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Journal articles on the topic 'Machine learning interactif'

Author: Grafiati

Published: 14 December 2024

Last updated: 31 July 2025

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1

Chavel, Thierry. "La rencontre humaine est-elle soluble dans l’intelligence artificielle ?" Management international 28, no. 2 (2024): 142–44. http://dx.doi.org/10.59876/a-ma53-q5cw.

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Avec la numérisation du monde, la réalité n’est plus ce qu’elle était. Je peux avoir l’illusion d’être ici et ailleurs. Un cloud remplace ma mémoire personnelle. L’autre du débat s’efface au profit du même des communautés virtuelles. La 4e révolution industrielle n’est pas qu’un saut technologique, c’est surtout un choix de société qui renouvelle en profondeur l’exercice du leadership et ses trois fondements humanistes : la fragilité, l’altérité et la responsabilité. L’irruption d’outils de machine learning tels que Chat-GPT transforme violemment les métiers de la prestation intellectuelle. Un

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Li, Jiahang. "Research on Interactive System of Movie Subtitle Speech Based on Machine Learning Technology." Frontiers in Computing and Intelligent Systems 2, no. 2 (2022): 22–24. http://dx.doi.org/10.54097/fcis.v2i2.3744.

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The composition elements of subtitles, from the early single text, have developed into the present text, graphics, colors, animation, special effects and other combinations. With the development of speech technology and natural language understanding, speech interaction system has become a hot research field. Different from the traditional data interaction between keyboard, mouse and display, using hearing to transmit data makes the interactive system of movie subtitles more anthropomorphic and intelligent. It is the most natural and convenient means for human beings to exchange information wi

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Animesh, Kumar, and Dr Srikanth V. "Enhancing Healthcare through Human-Robot Interaction using AI and Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 184–90. http://dx.doi.org/10.55248/gengpi.5.0324.0831.

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An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.

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Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimen

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Amershi, Saleema, James Fogarty, Ashish Kapoor, and Desney Tan. "Effective End-User Interaction with Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1529–32. http://dx.doi.org/10.1609/aaai.v25i1.7964.

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End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate th

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Guo, Chao-Yu, and Ke-Hao Chang. "A Novel Algorithm to Estimate the Significance Level of a Feature Interaction Using the Extreme Gradient Boosting Machine." International Journal of Environmental Research and Public Health 19, no. 4 (2022): 2338. http://dx.doi.org/10.3390/ijerph19042338.

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Recent studies have revealed the importance of the interaction effect in cardiac research. An analysis would lead to an erroneous conclusion when the approach failed to tackle a significant interaction. Regression models deal with interaction by adding the product of the two interactive variables. Thus, statistical methods could evaluate the significance and contribution of the interaction term. However, machine learning strategies could not provide the p-value of specific feature interaction. Therefore, we propose a novel machine learning algorithm to assess the p-value of a feature interacti

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Zholshiyeva, Lazzat, Zhanat Manbetova, Dinara Kaibassova, et al. "Human-machine interactions based on hand gesture recognition using deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 741–48. https://doi.org/10.11591/ijece.v14i1.pp741-748.

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Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and

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Spillard, Samuel, Christopher J. Turner, and Konstantinos Meichanetzidis. "Machine learning entanglement freedom." International Journal of Quantum Information 16, no. 08 (2018): 1840002. http://dx.doi.org/10.1142/s0219749918400026.

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Quantum many-body systems realize many different phases of matter characterized by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [C. J. Turner, K. Meichanetzidis, Z. Papic and J. K. Pachos, Nat. Commun. 8 (2017) 14926, Phys. Rev. B 97 (2018) 125104]. The computation of the inte

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Kumar, Dr Tribhuwan, Klinge Orlando Villalba-Condori, Dennis Arias-Chavez, Rajesh K., Kalyan Chakravarthi M, and Dr Suman Rajest S. "An Evaluation on Speech Recognition Technology based on Machine Learning." Webology 19, no. 1 (2022): 646–63. http://dx.doi.org/10.14704/web/v19i1/web19046.

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Speech is the basic way of interaction between the listener to the speaker by voice or expression. Humans can easily understand the speakers' message, but machines can't understand the speaker's word. Nowadays, most of our lives are occupied by machines; but we can't interact with machines. The human brain, like machine learning technology, is essential for speech recognition to interact with machines to humans. The language used for speech recognition must be a global language, so English has been used in this paper. The machine learning methodology is used in a lot of assignments through the

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Coe, J. P. "Machine Learning Configuration Interaction." Journal of Chemical Theory and Computation 14, no. 11 (2018): 5739–49. http://dx.doi.org/10.1021/acs.jctc.8b00849.

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11

Holzinger, Andreas. "Interactive Machine Learning (iML)." Informatik-Spektrum 39, no. 1 (2015): 64–68. http://dx.doi.org/10.1007/s00287-015-0941-6.

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Harvey, Neal, and Reid Porter. "User-driven sampling strategies in image exploitation." Information Visualization 15, no. 1 (2014): 64–74. http://dx.doi.org/10.1177/1473871614557659.

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Both visual analytics and interactive machine learning try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human–computer interaction. This article focuses on one aspect of the human–computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteratio

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Dawood, Dr Amina Atiya, and Balasem Alawi Hussain. "Machine Learning for Single and Complex 3D Head Gestures: Classification in Human-Computer Interaction." Webology 19, no. 1 (2022): 1431–45. http://dx.doi.org/10.14704/web/v19i1/web19095.

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This paper presents a new Hidden Markov Model based approach for fast and automatic detection and classification of head movements in real time dynamic videos. The model has been developed to utilize human-computer interaction applications by using only the laptop webcam. The proposed model has the ability to predict single head and combined simultaneously in fast responses. Other models paid more attention to classify head nod and shake only, but our model contribute the role of other head movements. The model proposed here doesn’t need any user intervention or previous knowledge of its envir

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Zholshiyeva, Lazzat, Zhanat Manbetova, Dinara Kaibassova, et al. "Human-machine interactions based on hand gesture recognition using deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 741. http://dx.doi.org/10.11591/ijece.v14i1.pp741-748.

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Human interaction with computers and other machines is becoming an increasingly important and relevant topic in the modern world. Hand gesture recognition technology is an innovative approach to managing computers and electronic devices that allows users to interact with technology through gestures and hand movements. This article presents deep learning methods that allow you to efficiently process and classify hand gestures and hand gesture recognition technologies for interacting with computers. This paper discusses modern deep learning methods such as convolutional neural networks (CNN) and

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Lindvall, Martin, Jesper Molin, and Jonas Löwgren. "From machine learning to machine teaching." Interactions 25, no. 6 (2018): 52–57. http://dx.doi.org/10.1145/3282860.

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Thieme, Anja, Danielle Belgrave, Akane Sano, and Gavin Doherty. "Machine learning applications." Interactions 27, no. 2 (2020): 6–7. http://dx.doi.org/10.1145/3381342.

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Baker del Aguila, Ryan, Carlos Daniel Contreras Pérez, Alejandra Guadalupe Silva-Trujillo, Juan C. Cuevas-Tello, and Jose Nunez-Varela. "Static Malware Analysis Using Low-Parameter Machine Learning Models." Computers 13, no. 3 (2024): 59. http://dx.doi.org/10.3390/computers13030059.

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Recent advancements in cybersecurity threats and malware have brought into question the safety of modern software and computer systems. As a direct result of this, artificial intelligence-based solutions have been on the rise. The goal of this paper is to demonstrate the efficacy of memory-optimized machine learning solutions for the task of static analysis of software metadata. The study comprises an evaluation and comparison of the performance metrics of three popular machine learning solutions: artificial neural networks (ANN), support vector machines (SVMs), and gradient boosting machines

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Gillies, Marco. "Understanding the Role of Interactive Machine Learning in Movement Interaction Design." ACM Transactions on Computer-Human Interaction 26, no. 1 (2019): 1–34. http://dx.doi.org/10.1145/3287307.

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Sadhasivam, Jayakumar, Senthil J, Ganesh R.M, and Chellapan N. "Liver Disease Prediction Using Machine Learning Classification." Webology 18, no. 02 (2021): 441–52. http://dx.doi.org/10.14704/web/v18si02/web18293.

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People have disorder of liver that require medical care at correct time. It is utmost important to find the disease before it elapse the curable stage. Significantly, much of understanding of organ development has arisen from analyses of patients with liver deficiencies. Data mining is beneficial to find the disease at early stage based on the factors that can be gathered by performing test on the patient. Nowadays, around 65 % of the population in India are eating junk foods which minimize the metabolism rate and effect liver in many ways. In recent years, liver disorders have excessively inc

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J, Cynthia, G. Sakthi Priya, C. Kevin Samuel, Suguna M, Senthil J, and S. Abraham Jebaraj. "Traffic Flow Forecasting Using Machine Learning Techniques." Webology 18, no. 04 (2021): 1512–26. http://dx.doi.org/10.14704/web/v18si04/web18295.

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Congestion due to traffic, results in wasted fuel, increase in pollution level, increase in travel time and vehicular queuing. Smart city initiatives are aimed to improve the quality of urban life. Intelligent Transportation System (ITS) provides solution for many smart city projects, as they capture real time data without any fixed infrastructure. The real-time prediction of traffic flow aids in alleviating congestion. Accurate and timely prediction on the future traffic flow helps individual travellers, public transport, and transport planning. Existing systems are designed to predict specif

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Abdullah, Syahid, Wisnu Ananta Kusuma, and Sony Hartono Wijaya. "Sequence-based prediction of protein-protein interaction using autocorrelation features and machine learning." Jurnal Teknologi dan Sistem Komputer 10, no. 1 (2022): 1–11. http://dx.doi.org/10.14710/jtsiskom.2021.13984.

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Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Fo

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Bai, Xiuyan, and Jiawen Shi. "A Machine Learning Based Method to Evaluate Learning in Gamification Practices." International Journal of Emerging Technologies in Learning (iJET) 18, no. 21 (2023): 171–85. http://dx.doi.org/10.3991/ijet.v18i21.44689.

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With the integration of advanced methods and technologies in higher vocational education, educational gamification has emerged as a new approach to encourage students’ active participation in learning. However, it is difficult to accurately evaluate student participation in this environment and delve into the process of interactive evolution. Most existing research methods primarily focus on qualitative analysis, while attempts to conduct quantitative analysis are often constrained by traditional statistical methods. Moreover, these methods frequently fail to consider the interactive dynamics

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Phongying, Methaporn, and Sasiprapa Hiriote. "Diabetes Classification Using Machine Learning Techniques." Computation 11, no. 5 (2023): 96. http://dx.doi.org/10.3390/computation11050096.

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Machine learning techniques play an increasingly prominent role in medical diagnosis. With the use of these techniques, patients’ data can be analyzed to find patterns or facts that are difficult to explain, making diagnoses more reliable and convenient. The purpose of this research was to compare the efficiency of diabetic classification models using four machine learning techniques: decision trees, random forests, support vector machines, and K-nearest neighbors. In addition, new diabetic classification models are proposed that incorporate hyperparameter tuning and the addition of some inter

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Mitra, Manu. "Editorial on Advances in Machine Learning and Robotics." American Research Journal of Electronics and Communication Engineering 3, no. 1 (2019): 1–5. https://doi.org/10.5281/zenodo.2601637.

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Machine Learning is opening door to entirely new automation possibilities. It is set to disrupt practically every industry imaginable. Presently, machine learning are being applied in limited methods and are enhancing the abilities of industrial robotic systems. There is always room for improvement for the full potential of robotics and machine learning but applications are advantageous. There are four major areas for robotic process and machine learning are impacting to make current applications more efficient and beneficial. It includes Vision – machine learning is aiding robots to det

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Teso, Stefano, and Oliver Hinz. "Challenges in Interactive Machine Learning." KI - Künstliche Intelligenz 34, no. 2 (2020): 127–30. http://dx.doi.org/10.1007/s13218-020-00662-x.

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Wicaksono, Mochamad Fajar, Myrna Dwi Rahmatya, and Angga Rinaldi. "Interactive Letter and Number Learning Machines For Early Childhood." CCIT Journal 13, no. 2 (2020): 220–32. http://dx.doi.org/10.33050/ccit.v13i2.1057.

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The purpose of this study is to design and create interactive learning machines for letters and numbers for early childhood. The machine designed is an interactive learning machine so that early childhood is interested in learning and can learn independently. Arduino Mega2560 is used as the main processor on this machine. On this machine, there are three modes, namely learning mode, question mode, and counting mode. In learning mode, Arduino will read every button input pressed, display on the LCD and make a sound through DF Player in accordance with the input received. In the question mode, A

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Srushti, Surendra Naik. "Understanding Artificial Intelligence and Machine Learning." International Journal of Advance and Applied Research S6, no. 22 (2025): 816–20. https://doi.org/10.5281/zenodo.15533444.

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<em>Artificial Intelligence (AI) and Machine Learning (ML) are the driving forces behind many technological advancements that are transforming our world. These fields are transforming how we approach problem-solving, decision-making, and interaction with technology. While AI generally refers to the simulation of human intelligence in machines, ML is a subset that focuses on enabling machines to learn and improve through experience.</em> <em>In this research paper, we will dive into the fundamentals of AI and ML, the various types of machine learning, the applications of AI and ML in various in

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Ibrahim, Dr Abdul-Wahab Sami, and Dr Baidaa Abdul khaliq Atya. "Detection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques." Webology 19, no. 1 (2022): 1493–503. http://dx.doi.org/10.14704/web/v19i1/web19100.

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Plant diseases have a negative impact on the agricultural sector. The diseases lower the productivity of the production yield and give huge losses to the farmers. For the betterment of agriculture, it is very essential to detect the diseases in the plants to protect the agricultural crop yield while it is also important to reduce the use of pesticides to improve the quality of the agricultural yield. Image processing and data mining algorithms together help analyze and detection of diseases. Using these techniques diseases detection can be done in rice leaves. In this research, the image proce

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Duan, Jiashun, and Xin Zhang. "A Class-Incremental Learning Method for Interactive Event Detection via Interaction, Contrast and Distillation." Applied Sciences 14, no. 19 (2024): 8788. http://dx.doi.org/10.3390/app14198788.

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Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of “machine recommendation-human review–machine incremental learning” was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning me

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V., Dr Suma. "COMPUTER VISION FOR HUMAN-MACHINE INTERACTION-REVIEW." Journal of Trends in Computer Science and Smart Technology 2019, no. 02 (2019): 131–39. http://dx.doi.org/10.36548/jtcsst.2019.2.006.

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The paper is a review on the computer vision that is helpful in the interaction between the human and the machines. The computer vision that is termed as the subfield of the artificial intelligence and the machine learning is capable of training the computer to visualize, interpret and respond back to the visual world in a similar way as the human vision does. Nowadays the computer vision has found its application in broader areas such as the heath care, safety security, surveillance etc. due to the progress, developments and latest innovations in the artificial intelligence, deep learning and

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Stephens, Keri, Anastazja Harris, Amanda Hughes, et al. "Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication." Human-Machine Communication 6 (July 1, 2023): 65–85. http://dx.doi.org/10.30658/hmc.6.5.

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Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perce

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Pooja, Tyagi, and Sharma Anurag. "Implementation of Fraudulent Sellers Detection System of Online Marketplaces using Machine Learning Techniques." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 2 (2021): 194–98. https://doi.org/10.35940/ijrte.B6298.0710221.

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The E-commerce proportion in global retail expenditure has been steadily increasing over the years showing an obvious shift from brick and mortar to retail clicks. To analyze the exact problem of building an interactive models for the identification of auction fraud in the entry of data into ecommerce. This is why the most popular site's business develops with retailers and other auction customers. Where viral customers purchase products from online trading, customers may worry about fraudulent actions to get unlawful benefits from honest parties. Proactive modesty systems for detecting fr

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Shanmugapriya, Dr V., and Nivetha R S. "AUTISM PREDICTION USING MACHINE LEARNING." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02471.

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Autism Spectrum Disorder (ASD) is a complex neurological condition that affects social interaction, communication, and behavioral patterns. Early diagnosis and intervention are critical to improving the quality of life for individuals with autism. Traditional diagnostic methods often rely on time-consuming behavioral assessments conducted by specialists, which can delay detection and intervention. This study proposes a machine learning- based approach to predict autism by analyzing behavioral, demographic, and clinical data. By leveraging algorithms such as Random Forest, Support Vector Machin

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Berg, Stuart, Dominik Kutra, Thorben Kroeger, et al. "ilastik: interactive machine learning for (bio)image analysis." Nature Methods 16 (September 30, 2019): 1226–32. https://doi.org/10.1038/s41592-019-0582-9.

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We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on dat

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Antonio Lorenza, Gabriella Caterina, and Bianca Isabella. "Machine Learning Techniques for Accurate Prediction of Proteins Function." Fusion of Multidisciplinary Research, An International Journal 1, no. 2 (2020): 85–96. https://doi.org/10.63995/hixn4599.

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Machine learning techniques are revolutionizing the prediction of protein functions, offering unprecedented accuracy and efficiency in understanding biological processes. By leveraging large datasets of protein sequences and structures, machine learning models can identify patterns and relationships that are often elusive to traditional methods. Techniques such as deep learning, support vector machines, and random forests have shown remarkable success in predicting protein functions, including enzymatic activities, binding sites, and interaction networks. These approaches utilize diverse data

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Yarimizu, Masayuki, Cao Wei, Yusuke Komiyama, et al. "Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine." Advances in Bioinformatics 2015 (August 11, 2015): 1–5. http://dx.doi.org/10.1155/2015/528097.

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Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting

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Chetan, Bulla, Parushetti Chinmay, Teli Akshata, Aski Samiksha, and Koppad Sachin. "A Review of AI Based Medical Assistant Chatbot." Research and Applications of Web Development and Design 3, no. 2 (2020): 1–14. https://doi.org/10.5281/zenodo.3902215.

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<em>This is now the age of smart computer. Machines have started to impersonate as human, with the advent of artificial intelligence, machine learning, and deep learning. Chatbot is classified as conversational software agents enabled by natural language processing, and is an excellent example of such system. A Chatbot is a program which allows the user to start a conversation with the machine. This is a platform focused on Artificial Intelligence (AI), which can be developed as messaging applications, web applications, or smartphone applications. A chatbot represents machine that answers ques

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Fung, Lee Hua, and Seetha Letchumy M. Belaidan. "Sentiment Analysis in Online Products Reviews Using Machine Learning." Webology 18, SI05 (2021): 914–28. http://dx.doi.org/10.14704/web/v18si05/web18271.

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Online Shopping is a phenomenon that is growing rapidly. It refers to the act of buying and selling products or services over the internet. Since customers are shopping online, there are some problems with this process. Firstly, is that customers can fall into fraud and security concerns as there is an inability to inspect the goods that you are purchasing beforehand. There is also the other issue on the quality of the product, this is because when selling online, only simple pictures and or descriptions of the product are all a customer can rely on when purchasing. There is also another facto

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Yang, Humin, Achyut Shankar, and Velliangiri S. "Artificial Intelligence-Enabled Interactive System Modeling for Teaching and Learning Based on Cognitive Web Services." International Journal of e-Collaboration 19, no. 2 (2023): 1–18. http://dx.doi.org/10.4018/ijec.316655.

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The future of modern education and web-based learning is inherently associated with the advancement in modern technologies and computing capacities of new smart machines, such as artificial intelligence (AI). AI is a high-performance computing environment powered by special processors that use cognitive computing for machine learning and data analytics. There are major challenges in online or web-based learning, such as flexibility, student support, classification of teaching, and learning activities. Hence, this paper proposes smart web-based interactive system modeling (SWISM)based on artifi

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Porter, Reid, James Theiler, and Don Hush. "Interactive Machine Learning in Data Exploitation." Computing in Science & Engineering 15, no. 5 (2013): 12–20. http://dx.doi.org/10.1109/mcse.2013.74.

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de Wit, Paulus A. J. M., and Roberto Moraes Cruz. "Learning from AF447: Human-machine interaction." Safety Science 112 (February 2019): 48–56. http://dx.doi.org/10.1016/j.ssci.2018.10.009.

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Glikis, Rafael, Christos Makris, and Nikos Tsirakis. "DrCaptcha: An interactive machine learning application." Computer Science and Information Systems, no. 00 (2020): 48. http://dx.doi.org/10.2298/csis200130048g.

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The creation of a Machine Learning system is a typical process that is mostly automated. However, we may address some problems in the during development, such as the over-training on the training set. A technique for eliminating this phenomenon is the assembling of ensembles of models that cooperate to make predictions. Another problem that almost always occurs is the necessity of the human factor in the data preparation process. In this paper, we present DrCaptcha [15], an interactive machine learning system that provides third-party applications with a CAPTCHA service and, at the same time,

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Sundar, Balapuri Shiva. "Emotion Detection on text using Machine Learning and Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2277–86. http://dx.doi.org/10.22214/ijraset.2022.44293.

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Abstract: Emotion detection on text is an important field of research in Artificial Intelligence and human-computer interaction. Emotions play key role in human interaction. Emotion detection is closely associated with sentiment detection, in which we detect the polarity of the text. But in emotion detection, we detect emotions such as joy, love, surprise, sadness, fear, and anger. Emotion detection helps the machines to understand human behavior and ultimately it provides users with emotional awareness feedback. In this paper, we are going to compare Machine Learning and Deep Learning techniq

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Nivas, K., M. Rajesh Kumar, G. Suresh, T. Ramaswamy, and Yerraboina Sreenivasulu. "Facial Emotion Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 427–33. http://dx.doi.org/10.22214/ijraset.2023.48585.

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Abstract: The use of machines to perform various tasks is ever increasing in society. By imbuing machines with perception, they will be able to perform a wide variety of tasks. There are also very complex ones, such as aged care. Machine perception requires the machine to understand the surrounding environment and the intentions of the interlocutor. Recognizing facial emotions can help in this regard. During the development of this work, deep learning techniques were used on images showing facial emotions such as happiness, sadness, anger, surprise, disgust, and fear. In this study, a pure con

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Adawy, Mohammad, Hasan Abualese, Nidhal Kamel Taha El-Omari, and Abdulwadood Alawadhi. "Human-Robot Interaction (HRI) using Machine Learning (ML): a Survey and Taxonomy." International Journal of Advances in Soft Computing and its Applications 16, no. 3 (2024): 166–82. http://dx.doi.org/10.15849/ijasca.241130.11.

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Human-robot interaction (HRI) which has become the fundamental need of the hour is born out of the necessity for studying the relation between humans and robots. This cutting-edge discipline is a multidisciplinary field that draws from computer science, robotics along with human-computer interaction and psychology. It focuses mainly on designing and programming machines, best known as automated machines or robots, which are used by humans to perform specific tasks in a timely manner and with higher quality. The key problem in HRI is to realize, shape, tune, and modelling the humanrobot interac

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Lürig, Christoph. "Learning Machine Learning with a Game." European Conference on Games Based Learning 16, no. 1 (2022): 316–23. http://dx.doi.org/10.34190/ecgbl.16.1.481.

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AIs playing strategic games have always fascinated humans. Specifically, the reinforcement learning technique Alpha Zero (D.Silver, 2016) has gained much attention for its capability to play Go, which was hard to crack problem for AI for a long time. Additionally, we see the rise of explainable AI (xAI), which tries to address the problem that many modern AI decision techniques are black-box approaches and incomprehensible to humans. Combining a board game AI for the relatively simple game Connect-Four with explanation techniques offers the possibility of learning something about an AI's inner

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Surse, Shriram, Pankaj Koli, Tahoor Khan, and Prof Aditi Wangikar. "GESTURE CONTROL MOUSE USING MACHINE LEARNING." International Journal of Engineering Applied Sciences and Technology 10, no. 01 (2025): 76–81. https://doi.org/10.33564/ijeast.2025.v10i01.011.

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In this research paper, we present a pioneering system for gesture-based control of a virtual mouse using computer vision and machine learning techniques. The system leverages the capabilities of the MediaPipe library and OpenCV to accurately detect and track hand gestures in real time through a standard webcam. The virtual mouse controller, implemented in Python, interprets the hand gestures to enable intuitive and natural interaction with the computer. Our approach includes the detection and tracking of multiple hand landmarks, allowing for precise mapping of hand movements and finger positi

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Raj, Rishav, Vivek Herenj, Vikash Roy, and ManojKumar Mishra. "Personality Prediction System Using Machine Learning." International Research Journal of Computer Science 11, no. 10 (2024): 627–32. https://doi.org/10.26562/irjcs.2024.v1110.05.

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A personality prediction system leverages advanced data analysis techniques to assess and predict an individual's personality traits based on diverse inputs such as text, voice, behaviour, and biometric data. These systems typically utilize psychological models, like the Big Five Personality Traits or Myers-Briggs Type Indicator (MBTI), to derive insights from patterns in the data. Text-based prediction systems use natural language processing (NLP) to analyze written or spoken communication, while voice- based systems analyze vocal attributes such as tone and pitch. Behavioral and biometric da

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Raj, Rishav, Vivek Herenj, Vikash Roy, and Manoj Mishra. "Personality Prediction System Using Machine Learning." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 819–24. https://doi.org/10.26562/ijirae.2024.v1111.05.

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A personality prediction system leverages advanced data analysis techniques to assess and predict an individual's personality traits based on diverse inputs such as text, voice, behaviour, and biometric data. These systems typically utilize psychological models, like the Big Five Personality Traits or Myers-Briggs Type Indicator (MBTI), to derive insights from patterns in the data. Text-based prediction systems use natural language processing (NLP) to analyze written or spoken communication, while voice-based systems analyze vocal attributes such as tone and pitch. Behavioral and biometric dat

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Zhu, Chaoyang. "Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 01–13. http://dx.doi.org/10.17762/ijritcc.v11i7.7736.

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Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algori

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