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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Susindra S Sweta B Keerthikha Shri R K. Brindha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems.

关键词： Industries Emotion recognition Technological innovation Sentiment analysis Decision making Machine learning User experience Real-time systems Internet of Things Recommender systems

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Goal-Oriented Communication, Estimation, and Control Over Bidirectional Wireless Links

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IEEE Transactions on Communications 2025年第5期73卷 3031-3045页

作者： Cao, Jie Kurniawan, Ernest Boonkajay, Amnart Pappas, Nikolaos Sun, Sumei Popovski, Petar School of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Institute of Infocomm Research Agency for Science Technology and Research Fusionopolis 138632 Singapore Linköping University Department of Computer and Information Science Linköping58183 Sweden Aalborg University Department of Electronic Systems Aalborg9220 Denmark

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost. © 1972-2012 IEEE.

关键词： Markov processes

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Integration of a Hybrid Model with XAI for Stock Price Forecasting: A Dashboard-Driven Approach

Integration of a Hybrid Model with XAI for Stock Price Forec...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jagannathan J Veera Venkata Shivamani Krishna Goditi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This paper on Integration of a Hybrid Model with XAI for Stock price Forecasting focuses on improving the accuracy and transparency of stock price predictions through a combined model of Long Short-Term Memory (LSTM) and MultiProphet, with an emphasis on explainable artificial intelligence (XAI). LSTM which is a deep learning model, is good at identifying patterns in time-series data. while MultiProphet which is an enhanced version of Facebook's Prophet model, effectively handles seasonal variations and anomalies in financial data. By integrating both models, the hybrid approach delivers more reliable stock forecasting. In addition, this project integrates XAI techniques like SHAP and LIME to offer greater insight into how the model generates predictions, which is crucial for informed financial decision-making. Dynamic dashboard provides an intuitive interface for users to track stock price trends, view forecasts, and access explanations of the model's predictions in real-time. This combination of improved prediction accuracy and model transparency enables better decision-making by stakeholders, who can base their actions on clear and interpretable information. Overall, the project represents a significant advancement in making advanced AI models more accessible and actionable in practical, user-oriented financial applications.

关键词： Analytical models Technological innovation Accuracy Explainable AI Decision making Predictive models Market research Data models Forecasting Long short term memory

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Enhancing IoT Security Through Hierarchical Message-Passing Graph Neural Networks: A Trust-Driven Strategy for Identifying Malicious Nodes 1st

Enhancing IoT Security Through Hierarchical Message-Passing ...

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1st International Conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Senthil Kumar, C. Vijay Anand, R. School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9783031808388

Internet of Things (IoT) gadgets connect and simplify life in several ways. The independence of these technologies poses some issues, such as the protection of security and privacy from malicious and compromised nodes inside the network. To overcome this complication, Enhancing IoT Security Through Hierarchical Message-Passing Graph Neural Networks: A Trust-Driven Strategy for Identifying Malicious Nodes (HMPGNN-Trust-IoT) is proposed. These issues are addressed by the proposed HMPGNN-Trust-IoT solution, which develops a mechanism for recognizing malicious and compromised nodes utilizing trust-based deep learning. With the help of the dataset provided, HMPGNN-Trust develops a worldwide model to predict the IoT node’s irrational behavior. A global dataset comprising 19 trust parameters on three main categories including knowledge, experience, and reputation are used in the proposed HMPGNN-Trust-IoT technique. The data are fed to pre-processing. In preprocessing, it cleans and normalizes the data in the dataset by utilizing a Nanoplasmonic Ultra Wideband Band Pass Filter (NUWBF). HMPGNN-Trust uses the idea of communities using dedicated servers to divide the dataset into smaller portions for effective training to lessen the computational strain. Finally, it classifies the nodes by utilizing a Hierarchical Message-Passing Graph Neural Network (HMPGNN) into benign and malicious nodes. The proposed HMPGNN-Trust-IoT is implemented using Python. To identify Trust Management in IOT performance metrics like precision, accuracy, and F1-score are considered. Performance of the HMPGNN-Trust-IoT approach attains 24.11%, 28.56%, and 22.73% high accuracy, 21.89%, 23.04%, and 9.51% high precision, 25.289%, 15.35%, and 19.91% higher F1-Score compared with existing methods such as the Securing IoT along Deep Federated Learning: A Trust-based Malicious Node Identification method (TM-ANN -IoT), the Trust-driven reinforcement selection technique in federated learning on Internet of Thin

关键词： Graph neural networks

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An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

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computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

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Unveiling the Potential of Audio Classification for Poultry Health Diagnosis: A Deep Learning Approach 1st

Unveiling the Potential of Audio Classification for Poultry ...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Kadam, Shivanjali Ambadas Madhuranthakam, S. Kavya Sree Sujatha, R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9783031823886

Audio classification has become a notable area of interest, particularly for its promising uses in the domain of poultry farming, where early detection of health issues is crucial. However, there is a lack of research on using deep learning techniques for this purpose. This study aims to fill this gap by exploring deep learning’s potential for detecting various health issues in poultry. We propose a ma chine learning approach using audio data, collected from healthy and unhealthy birds, including vocalizations, breathing patterns, and movement noises. After preprocessing and feature extraction, machine learning models were trained to classify audio signals into healthy and diseased categories. The evaluation metrics, including accuracy of 0.9423, precision of 0.88, recall of 0.80, and F1-score of 0.8380, were used to demonstrate promising results. This research contributes to developing non-invasive and cost-effective methods for early disease detection in poultry, enhancing animal welfare and industry sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Deep Learning-Based Enhanced Object Detection for Humanoid Robots

Deep Learning-Based Enhanced Object Detection for Humanoid R...

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Akshit Negi Harshita Patel School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society.

关键词： Deep learning Analytical models Humanoid robots computer architecture Object detection Medical services Object recognition Security Computational intelligence Business

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Histopathological Image Analysis Using Improved Gorilla Troops Optimizer for Lung Cancer Detection

Histopathological Image Analysis Using Improved Gorilla Troo...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： N Bharanidharan V Nepolian School of Computer Science Engineering And Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

Among many different modalities, histopathological imaging is one of the important and popular diagnostic technique for lung cancer. computer aided diagnostic tools that are based on Artificial Intelligence will be very useful in analyzing those images and helps the clinician to take the decision about the disease occurrence and treatment options. In this research work, deep features are extracted from the histopathological images using three popular deep learning techniques namely VGG, DenseNet, and AlexNet. Then Improved Gorilla Troops Optimizer is suggested as feature transformation technique on the deep features extracted from the histopathological images for lung cancer identification. Random Forest algorithm is used for classifying the transformed features. Significance of proposed optimization as feature transform is proved through experimentation on the publicly available LC25000 dataset that contains 3 classes where one class belongs to benign category and another two classes belong to malignant lung lesions namely adenocarcinoma and squamous cell carcinoma. The proposed technique attains $98.2 \%$ accuracy in classifying the histopathological images into above mentioned three classes.

关键词： Deep learning Accuracy Squamous cell carcinoma Transfer learning Lung cancer Transforms Feature extraction Classification algorithms Random forests Optimization

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Early Time Series Anomaly Prediction with Multi-Objective Optimization

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第1期9卷 972-987页

作者： Chao, Ting-En Huang, Yu Dai, Hao Yen, Gary G. Tseng, Vincent S. National Yang Ming Chiao Tung University Institute of Information Management Hsinchu300093 Taiwan Oklahoma State University Department of Electrical and Computer Engineering StillwaterOK74078 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300093 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

Anomaly prediction, aiming to predict abnormal events before occurrence, plays a key role in significantly reducing costs and minimizing potential threats to mechanical devices. Monitoring machines using fixed-length time windows faces challenges in accommodating the varying characteristics of anomaly events. The lengthy and imbalanced sequence data associated with anomaly events further complicates the resolution of these challenges. Additionally, the inherent trade-off between accurate prediction and timely alarm is a crucial concern, posing difficulties in decision-making. This study puts forward a novel framework for early anomaly prediction, named early time series Anomaly Prediction with Neighbor Over-sampling and Multi-objective Optimization (APNOMO), pronounced as 'abnormal'. The framework employs three key techniques: 1) sliding windows that divide long input sequences into segments for prediction at proper intervals, 2) over-sampling and proposed neighbor over-sampling that handle imbalanced data in a novel way, and 3) multi-objective optimization that searches optimal thresholds to balance accurately prediction and timely alarm the abnormal. Experiments on a real-world dataset demonstrate APNOMO's superior performance over some state-of-the-art designs, with higher recall, F1 score, and more suitable earliness. It can predict anomalies 0.78-3.56 hours in advance, showcasing excellent early anomaly prediction capabilities for enabling predictive maintenance. © 2017 IEEE.

关键词： Predictive maintenance

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches 4

Early Detection of Childhood Malnutrition Using Survey Data ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Zumma, Thoufiq Rahaman, Anikur Islam Prova, Nuzhat Noor Haque, Tasriful Joy Bose, Julias Caeser Youki, Ravina Akter Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Washington University Science and Technology Department of Computer Science New York United States Pace University Department of Seidenberg School of Csis New York United States Victorian Institute of Technology Master of Information Technology and Systems Australia University of Wollongong Faculty of Artificial Intelligence and Science Australia University of Technology Sydney Faculty of Engineering and It Australia

ISBN: (纸本)9798331523923

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%. © 2025 IEEE.

关键词： Adversarial machine learning

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