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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Subhashini, S.J. Angelina, J.JaneRubel Niteesh, V. Reddy, V.Venkata Sai, V.Krishna Saradhi, T.Pardha SRM Madurai College for Engineering and Technology Computer Science and Engineering TamilNadu Sivagangai India School of Computing Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India

ISBN: (纸本)9798350322842

Pick and place robots are now extensively utilised in modern manufacturing facilities. The process of picking up the components or things and putting them in new locations is sped up by pick and place automation. The production rates of these processes can be raised by automating them. Decide and choose Robots handle monotonous tasks, freeing up human workers to focus on more challenging jobs. The food processing and packaging sector is expanding quickly. Manufacturers are utilising robotics to complete operations that would otherwise require dedicated physical labour to satisfy the increased demands and necessary efficiency. One of the most often used automation devices in the food packing environment is the pick and place robot. It is a disruptive brain ailment called Alzheimer's disease (AD). Since there is presently no treatment for it, early diagnosis is essential to both preventing and slowing the spread of the illness. In order to interpret AD utilising brain MRI scans and powerful deep learning models that can accurately find the areas of anomalies using medically validated bio-markers, this research conducts a comparison analysis.[16]. The process of analysing data, learning from that data, and using what has been learned to make decisions is known as machine learning. Indeed, machine learning is a subset of deep learning. Whilst it theoretically falls under the category of machine learning and operates similarly, it has various characteristics [17]. It can be a cylindrical robot that can move in all three planes of motion-horizontal, vertical, and rotational - or a spherical robot that can move in two planes of rotation and one plane of linear motion. It can also be an articulated robot or a scara robot (fixed robots with 3 vertical axes rotary arms). These robots have the ability to move objects from stationary surfaces to stationary surfaces, stationary surfaces to moving surfaces, moving surfaces to moving surfaces, and moving surfaces to moving surface

关键词： Beverages

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Multimodal Machine Learning for Mental Disorder Detection: A Scoping Review 27

Multimodal Machine Learning for Mental Disorder Detection: A...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Nguyen, Thuy Trinh Pham, Viet Hoang-Quoc Le, Duc-Trong Vu, Xuan-Son Deligianni, Fani Nguyen, Hoang D. School of Computing Science University of Glasgow United Kingdom University of Engineering and Technology Vietnam National University Viet Nam Department of Computing Science Umeå University Sweden School of Computer Science and Information Technology University College Cork Ireland

Recent advancements in machine learning and multimedia technologies have paved new ways for automatic medical diagnosis. In mental health, multimodal inputs such as visual and audible sensing data are promising to investigate the underlying mechanisms of many conditions, such as depression and bipolar disorders. With the increasing burden on healthcare systems, timely diagnosis of mental diseases using multiple modalities might benefit millions of people worldwide. This scoping review provides an exploratory overview of recent multimodal machine learning approaches for mental disorder screening. We also discuss a generalised end-to-end multimodal machine learning pipeline for future research and development of multimodal disease detection. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： bipolar disorders depression mental disorder diagnosis Multimodal machine learning stress disorders

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Review on Vision Based Human Motion Detection Using Deep Learning 4

Review on Vision Based Human Motion Detection Using Deep Lea...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Zachariah, Lino Maharajan, K. Mar Baselios College of Engineering and Technology Trivandrum India Science and Engineering School of Computing Kalasalingam Academy of Research and Education Department of Computer Krishnankoil626126 India

ISBN: (纸本)9798350300888

The challenging problems addressed by vision-based human motion recognition and its wide range of potential applications have captured the interest of numerous researchers. These applications encompass simple gesture recognition to intricate analysis of behavior in surveillance systems. Since there was a need for practical, automatic violence detection techniques that looked at the visual information obtained from security cameras placed throughout various locations, the violence detection sector became quite popular in the domain of human motion detection systems. As a result, there is a significant advancement in the methods for representing and recognizing human motion. The focus of this study is to discuss the recent advancements made by researchers in these fields. It offers a comprehensive analysis of the methodologies employed and evaluates the credibility of the various submissions. Additionally, the article offers guidelines on approaching future directions for this application. © 2023 IEEE.

关键词： Deep learning

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Simulated Evaluation of a System Controlled Multi-specialty Robotic-Assisted Surgery in Modern Healthcare 5th

Simulated Evaluation of a System Controlled Multi-specialty ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Nayak, Amanata Samal, Sumit Kumar Mishra, Sushruta Kumar, Anil Issa, Ali Ashoor Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India School of Computing DIT University Dehradun India College of Technical Engineering National University of Science and Technology Dhi Qar64001 Iraq

ISBN: (纸本)9789819760350

Surgical robot-driven systems are emerging as a strong modern approach in the healthcare sector. It is extremely fruitful in enhancing surgical techniques. With this constant rise in robotic surgery methods, there is a constant desire to develop more reliable and robust models for such operations to take place in a precise manner. In today’s world, robotic surgery is applied in many medical centers successfully and is widely accepted too. This study discusses the relevance of robotic surgery in the modern healthcare scenario. Also a novel system-enabled multi-specialty robotic surgical simulated model is proposed to enhance the quality of healthcare. Upon demonstration, it is observed that simulation with robotic surgery is very promising. A mean latency of 4.3 s was recorded with the proposed robotic surgical model and the average success rate observed is 87.1% which is quite high when compared to traditional medical approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Robotic surgery

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PIM-FFL: Evaluating Participation Index Metrics for Fairness Enhancement in Federated Learning 4

PIM-FFL: Evaluating Participation Index Metrics for Fairness...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Batool, Hina Xu, Jiuyun Ur Rehman, Ateeq Kaur, Gurjot Bharany, Salil College of Computer Science and Technology China University of Petroleum China School of Computing Gachon University Seongnam13120 Korea Republic of Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331540364

Federated learning (FL) is a technique that allows multiple devices to train a machine learning model without sharing their raw data, this preserves user privacy. However, traditional FL often suffers from problems like nodes failing to participate in training due to resource limitations. This negatively impacts the model's performance and fairness, making it harder for all devices to benefit equally. Ensuring fairness in FL is challenging due to the significant differences in clients' data quality, data size, and contribution levels. A notable issue arises when prime contributing clients do not receive fair rewards while underperforming still benefits. This disparity leads to an inequity causing complexity among contributors. As a result, clients may feel unappreciated, leading to a decrease in their interest in participating which ultimately compromises the complete reliability and effectiveness of the system. To address these challenges, a client clustering method is presented based on their participation index, which ensures that prime participants receive better model updates rather than underperformers which makes the system more reliable. Additionally, the Double Deep Q-Networks (DDQN) method is employed, as it determines the best actions through learning from past experiences and updating Q-values, which dynamically update participation count and reduce the model biases. Our experiments show that this clustering method substantially enhances fairness in Federated Learning, fostering a more efficient and cooperative learning environment that outperforms traditional methods like FedAvg and FedProx. © 2024 IEEE.

关键词： Federated learning

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Representation of Semantic Word Embeddings Based on SLDA and Word2vec Model

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Chinese Journal of Electronics 2023年第3期32卷 647-654页

作者： TANG Huanling ZHU Hui WEI Hongmin ZHENG Han MAO Xueli LU Mingyu GUO Jin School of Computer Science and Technology Shandong Technology and Business University Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing Key Laboratory of Intelligent Information Processing in Universities of Shandong Shandong Technology and Business University School of Information and Electronic Engineering Shandong Technology and Business University Information Science and Technology College Dalian Maritime University School of Computer and Information Technology Liaoning Normal University

To solve the problem of semantic loss in text representation, this paper proposes a new embedding method of word representation in semantic space called wt2svec based on supervised latent Dirichlet allocation(SLDA) and Word2vec. It generates the global topic embedding word vector utilizing SLDA which can discover the global semantic information through the latent topics on the whole document set. It gets the local semantic embedding word vector based on the Word2vec. The new semantic word vector is obtained by combining the global semantic information with the local semantic information. Additionally, the document semantic vector named doc2svec is generated. The experimental results on different datasets show that wt2svec model can obviously promote the accuracy of the semantic similarity of words,and improve the performance of text categorization compared with Word2vec.

关键词： Semantics Text categorization Resource management Task analysis

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A Graph Neural Network and Transformer Encoder Technique for Anomaly and Cyber Threat Detection in Smart Grids

A Graph Neural Network and Transformer Encoder Technique for...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Lakshmanan, M. Adnan, Myasar Mundher Reddy, R. Archana Vasukidevi, G. Aarthy, G. Nitte Meenakshi Institute of Technology Department of Eee Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Sr University School of Computer Science and Engineering Warangal India R.M.K. Engineering College Departmen of Cse Chennai India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350360660

Nowadays, Smart grids enhance traditional power grids by integrating information and communication technologies for more efficient power delivery and management. However, this integration introduces security vulnerabilities and their exploitation leads to cyber-attacks causing severe consequences like infrastructural damage, massive blackouts. This paper presents an effective approach to detect anomaly and cyber threat in smart grids based on deep learning. A novel Graph Neural Network and Transformer encoder (GNN-Transformer encoder) feature extraction method is proposed in this paper for anomaly and cyber threat identification. Initially, the input data is normalized using min-max normalization technique to transform the data into a common scale. The GNN is utilized for spatial feature extraction which conducts representation learning based on topology of power system. The transformer encoder with self-attention mechanism determines the importance of different time steps and captures long-range dependencies. The fully connected layers provided accurate prediction results by mapping the high-dimensional feature vectors to the final output. The proposed method is evaluated on PMU dataset and achieved high accuracy 97.90%, precision 0.82, recall 0.98. The GNN-Transformer encoder method outperformed existing anomaly detection models such as Gaussian Mixture Model (GMM), Spatial Pyramid Pooling with Convolutional Neural Network (SPP-CNN). © 2024 IEEE.

关键词： Cyber attacks

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A hybrid Improved Fully Homomorphic Encryption with AES to provide high security in Cloud computing

A hybrid Improved Fully Homomorphic Encryption with AES to p...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Ravindran, K. Prabhu, S. Nagendra Easwari Engineering College SRM Institute of Science and Technology Department of Information Technology Chennai India SRM Institute of Science and Technology Department of Computational Intelligence School of Computing India

ISBN: (纸本)9798350319200

Since this information may be accessible by a third party and could compromise the client's information's confidentiality, there is a barrier between the customer and the true location of his information. The most often used approach to bridging this security gap has been to first encrypt online data using traditional methods before sending it to the cloud service provider. But to decrypt the information and complete the calculations that are required, the customer must give the server a secret key. Using homomorphic encryption, mathematical calculations can be made on encrypted information without having to first decode it. The electrocardiogram (ECG) measures and finds arrhythmias in the circulatory system. It differ from individual to individual, these characteristics are now employed for identification. So it's crucial to secure an ECG signal. A safety method known as Homomorphic Encryption (HE) handles encrypted information without the need for *** identical initial information findings are provided by HE. In this research, an Improved Fully Homomorphic Encryption (IFHE) technique using Advanced Encryption Standard (AES) is proposed for encoding the ECG measures to enhance safety while safeguarding the confidentiality of information while removing sharing key limitations. The information of the customer is encrypted in the cloud server discussed in this work, so required computations can be performed on the encrypted information. To do this, it is proposed that the method of assessment be added as a further layer of security to the AES technique, which then executes several homomorphic mathematical computations on the encrypted information in place of the initial information. To increase safety, a linear polynomial equation with changeable values is proposed for the assessment ***, the IFHEAES method has a 100% success rate in decryption (recovering the original signal) and 95.8% efficiency in categorization. Its decryption and enc

关键词： Advanced Encryption Standard Cloud computing Improved Fully Homomorphic Encryption Performance Measures Security

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A Distributed Pricing Mechanism and Optimal Schedulingin Smart Grid using Improved Grey Wolf Optimization

A Distributed Pricing Mechanism and Optimal Schedulingin Sma...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Nagendar, Y. Alabdeli, Haider Prashanth, V. Aarthy, G. Thangavel, C. Sr University School of Computer Science and Engineering Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Mechanical Engineering Salem India

ISBN: (纸本)9798350360660

In recent days, energy is most important resource new techniques and methods which introduced to fulfill energy demand for price-based demand response (DR). The preface to this study has shown that load management strategies implemented based on Costlier scheduling for low or consistent energy users results from price-based demand response strategies. Suggested work has taken into account and broadened the same concept to include numerous EV charging regions and corresponding loads from an analytical and implementation point of view. This research provides an innovative approach called Improved Grey Wolf Optimization (IGWO) that uses the distinct energy consumption patterns of EVs in each location to calculate EV charging costs. Dynamic price signal that is non-discriminatory is sent to all EV regions and stations. First, a brand-new mathematical model based on market dynamics and real-time load demand is provided to determine charging prices. The global optimum solution produced by the optimization algorithm successfully converges, confirming the efficacy of the suggested mechanism. Lastly, analysis and simulation outcomes are performed to demonstrate the accomplishments of our suggested task in standings of reasonable cost high user satisfaction distribution. IGWO demonstrated usefulness's revenue is modest by either mechanism or cost saving is 0.69-6.89%. © 2024 IEEE.

关键词： Demand response

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A Deep Learning Approach for ECG-Based Cardiac Anomaly Detection with Improved Feature Extraction 2

A Deep Learning Approach for ECG-Based Cardiac Anomaly Detec...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Thanjaivadivel, M. Purandhar, N. Bhasker, B. Ganesh, A. Reva University School of Computing and Information Technology Karnataka Bengaluru India Madanapalle Institute of Technology & Science School of Computers Andhra Pradesh Madanapalle India Sri Venkateswara College of Engineering Department of Cse Andhra Pradesh Tirupati India

ISBN: (纸本)9798350388916

Heart disease is a global health concern that requires accurate and timely prediction tools for successful management and treatment. Electrocardiogram (ECG) data analysis helps discover cardiac anomalies by revealing heart function and risk factors. ECG data is complicated and variable, making interpretation and prediction difficult. Researchers have used preprocessing, feature extraction, and machine learning algorithms to enhance heart disease prediction. Ensemble learning can improve predictive performance by combining model predictions. This study attempts to improve cardiac risk assessment and patient outcomes by merging multiple *** goal of this paper is to improve heart disease prediction accuracy by evaluating ECG data using machine learning techniques. To improve cardiac abnormality identification, preprocess ECG images, extract key characteristics, and use ensemble learning. The work investigates signal contour extraction and dimensionality reduction to provide a comprehensive cardiac risk assessment *** study uses a multi-step technique to improve heart disease prediction using ECG data. Preprocessing ECG pictures with signal contour extraction, grayscale conversion, and Gaussian blurring improves data quality and clarity. To find predictive modelling features, feature extraction methods like PCA dimensionality reduction are used. Ensemble learning methods like the Voting Classifier combine predictions from numerous machine learning models to increase predictive performance. Hyperparameter adjustment improves model performance, achieving 98% testing set correctness. This study's methods give a complete foundation for precise and fast cardiac risk assessment. © 2024 IEEE.

关键词： Dimensionality reduction

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