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A Resilient Overlay for Human Emotion Recognition Using Mixed Frameworks in Machine-Human Interactions

SN computer science 2024年第4期5卷 414页

作者： Fayaz, Fayaz Ahmad Malik, Arun Batra, Isha Ansarullah, Syed Immamul National Institute of Electronics and Information Technology (NIELIT) Jammu and Kashmir Srinagar 191132 India Department of Computer Applications Lovely Professional University (LPU) Punjab Phagwara 144402 India Department of Computer Science and Engineering LPU Punjab Phagwara 144402 India Department of IMBA North Campus University of Kashmir Kashmir Baramullah 193101 India

Human sentiments are irrational responses to things or situations associated with various physiological, behavioural, and cognitive processes. Understanding human sentiments is pivotal in diverse fields such as human–computer interaction, virtual reality, self-driving technology, and health monitoring. The scientific community is becoming more and more interested in emotion recognition. Emotion recognition, mainly through brain-generated electroencephalogram (EEG) waves, has gained significant attention in developing brain-computer interfaces. This article addresses critical research gaps in existing cross-subject emotion detection methods based on EEG signals and multi-modal techniques that combine EEG data with other modalities. We proposed an effective hybrid technique for cross-subject sentiment identification using EEG and facial movements. The suggested method combines a spectrum of directed variations and local binary pattern characteristics with spectral and statistical features from the facial dataset. The next step is classifying emotions using support vector machines, k-nearest neighbour, and ensembles. Notably, our method tackles the challenge of category imbalance using an up-sampling approach. Our results are promising after evaluating the efficiency of our proposed technique on a sentiment analysis dataset with physiological data and employing tenfold cross-validation. Notably, the accuracy levels for valence and arousal reach impressive highs at 96.25% and 97.10%, respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence EEG Human–computer interaction Machine learning Multimodal

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Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

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computer Systems science & engineering 2023年第12期47卷 3145-3163页

作者： Shahina Anwarul Sunil Kumar Ashok Bhansali Hammam Alshazly Hany S.Hussein School of Computer Science University of Petroleum and Energy StudiesDehradun248007India Department of Computer Science Graphic Era Hill UniversityDehradunUttarakhand248001India Department of Computer Engineering and Applications GLA UniversityMathuraIndia Faculty of Computers and Information South Valley UniversityQena83523Egypt Electrical Engineering Department College of EngineeringKing Khalid UniversityAbha62529Saudi Arabia Electrical Engineering Department Faculty of EngineeringAswan UniversityAswan81528Egypt

Security systems are the need of the hour to protect data from unauthorized *** dissemination of confidential information over the public network requires a high level of *** security approach such as steganography ensures confidentiality,authentication,integrity,and *** helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and ***,we present an efficient hybrid security model that provides multifold security *** this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high *** modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret *** enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)*** experiments are conducted on the images of the USC-SIPI standard image *** experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.

关键词： Cryptography steganography digital signature rectified AES encryption

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An Optimized Combinatorial Approach For Stock Market Prediction Considering User Preferences 1

An Optimized Combinatorial Approach For Stock Market Predict...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Iyappan, P. Anuradha, K. Revathi, R. Sathish Kumar, L. Saravanan, D. Maragatharajan, M. Vellore Institute of Technology Tamil Nadu Vellore India Karpagam College of Engineering School of Computer Applications Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Kotrikalan India

ISBN: (纸本)9798350372748

The economy is often seen as the engine that propels a nation forward. It regulates the distribution of resources by keeping an eye on the complex relationship between production and consumption. To put it simply, the economic system is there to meet the demands of all the people and businesses that make up the economy. The stock market is an important part of any economic understanding since it is the setting in which activities such as the purchase and sale of shares and the issue of new shares by different types of limited liability companies (both public and private) take place in relation to market research and exchanges. Because it is so difficult for the average person to predict when stock prices will go up or down, it is critical to find the best stocks on the market and put their money into them. When it comes to intelligently and reliably meeting user demands, however, conventional processes fall short. An optimized stock prediction system that takes user preferences and demands into account is presented in this study as a new way to manage retrieval. This technique is crucial for properly satisfying user requirements. Essential aspects such as supply-demand dynamics, inflation, geopolitical events, technological advancements, natural catastrophes, company performance, and the efficiency of government data may be dynamically gathered using this technique. All of these things add together to make stock price swing predictions. Machine learning algorithms are crucial for stock prediction because of the massive amounts of data that must be collected, processed, cleaned, and analyzed. The proposed method, ML + LSTM, yields 96% accuracy. Comparing the existing methods, which are KNN, decision tree, and random forest, gives accuracy like 80, 92, and 94 percentages. In addition, the article highlights the use of ML techniques including k-nearest neighbour, decision tree, and random forest. Important financial indicators, including common factors, sector factors,

关键词： Decision trees

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Image Processing Based Early Breast Cancer Detection in Mammography Images Using GRU and XGBoost Approach 1

Image Processing Based Early Breast Cancer Detection in Mamm...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Pandey, Abhishek Ramesh, V. Mohan, Ravi Kaliappan, S. Swetha Reddy, A. Gurunathan, K. SCSVMV University Department of Computer Applications Kanchipuram India Shri Ram Institute of Technology Department of ECE Madhya Pradesh Jabalpur India Lovely Professional University Division of Research and Development Punjab India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Mount Zion College of Engineering and Technology Department of Computer Science and Engineering Pudukkottai India

ISBN: (纸本)9798350371802

One out of every eight women will be affected by breast cancer at some point in their lives, making it one of the most common malignancies in females. The most reliable method for finding breast cancer in its early stages is mammography. Due to the low visibility and weak contrast of mammographic photos, early identification of breast cancer is crucial for effective therapy. As a result of several computer-aided detection approaches, radiologists are now able to make more precise diagnoses. Operating Sequencing is crucial for several tasks, including morphology, preprocessing, segmentation, feature selection, and training models. The suggested method makes use of preprocessing technologies that can enhance mammography, remove noise, and identify pectoral muscles. Using image processing techniques, a digital image can be 'segmented' into many layers. Acquiring a precise image of the mass is the goal of the morphological process. Geometric algorithms are employed by the feature selection technique. Training a GRU-XGBoost model requires careful consideration of which features to use. The proposed method outperforms the current gold standard algorithms, GRU and XGBoost, in every respect. The results showed a remarkable improvement, with an accuracy of 95.63%. © 2024 IEEE.

关键词： Diseases

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Optimizing Supply Chain Operations through Machine Learning: Opportunities and Challenges

Optimizing Supply Chain Operations through Machine Learning:...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Santhakumar, D. Soundararajan, Gopalakrishnan Chintala, Suman Bharath, E. Murthy Inumula, Krishna Chouhan, Kuldeep Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai602105 India Muscat College Ruwi Oman Business Intelligence Architect United States C K College of Engineering & Technology Anna University Department of Artificial Intelligence & Data Science Tamil Nadu Cuddalore India Maharashtra Pune411057 India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

In order to create enduring partnerships that are essential to attaining long-term economic success, supply chain management, or SCM, is essential. Strict standards and decision-making procedures are necessary for efficient SCM, and they have a big influence on the final results. In order to efficiently manage inventory levels and logistical procedures, this study offers a complex ML system that combines demand forecasts and optimisation methods. For predicting demand, the approach uses a Generative Adversarial Networks (GAN) model, taking advantage of its capacity to process time-series data and identify intricate trends. The algorithm achieves an impressive 96.4% forecasting of demand accuracy by examining extensive historical sales data and a variety of supply chain variables. In order to optimise inventory rules, the investigation also uses optimisation techniques, particularly Genetic Algorithms (GA) in conjunction with Simulated Annealing (SA). The model demonstrated significant improvements when implemented to an actual commercial logistics system. Notably, it reduced storage expenses by 19% and increased order fulfillment rates by 24%, proving its capacity for combining service standards with financial efficiency. The findings highlight the significant impact of ML on improving supply chain efficiency and show how it can result in significant cost savings and improve service quality. © 2025 IEEE.

关键词： Simulated annealing

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Predicting the Emergence of Hyperglycaemia through the Application of Machine Learning Methodologies 5

Predicting the Emergence of Hyperglycaemia through the Appli...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Pattnayak, Parthasarathi Patnaik, Sanghamitra Gourisaria, Mahendra Kumar Singh, Satyendr Goje, Nitin S. Patra, Sudhansu Shekhar KIIT Deemed to be University School of Computer Applications Bhubaneswar India KIIT Deemed to be University School of Law Bhubaneswar India KIIT Deemed to be University School of Computer Engineering Bhubaneswar India BML Munjal University Computer Science & Engg Department Gurugram India Webster University Computer and Information Sciences Department Tashkent Uzbekistan

ISBN: (纸本)9798350367171

Hyperglycaemia is a lifelong digestive condition that causes excessive blood sugar levels. Early detection along with treatment can avoid or prolong the problems in their beginning. Earlier studies have used machine learning to predict pathology. This study suggests that an artificially intelligent neural network could be a beneficial tool for managing and preventing diabetes. Hyperglycaemia can lead to both acute and chronic consequences, including fatalities. Chronic problems can lead to various impairments and organ deterioration. Failure to provide comprehensive remedies and preventions for diabetes patients can result in increased medical costs and a reduction in society's overall quality of life. Eight subject parameters were examined in this study: age, body mass index, plasma glucose level, insulin level, diastolic blood pressure, sebum thickness, pregnancies' number, and diabetes pedigree function. This study used to build neural network models and assess their accuracy in predicting diabetes. Results showed that some models performed better than others. © 2024 IEEE.

关键词： Self-supervised learning

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FedMUP: Federated learning driven malicious user prediction model for secure data distribution in cloud environments

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Applied Soft Computing 2024年 157卷

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Kumar, Jatinder Singh, Ashutosh Kumar Department of Computer Science & Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Department of Computer Science & Engineering University of Aizu Aizuwakamatsu Fukushima Japan Department of Computer Applications SRM Institute of Science and Technology NCR Campus-Ghaziabad201204 India Department of Computer Applications National Institute of Technology Kurukshetra136119 India Department of Computer Science & Engineering Indian Institute of Information Technology Bhopal462003 India Department of Computer Science The University of Economics and Human Sciences Warsaw01043 Poland

Cloud computing is flourishing at a rapid pace. Significant consequences related to data security appear as a malicious user may get unauthorized access to sensitive data which may be misused, further. This raises an alarm-ringing situation to tackle the crucial issue related to data security and proactive malicious user prediction. This article proposes a Federated learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments (FedMUP). This approach firstly analyzes user behavior to acquire multiple security risk parameters. Afterward, it employs the federated learning-driven malicious user prediction approach to reveal doubtful users, proactively. FedMUP trains the local model on their local dataset and transfers computed values rather than actual raw data to obtain an updated global model based on averaging various local versions. This updated model is shared repeatedly at regular intervals with the user for retraining to acquire a better, and more efficient model capable of predicting malicious users more precisely. Extensive experimental work and comparison of the proposed model with state-of-the-art approaches demonstrate the efficiency of the proposed work. Significant improvement is observed in the key performance indicators such as malicious user prediction accuracy, precision, recall, and f1-score up to 14.32%, 17.88%, 14.32%, and 18.35%, respectively. © 2024 Elsevier B.V.

关键词： Forecasting

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Combining Federated Learning and Blockchain for Enhanced Traceability in Agricultural Supply Chains 2

Combining Federated Learning and Blockchain for Enhanced Tra...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Bhaskar, Nuthanakanti Margarat, G.Simi Al-Farouni, Mohammed Nagaraju, Krishna Chythanya Kumar, Rakesh Krishnakumar, K. Department of CSE CMR Technical Campus Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of CSE Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

To address these challenges, this research will adopt a federated learning method and implement the use of a blockchain system to implement a traceability system on the agricultural chain. Based on the voluntary collaboration of decentralized datasets and blockchain-based ledgers, the technology is poised to enhance the transparency, effectiveness, and reliability of the agricultural chain. TensorFlow Federated utilized in the development of the federated learning model exhibits cifar-10 acuracy of 0. 92, precision of 0. 88, recall of 0. 94 and F1 score of 0, indicating that the classifier significantly outperforms the baseline random forest classifier. 91. The model is tested for joint training from the distributed data sources and shows relatively reasonable training time (12 hrs) and communication complexity (8. 5 GB). The state of affairs of blockchain transactions show that successful transaction records up to 1800 are registered with various activities such as product registration, quality certification of the product, record of transportation of the products, and storage records of the products etc. The datasets included here include various types of agricultural data that cover activities taking place on the farming fields and in processing plants, distributor's centers, and retail stores amongst other areas of concern and may cover data such as crop yields, weather updates, and transportation routes among others. Some of the software tools used in the development process include Python that can be used for data preprocessing and analysis;TensorFlow Federated which can be used in the creation of federated learning models;Ethereum can be used in the implementation of blockchain. This approach provides an invaluable roadmap for successfully tackling important demands in agricultural SCM systems and optimizing the success of decision-makers and entities along the food supply chain. © 2024 IEEE.

关键词： Software testing

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Enhancing Facial Expression Recognition with a Directional Attentive Neural Network 3

Enhancing Facial Expression Recognition with a Directional A...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Masson, Palak Agrawal, Rishi Sharma, Amit Parashar, Amit Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Greater Noida India Gla University Department of Computer Engineering & Applications Mathura India Lovely Professional University School of Computer Application Phagwara India Pranveer Singh Institute of Technology Basic Science & Humanities-Chemistry Kanpur India

ISBN: (纸本)9798350384598

The proposed DANN was developed for better recognition of facial expression. The proposed DANN combines the attention and directional filters, which filter out features that focus on the informative facial regions while capturing the directional patterns in the facial queue. The average accuracy after benchmarking all the 10 experiment trials was in the very high level with the proposed DANN on datasets combining CK+, FER2013, and RAF-DB, with an average accuracy of 88.04%. The model showed a good trade-off between precision and recall: its average weighted F1 score was 0.881, while the average precision and recall were 0.883. This implies that the model will maximize the identification of true positive samples while, at the same time, minimizing that of false positives and false negatives. All these results are indicative of the robustness and reliability of the DANN architecture in classifying the facial expressions correctly across the different datasets and different experimental settings. The results suggest the high potential of DANN in furthering the state-of-the-art facial expression recognition with prosperous affective computing, human-computer interaction, and psychology. © 2024 IEEE.

关键词： Affective computing Attention mechanisms Deep learning Directional Attentive Neural Network Facial expression recognition

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Thiyagarajan, Gomathi Swathi, Y. Cmr Institute of Technology Department of Computer Applications Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates. © 2025 IEEE.

关键词： Marketplaces

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