares the performance of the basic MRNet architecture for the knee MRI image classification task, using various state-of-the-art computer vision architectures as framework networks for feature extraction. It also demonstrates a gradual increase in the prediction accuracy of these models in accordance with the evolution of the framework models themselves. A rather important aspect of the presented research is the fact that all machine learning models developed and trained in the considered experiment have a unified architecture, except for the feature extraction framework, and they were all trained from scratch using the same model parameters and training parameters. In addition, the model estimation strategies in this work use an additional metric that has not yet been measured and compared in any related work, namely Cohen’s Kappa metric. This metric is significant because the MRNet dataset used in this paper is not balanced.
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Aliyu, M. Abdu, M. Mokji Musa, and U. Sheikh Usman. "Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning." International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (2020): 670–83. https://doi.org/10.11591/ijai.v9.i4.pp670-683.
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Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security.
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Shin, Jihye, Hyeonjoon Moon, Chang-Jae Chun, Taeyong Sim, Eunhee Kim, and Sujin Lee. "Enhanced Data Processing and Machine Learning Techniques for Energy Consumption Forecasting." Electronics 13, no. 19 (2024): 3885. http://dx.doi.org/10.3390/electronics13193885.
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Energy consumption plays a significant role in global warming. In order to achieve carbon neutrality and enhance energy efficiency through a stable energy supply, it is necessary to pursue the development of innovative architectures designed to optimize and analyze time series data. Therefore, this study presents a new architecture that highlights the critical role of preprocessing in improving predictive performance and demonstrates its scalability across various energy domains. The architecture, which discerns patterns indicative of time series characteristics, is founded on three core components: data preparation, process optimization methods, and prediction. The core of this architecture is the identification of patterns within the time series and the determination of optimal data processing techniques, with a strong emphasis on preprocessing methods. The experimental results for heat energy demonstrate the potential for data optimization to achieve performance gains, thereby confirming the critical role of preprocessing. This study also confirms that the proposed architecture consistently enhances predictive outcomes, irrespective of the model employed, through the evaluation of five distinct prediction models. Moreover, experiments extending to electric energy validate the architecture’s scalability and efficacy in predicting various energy types using analogous input variables. Furthermore, this research employs explainable artificial intelligence to elucidate the determinants influencing energy prediction, thereby contributing to the management of low-carbon energy supply and demand.
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Rajeev Reddy Chevuri. "The Role of GPUs in Accelerating Machine Learning Workloads." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2676–84. https://doi.org/10.32628/cseit251127424.
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This article presents a comprehensive overview of Graphics Processing Units (GPUs) and their transformative role in accelerating machine learning workloads. Starting with an explanation of the fundamental architectural differences between GPUs and CPUs, the article explores how the parallel processing capabilities of GPUs enable dramatic improvements in training deep learning models. The discussion covers GPU applications across convolutional neural networks, transformer architectures, and multi-GPU training strategies. Beyond training, the article examines GPU acceleration in inference, scientific computing, data preprocessing, and emerging application domains. Cost-effective deployment strategies are also addressed, including cloud versus on-premises considerations, container orchestration, dynamic resource allocation, and computational optimization techniques. Throughout, the article highlights how GPUs have fundamentally altered what is computationally feasible in artificial intelligence, enabling complex models and applications that would otherwise remain theoretical.
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Corceiro, Ana, Nuno Pereira, Khadijeh Alibabaei, and Pedro D. Gaspar. "Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification." Algorithms 17, no. 1 (2023): 19. http://dx.doi.org/10.3390/a17010019.
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The global population’s rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future.
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Thakurdesai, Amogh. "Stock Price Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 10 (2024): 1432–38. http://dx.doi.org/10.22214/ijraset.2024.64895.
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Stock price prediction is a critical and challenging task in the field of financial analysis and investment decision making. The power of artificial intelligence and machine learning is leveraged to offer more reliable predictions for investors and financial institutions. This study explores and implements various machine learning architectures, including tree-based models such as decision trees, random forests and neural network based models like Recurrent Neural Networks (RNNs), LSTM (Long short-term memory) networks, GRU(Gated Recurrent Unit) and LSTM-CNN hybrid. The implementation of the above mentioned machine learning algorithms are assessed across various comparison criteria, which include accuracy, robustness, generalization and computational efficiency
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Chennupati, Gopinath, Nandakishore Santhi, Phill Romero, and Stephan Eidenbenz. "Machine Learning–enabled Scalable Performance Prediction of Scientific Codes." ACM Transactions on Modeling and Computer Simulation 31, no. 2 (2021): 1–28. http://dx.doi.org/10.1145/3450264.
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Hardware architectures become increasingly complex as the compute capabilities grow to exascale. We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.
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Khrisat, Mohammad S., Anwar Alabadi, Saleh Khawatreh, Majed Omar Al-Dwairi, and Ziad A. Alqadi. "Autoregressive prediction analysis using machine deep learning." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1509. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1509-1516.
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Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear autoregressive methods were introduced, and then the machine deep learning approach was used to apply prediction based on a selected input data set. The mean square error was calculated for various artificial neural networks architecture to reach the optimal architecture, which minimized the error. Different artificial neural network (ANN) architectures were trained, tested, and validated using various regressive models, a recommendation was raised according to the obtained and analyzed experimental results. It was shown that using the concepts of machine deep learning will enhance the response of the prediction model.
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Xiao, Yuxin, Yuanning Zhai, Lei Zhou, Yiming Yin, Hengnian Qi, and Chu Zhang. "Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars." Foods 14, no. 12 (2025): 2091. https://doi.org/10.3390/foods14122091.
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Hyperspectral imaging (HSI) has broad applications for detecting the soluble solids content (SSC) of fruits. This study explores the integration of HSI with machine learning and deep learning to predict SSC in two mandarin varieties: Ponkan and Tianchao. Traditional machine learning models (support vector machines and partial least squares regression) and deep learning models (convolutional neural networks, long short-term memory, and Transformer architectures) were evaluated for SSC prediction performance. Combined models that integrated different deep learning architectures were also explored. Results revealed varietal differences in prediction performance. For Ponkan mandarins, the best SSC prediction model was achieved by partial least squares regression, outperforming deep learning models. In contrast, for Tianchao mandarins, the deep learning model based on convolutional neural network slightly surpassed the traditional model. SHapley Additive exPlanations (SHAP) analysis indicated that the influential wavelengths varied between varieties, suggesting differences in key spectral features for SSC prediction. These findings highlight the potential of combining HSI with advanced modeling for citrus SSC prediction, while emphasizing the need for variety-specific models. Future research should focus on developing more robust and generalized prediction models by incorporating a broader range of citrus varieties and exploring the impact of varietal characteristics on model performance.
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Dipanjan De et al.,, Dipanjan De et al ,. "Optimizing IoT Cloud Architectures for Pipelining Data through Machine Learning Models." International Journal of Computer Networking, Wireless and Mobile Communications 9, no. 1 (2019): 35–44. http://dx.doi.org/10.24247/ijcnwmcjun20195.
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Md Ismail Hossain Siddiqui, Anamul Haque Sakib, Sanjida Akter, Jesika Debnath, and Mohammad Rasel Mahmud. "Comparative analysis of traditional machine learning Vs deep learning for sleep stage classification." International Journal of Science and Research Archive 15, no. 1 (2025): 1778–89. https://doi.org/10.30574/ijsra.2025.15.1.1159.
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Sleep stage classification is crucial for diagnosing sleep disorders and understanding sleep physiology. This study presents a comprehensive comparison between traditional machine learning algorithms and deep learning architectures using EEG recordings from the Physionet database. We extract 23 time and frequency domain features from each 30-second EEG segment and evaluate their performance across SVM, Random Forest, k-NN, and Gradient Boosting against CNN, LSTM, and hybrid CNN-LSTM models with attention mechanisms. Our results demonstrate that while traditional approaches achieve 82.4% accuracy with significant interpretability advantages, deep learning models reach 89.7% accuracy but require substantially more computational resources. The CNN-LSTM architecture with attention mechanisms performs best across all sleep stages, particularly for discriminating between similar stages like S1 and REM. However, traditional Random Forest classifiers offer competitive performance for resource-constrained applications with only 15% longer inference time. This comparative framework provides valuable insights for researchers and clinicians selecting appropriate methodologies for sleep analysis based on their specific requirements for accuracy, interpretability, and computational efficiency.
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Albattah, Waleed, and Saleh Albahli. "Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures." Applied Sciences 12, no. 19 (2022): 10155. http://dx.doi.org/10.3390/app121910155.
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Handwritten character recognition is a computer-vision-system problem that is still critical and challenging in many computer-vision tasks. With the increased interest in handwriting recognition as well as the developments in machine-learning and deep-learning algorithms, researchers have made significant improvements and advances in developing English-handwriting-recognition methodologies; however, Arabic handwriting recognition has not yet received enough interest. In this work, several deep-learning and hybrid models were created. The methodology of the current study took advantage of machine learning in classification and deep learning in feature extraction to create hybrid models. Among the standalone deep-learning models trained on the two datasets used in the experiments performed, the best results were obtained with the transfer-learning model on the MNIST dataset, with 0.9967 accuracy achieved. The results for the hybrid models using the MNIST dataset were good, with accuracy measures exceeding 0.9 for all the hybrid models; however, the results for the hybrid models using the Arabic character dataset were inferior.
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Rakesh Chowdary Ganta. "Scaling machine learning and operations research models for omni-channel retail in the cloud: A framework for real-time decision optimization." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1842–59. https://doi.org/10.30574/wjarr.2025.26.2.1793.
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This article examines how cloud-native architectures enable retailers to scale machine learning and operations research models across omni-channel environments. It explores the transformation from monolithic on-premise systems to flexible cloud platforms, highlighting how distributed computing frameworks address the computational demands of retail-scale ML model training and inference. The discussion covers architectural patterns for real-time data processing, distributed training techniques, auto-scaling inference architectures, and parallelization strategies for complex optimization problems. The integration of predictive ML insights with prescriptive OR optimization is presented as a critical capability, with various integration patterns examined including sequential, feedback loop, stochastic, and joint learning approaches. Data pipelines connecting predictive and prescriptive models are explored alongside event-driven architectures for cross-channel decision workflows and API design patterns for unified retail intelligence systems. Implementation challenges and technical debt considerations complete the analysis, focusing on both architectural principles and organizational factors that influence successful adoption of cloud-scaled retail analytics
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Sushma, Prof Ksn, Nishant Upadhyay, Ajeet Singh, Prasenjeet Kr Singh, and Tanzeelah Firdaus. "Plant Disease Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1099–101. http://dx.doi.org/10.22214/ijraset.2022.41451.
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Abstract: Early diagnosis of plant diseases is critical since they have a substantial impact on the growth of their unique species. Many Machine Learning (ML) models have been used to detect and categorize plant diseases, but recent breakthroughs in a subset of ML called Deep Learning (DL) look to hold a lot of promise in terms of improved accuracy. A variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments. In addition, a number of performance measurements are used to evaluate various architectures/techniques. This article explains how to use DL models to display a variety of plant diseases. Furthermore, several research gaps are identified, allowing for improved efficiency in detecting plant illnesses even before issues emerge. Keywords: Plant disease; deep learning; convolutional neural networks (CNN), Google Net Architecture, Tensorflow, and PyTorch are some of the tools that can be used;
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Cohn, D. A., Z. Ghahramani, and M. I. Jordan. "Active Learning with Statistical Models." Journal of Artificial Intelligence Research 4 (March 1, 1996): 129–45. http://dx.doi.org/10.1613/jair.295.
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For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.
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Pozas-Kerstjens, Alejandro, Senaida Hernández-Santana, José Ramón Pareja Monturiol, et al. "Privacy-preserving machine learning with tensor networks." Quantum 8 (July 25, 2024): 1425. http://dx.doi.org/10.22331/q-2024-07-25-1425.
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Tensor networks, widely used for providing efficient representations of low-energy states of local quantum many-body systems, have been recently proposed as machine learning architectures which could present advantages with respect to traditional ones. In this work we show that tensor-network architectures have especially prospective properties for privacy-preserving machine learning, which is important in tasks such as the processing of medical records. First, we describe a new privacy vulnerability that is present in feedforward neural networks, illustrating it in synthetic and real-world datasets. Then, we develop well-defined conditions to guarantee robustness to such vulnerability, which involve the characterization of models equivalent under gauge symmetry. We rigorously prove that such conditions are satisfied by tensor-network architectures. In doing so, we define a novel canonical form for matrix product states, which has a high degree of regularity and fixes the residual gauge that is left in the canonical forms based on singular value decompositions. We supplement the analytical findings with practical examples where matrix product states are trained on datasets of medical records, which show large reductions on the probability of an attacker extracting information about the training dataset from the model&apos;s parameters. Given the growing expertise in training tensor-network architectures, these results imply that one may not have to be forced to make a choice between accuracy in prediction and ensuring the privacy of the information processed.
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Yermolenko, Ruslan, Denys Klekots, and Olga Gogota. "Development of an algorithm for detecting commercial unmanned aerial vehicles using machine learning methods." Naukovij žurnal «Tehnìka ta energetika» 15, no. 2 (2024): 33–45. http://dx.doi.org/10.31548/machinery/2.2024.33.
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This study aimed to train algorithms for detecting commercial unmanned aerial vehicles using machine learning techniques. Neural network architectures YOLOv8 and MobileNetV3 were used to detect unmanned aerial vehicles in images and videos. The models used were pre-trained on the ImageNet dataset and then refined on the SimUAV dataset containing images of four types of drones (Parrot A.R. Drone 2.0; DJI Inspire I; DJI Mavic 2 Pro; and DJI Phantom 4 Pro), different sizes and in eight different background locations. The study confirmed that the combination of the YOLOv8 and MobileNetV3 architectures has significant potential for detecting commercial unmanned aerial vehicles in various types of images. The trained models demonstrated high performance in the recognition and classification of unmanned aerial vehicles, achieving an average detection accuracy (at an IoU threshold of 50%) of 0.747 and 0.909 for the MobileNetV3_Small and MobileNetV3_Large models, respectively. This demonstrates the high efficiency and accuracy of the models in detecting objects on the test data. The results of the study also included the values of the binary cross-entropy metric, which were 0.308 and 0.216, respectively, indicating the high accuracy of the models in object classification and confirming the high efficiency and reliability of these models in working with objects on the test data. During the study, the MobileNetV3_Large model showed more accurate results than MobileNetV3_Small, which indicates its higher efficiency in detecting and classifying aircraft. The obtained results confirm the prospects of applying machine learning methods in the field of monitoring and security systems, which reliably detect and track unmanned aerial vehicles in various conditions. The high performance of the trained models demonstrates their effectiveness in real-world operating conditions, making them a valuable tool for solving important control and supervision tasks
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Dara, Omer Asghar, Jose Manuel Lopez-Guede, Hasan Issa Raheem, Javad Rahebi, Ekaitz Zulueta, and Unai Fernandez-Gamiz. "Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey." Applied Sciences 13, no. 14 (2023): 8298. http://dx.doi.org/10.3390/app13148298.
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Alzheimer’s is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual’s cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer’s disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The utilization of computer-aided analysis models based on machine learning and artificial intelligence has the potential to significantly enhance the exploration of various neuroimaging methods and non-image biomarkers. This study conducts a comparative assessment of more than 80 publications that have been published since 2017. Alzheimer’s disease detection is facilitated by utilizing fundamental machine learning architectures such as support vector machines, decision trees, and ensemble models. Furthermore, around 50 papers that utilized a specific architectural or design approach concerning Alzheimer’s disease were examined. The body of literature under consideration has been categorized and elucidated through the utilization of data-related, methodology-related, and medical-fostering components to illustrate the underlying challenges. The conclusion section of our study encompasses a discussion of prospective avenues for further investigation and furnishes recommendations for future research activities on the diagnosis of Alzheimer’s disease.
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Sekhar, Ravi, Nitin Solke, and Pritesh Shah. "Lean Manufacturing Soft Sensors for Automotive Industries." Applied System Innovation 6, no. 1 (2023): 22. http://dx.doi.org/10.3390/asi6010022.
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Lean and flexible manufacturing is a matter of necessity for the automotive industries today. Rising consumer expectations, higher raw material and processing costs, and dynamic market conditions are driving the auto sector to become smarter and agile. This paper presents a machine learning-based soft sensor approach for identification and prediction of lean manufacturing (LM) levels of auto industries based on their performances over multifarious flexibilities such as volume flexibility, routing flexibility, product flexibility, labour flexibility, machine flexibility, and material handling. This study was based on a database of lean manufacturing and associated flexibilities collected from 46 auto component enterprises located in the Pune region of Maharashtra State, India. As many as 29 different machine learning models belonging to seven architectures were explored to develop lean manufacturing soft sensors. These soft sensors were trained to classify the auto firms into high, medium or low levels of lean manufacturing based on their manufacturing flexibilities. The seven machine learning architectures included Decision Trees, Discriminants, Naive Bayes, Support Vector Machine (SVM), K-nearest neighbour (KNN), Ensembles, and Neural Networks (NN). The performances of all models were compared on the basis of their respective training, validation, testing accuracies, and computation timespans. Primary results indicate that the neural network architectures provided the best lean manufacturing predictions, followed by Trees, SVM, Ensembles, KNN, Naive Bayes, and Discriminants. The trilayered neural network architecture attained the highest testing prediction accuracy of 80%. The fine, medium, and coarse trees attained the testing accuracy of 60%, as did the quadratic and cubic SVMs, the wide and narrow neural networks, and the ensemble RUSBoosted trees. Remaining models obtained inferior testing accuracies. The best performing model was further analysed by scatter plots of predicted LM classes versus flexibilities, validation and testing confusion matrices, receiver operating characteristics (ROC) curves, and the parallel coordinate plot for identifying manufacturing flexibility trends for the predicted LM levels. Thus, machine learning models can be used to create effective soft sensors that can predict the level of lean manufacturing of an enterprise based on the levels of its manufacturing flexibilities.
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Goyal, Dinesh. "Foreword of Special Issue on : Current Research Trends in Secure Computer Vision, IoT and Machine Learning." Journal of Discrete Mathematical Sciences & Cryptography 26, no. 3 (2023): i—vii. http://dx.doi.org/10.47974/jdmsc-26-3-foreword.
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Security has been the most perpetual domain, with advancements in other domains and applications like Computer vision, IoT and Machine learning, is today’s most rapidly growing technical domains to facilitate better human life across the globe and is highly dominated with statistics and data analytics using data science, artificial intelligence, and Machine Learning. All these application areas have developed new security requirements. Recent progress in these fields has been driven both by the development of new learning algorithms and theory by the ongoing explosion in the availability of online data and low-cost computation. With more advancements in technology compelled with need of scaling has provided a challenge to security of system and its architecture. Thus there is a diehard need for secure models or architectures in domains of computer vision, IoT models and Data analytics using Machine Learning.
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Helal, Maha, Tariq Kashmeery, Mohammed Zakariah, and Wesam Shishah. "Internet of things and intrusion detection fog computing architectures using machine learning techniques." Decision Science Letters 13, no. 4 (2024): 767–82. http://dx.doi.org/10.5267/j.dsl.2024.9.003.
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The exponential expansion of the Internet of Things (IoT) has fundamentally transformed the way people, machines, and gadgets communicate, resulting in unparalleled levels of interconnectedness. Nevertheless, the growth of IoT has also brought up notable security obstacles, requiring the creation of strong intrusion detection systems to safeguard IoT networks against hostile actions. This study investigates the utilization of fog computing architectures in conjunction with machine learning approaches to improve the security of the IoT. The UNSW-NB15 dataset, containing an extensive range of network traffic characteristics, is used as the basis for training and assessing the machine learning models. The study specifically applies and evaluates the performance of various models, including linear regression, Ridge classifier, SGD classifier, and ensemble learning. Furthermore, the findings indicate that these models are capable of accurately identifying intrusions, with success rates of 94%, 97%, 96.60%, and 96.50%, respectively. The Ridge Classifier demonstrates exceptional accuracy, highlighting its potential for effective implementation in IoT security frameworks. The results emphasize the efficacy of combining machine learning with fog computing to tackle the distinct security obstacles faced by IoT networks. In the future, our work will prioritize optimizing these models for real-time applications, improving their scalability, and investigating more advanced ensemble strategies to enhance detection accuracy. The project intends to enhance these areas to create a comprehensive and scalable intrusion detection system that can offer strong security solutions for the growing IoT environment. This will guarantee the integrity and dependability of linked devices and systems.
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Helcl, Jindřich, and Jindřich Libovický. "Neural Monkey: An Open-source Tool for Sequence Learning." Prague Bulletin of Mathematical Linguistics 107, no. 1 (2017): 5–17. http://dx.doi.org/10.1515/pralin-2017-0001.
Full textAbstract:
Abstract In this paper, we announce the development of Neural Monkey – an open-source neural machine translation (NMT) and general sequence-to-sequence learning system built over the TensorFlow machine learning library. The system provides a high-level API tailored for fast prototyping of complex architectures with multiple sequence encoders and decoders. Models’ overall architecture is specified in easy-to-read configuration files. The long-term goal of the Neural Monkey project is to create and maintain a growing collection of implementations of recently proposed components or methods, and therefore it is designed to be easily extensible. Trained models can be deployed either for batch data processing or as a web service. In the presented paper, we describe the design of the system and introduce the reader to running experiments using Neural Monkey.
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Abdu, Aliyu Muhammad, Musa Mohd Muhammad Mokji, and Usman Ullah Ullah Sheikh. "Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (2020): 670. http://dx.doi.org/10.11591/ijai.v9.i4.pp670-683.
Full textAbstract:
Image-based plant disease detection is among the essential activities in precision agriculture for observing incidence and measuring the severity of variability in crops. 70% to 80% of the variabilities are attributed to diseases caused by pathogens, and 60% to 70% appear on the leaves in comparison to the stem and fruits. This work provides a comparative analysis through the model implementation of the two renowned machine learning models, the support vector machine (SVM) and deep learning (DL), for plant disease detection using leaf image data. Until recently, most of these image processing techniques had been, and some still are, exploiting what some considered as "shallow" machine learning architectures. The DL network is fast becoming the benchmark for research in the field of image recognition and pattern analysis. Regardless, there is a lack of studies concerning its application in plant leaves disease detection. Thus, both models have been implemented in this research on a large plant leaf disease image dataset using standard settings and in consideration of the three crucial factors of architecture, computational power, and amount of training data to compare the duos. Results obtained indicated scenarios by which each model best performs in this context, and within a particular domain of factors suggests improvements and which model would be more preferred. It is also envisaged that this research would provide meaningful insight into the critical current and future role of machine learning in food security