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Journal articles on the topic 'Architectures and machine learning models'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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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

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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

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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

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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

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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)

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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

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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 shi

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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

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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-

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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 in

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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 layer

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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 lo

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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-ba

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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.

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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 neura

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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 aut

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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 di

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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

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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 cleani

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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 th

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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 sol

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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 res

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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 ben

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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, cla

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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 Köpp

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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 a

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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 securi

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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 th

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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 cla

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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

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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 compo

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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, scien

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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

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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 a

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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 a

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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 aut

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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 explore

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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% accura

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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 machi

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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

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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 arc

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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

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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 da

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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 architect

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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

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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.

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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 on

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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 ne

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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.

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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 t

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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.

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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

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