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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Pande, Sagar dhanraj Singh, Gurpreet Zekrifa, djabeur Mohamed Seifeddine Kodgire, Shilpa Prashant Patel, Sunil A. le, Viet-Thanh School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Faculty of Computational Science GNA University Phagwara India Centre for Quantum Computation and Communication Technology Sydney Australia Maharashtra Institute of Technology Maharashtra Aurangabad India Computer Engineering Department L.D. College of Engineering Ahmedabad India Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9781032669663

A threshold key-sharing technology-based publicly verifiable key-sharing technology is presented to explore blockchain users’ private keys’ security. After receiving key fragments, participating nodes validate them. When splitting the key, the central splitting node does evil. In the critical recovery stage, the key fragments of the nodes participating in key splicing are publicly verified to prevent them from doing evil. In the critical distribution stage, add Ids to participating nodes to track malicious nodes and update node status in real-time, and a dynamic threshold mechanism is designed. To maintain private key fragment integrity, the owner of the key fragment and the controller node redistribute it to the new participating nodes once the vital fragment node goes offline. Experimental results reveal that this scheme’s private key recovery rate is 80% and has threshold properties, traceability, unforgeability, and recoverability. © 2024 The Author(s).

关键词： Blockchain

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Towards Precision Cardiovascular Medicine - A Novel Framework for Risk Prediction Using MultiOmics and Imaging data 3

Towards Precision Cardiovascular Medicine - A Novel Framewor...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： lalitha, S. Sujay, V. Krishna, A.P. Jaya Navaneethan, S. Abijith, G.R. Priya, S. Sugantha Department of Cse Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Krishna University College of Engineering and Technology Department of Computer Science and Engineering Krishna Andhra Pradesh Machilipatnam India Sona College of Technology Department of Electronics and Communication Engineering TamilNadu Salem India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology St.Joseph's Institute of Technology Tamil Nadu Chennai India Department of Computer Science Dr Sns Rajalakshmi College of Arts and Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798331542375

The framework integrated multi-omics data and imaging characteristics to boost predictive precision for cardiovascular risk. Existing studies chiefly relied on restricted clinical information using common logistic regression designs unable to capture cardiovascular diseases (CVd's) intricacy. Such dated techniques experienced diminished accuracy and lack personalized risk prediction. But these restrictions are surpassed by the suggested system since it uses deep learning algorithms such as Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for structured data and visual information. To prepare the data, standardization and simplification of complexity firstly applied preprocessing techniques such as PCA and t-SNE first, before utilizing feature selection via RFE and lASSO. Results demonstrated substantially improved outcomes with 0.85 accuracy, 0.82 sensitivity and an AUC-ROC of 0.90, delivering a more trustworthy tool for early intervention and customized cardiovascular care. Complex sentences mixed with shorter, simpler constructions to vary structure and keep readers engaged throughout the lengthier description. © 2025 IEEE.

关键词： Recurrent neural networks

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Neural Networks for Natural language Processing

Neural Networks for Natural Language Processing

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： deshpande, Sudhindra B. Velrani, K. Sundara Bhagyalakshmi, A. Vani, M. Sowmya Ananthi, S.N. Sahu, devika Department of Information Science and Engineering KLS Gogte Institute of Technology Udyambag Karnataka Belagavi590006 India Department of Information Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Application Mohan Babu University Andhra Pradesh Tirupati517501 India Department of Computer Science and Engineering S.A Engineering College Tamil Nadu Chennai600077 India Department of Computer Science and Engineering Government Engineering College Chhattisgarh Raipur492001 India

ISBN: (纸本)9798350374933

Artificial neural networks have made Natural language Processing (NlP) a lot different because they can encode language data more efficiently. A lot of different natural language processing tasks, like mood analysis, translation, and text production, have been done very well with these networks are based on the neuronal structure of the human brain. This abstract delves into the fundamental principles of neural networks in natural language processing, with a particular emphasis on notable structures, including the more recent Transformer models, CNNs, and RNNs. The investigation investigates the potential future trajectories, challenges, and applications of improving language comprehension and developing assignments. © 2024 IEEE.

关键词： Natural language processing neural networks neural networks NlP recurrent neural networks RNNs

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Machine learning based human interacted robotic intelligence to detect the different categories of speech 2

Machine learning based human interacted robotic intelligence...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Singh, Ravinjit Yasmeen Srivastava, Aditi Bijeshdhyani, Bijeshdhyani deepa, d. Malaysia Cvr College of Engineering Hyderabad Csit Department India G.L.Bajaj Institute of Technology and Management India Graphic Era Deemed to Be University Department of Management Studies Dehradun India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India

ISBN: (纸本)9798350315905

Realizing artificial intelligence depends heavily on human-robot interaction (HRI) technology. Here, we present dual-function sound actuators built on graphene for human-robot exchanges that use machine learning. (GHRI). The GHRI's triboelectric acoustic detection mechanism and thermoacoustic sound output mechanism allow it to serve as both a prosthetic ear and tongue. Triboelectric materials, electrodes, and thermoacoustic sources made possible by laser-induced graphene contributed to the overall success of the combined device. The GHRI can recognize speech identities, feelings, substance, and other information in the human speech by optimizing the structure parameters to produce high sensitivity and working longevity. With the help of machine learning, a convolutional neural network is used to teach 30 voice categories, with an accuracy of 99.72% and 97.83% in training and test datasets, respectively. In addition, AIs can use GHRI to communicate through recognized voice characteristics. The implications of our research for the future of artificial intelligence in robots are vast. © 2023 IEEE.

关键词： Graphene

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An Industrial IoT driven Approach for Sewage Gas Identification and Reporting System using Intelligent Sensors with logical Controller 1

An Industrial IoT Driven Approach for Sewage Gas Identificat...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Rajasubha, J. Ramya, R. Juliet, A. Hency Janakiraman, V. devi, S. Rukmani Karuppaiah, S. Dhanalakshmi College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

Based on a real-time sewage gas level detection and monitoring system, this system seeks to offer intelligent solutions for the monitoring of toxic sewage gases. An alarm is issued to the observer who is looking at the circumstances from a distance whenever a specific threshold is crossed. Following this, the data is transmitted together with different gas ppm levels, indicating if the worker can clean or operate securely in that region. The suggested solution incorporates both the IoT platform and the remotely located IoT monitoring devices. This necessitates finding the appropriate threshold values for septic plants and facilities, as well as calibrating gas sensors for use in industry. The gear is meant to provide the sewage worker advance notification whenever the concentration of toxic gaseous substances grows over time, ensuring their safety. The presence of gas, temperature, and other factors in sewage may be detected using a variety of sensors. By notifying sewage workers as well as cleaners via SMS whenever the threshold value drops below the observed levels, this technology helps save people from harmful infections, which is a social good. A number of harmful gas concentrations are graphed out for real-time monitoring, and this is how it accomplishes its job. In order to assess the efficacy of the suggested model, this paper cross-validates it with the Conventional Sewage Gas detection (CSGd) based on GPRS functioning. The goal of the cross-validation is to create an IoT based Sewage Gas detection (IoTSGd) system that can monitor and analyze toxic gas levels in real-time and notify workers. Firebase receives the measured gas levels. If the levels rise over a certain point, it will notify the authorized individuals working remotely through their linked mobile devices. An SMS message can alert the authorities if the sewage system is likely to overflow. For the purpose of monitoring, an android app was created and connected with the planned system. © 2024 IEE

关键词： Internet of things

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Blockchain Optimized Routes for Efficient Short distance donation Services in Intelligent Urban Mobility 10

Blockchain Optimized Routes for Efficient Short Distance Don...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Patil, Aabha Amey Goradiya, Uma Bhavin Patil, Pallavi Nitin Revanna, Jai Keerthy Chowlur Shree L. R. Tiwari College of Engineering Department of Computer Engg Mira Bhayandar India Harrisburg University of Science and Technology Isem HarrisburgPA United States

ISBN: (纸本)9798350384369

The aim of this research is to explore the integration of blockchain technology and the vehicle routing problem (VRP) in the context of short-distance donation services, with the primary objective being to enhance supply chain effectiveness and optimize donation logistics through the adoption of a blockchain-driven approach. The paper presents a novel methodology that incorporates blockchain principles to efficiently utilize resources and minimize trip lengths for donation transportation, thereby streamlining the VRP process. By leveraging the decentralized and transparent nature of blockchain, the proposed solution instills confidence among stakeholders and ensures secure and traceable transactions. The practical applicability of the algorithm is evaluated through simulation and case studies, considering factors such as vehicle capacity, route optimization, and cost-effectiveness. The findings highlight significant improvements in donation delivery procedures, demonstrating the transformative potential of blockchain technology in revolutionizing last-mile logistics for philanthropic activities. This study contributes significantly to the evolving field of blockchain applications by showcasing its revolutionary impact on enhancing the efficiency of donation management systems. © 2024 IEEE.

关键词： decentralized finance

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Cloud Computing: Innovations and Impacts on Global data Infrastructure

Cloud Computing: Innovations and Impacts on Global Data Infr...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Banumathi, S. Sivajothi, E. Indumathi, G.S. Kefyalew, Enku Yosef Sivakumar, J. Priya, V. Banu Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St Joseph's College of Engineering OMR Department of MBA Chennai India Technical and Vocational Training Institute Department of Electrical and Electronics Technology Addis Ababa Ethiopia St.Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India R.M.K. College of Engineering and Technology Department of Mathematics Thiruvallur District India

ISBN: (纸本)9798331512965

Cloud computing has transformed global data infrastructure with unparalleled scale, flexibility, and accessibility. This study examines how cloud computing innovations have changed data storage, processing, and management worldwide. By examining the progression of cloud services from simple storage and computing to sophisticated platforms and SaaS, we examine how they have transformed corporate operations, economic growth, and societal development. Virtualization, containerization, serverless architectures, and edge computing are the main cloud computing advancements, which we investigate through a literature study and case studies. data security, privacy, and regulatory frameworks are also considered while adopting the cloud. Cloud computing promotes digital transformation in banking, healthcare, education, and government, according to study. The industry's sustainability and green computing efforts and data centers' environmental impacts are also covered. We found that cloud computing continues to spur innovation by enabling new business models, global connectivity, and data-driven application growth. Cloud technologies' integration with AI, IoT, and 5G networks is likely to change the global data infrastructure, opening new research and development options. This report covers the cloud computing landscape, its advancements, and its potential to shape global data management and digital ecosystems. © 2025 IEEE.

关键词： Cloud platforms

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Multi Meta Information Fusion Graph Neural Networks for Session-based Recommendation

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 366-382页

作者： Wen, Yan Sun, Hui-Zheng liu, liang Zhou, Ya-Kun Bian, Wei Yan, Ming-Hai College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Shandong Port Group Co. Ltd Qingdao266000 China Geography and Environment Management Faculty of Environment Universoty of Waterloo WaterlooN2L3G1 Canada

Session-based recommendation aims to predict the anonymous user’s next click action based on his/her latest click sequence. Most state-of-the-art approaches use graph neural networks to model sessions as graphs to capture dynamic transitions between items within a session. Inspired by recent progress on hypergraph-based neural networks, we think it is a good candidate to model the commonly related items within a session, and it can provide a sound complement to the normal graph models. In addition, cross session information may also be helpful. But the excessive noise introduced by other sessions is a big challenge and is still not be well resolved by existing works. To this end, we propose a new approach, called Multi Meta Information Fusion Graph Neural Networks (MM-GNN) to infer user preferences for the current session in a more comprehensive way for the anonymous *** fuse information from normal graph and hypergraph to fully exploit their capabilities of capturing sequential behavior and group intention within a session. In order to better use cross session information, we propose a globallevel subsequent item injection module, which injects information of the most possible subsequent item of the last item in the current session in the global scope. Besides, to learn richer information on the hypergraph structure, we model the whole session as a hyperedge to complement sliding windows-based hyperedges when creating the hypergraph. We have evaluated the method on several benchmark datasets and the results show that our method outperforms state-of-the-art methods. © 2025, Taiwan Ubiquitous Information CO lTd. All rights reserved.

关键词： Graph neural networks

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Machine learning and deep Transfer learning approaches were used to create a Face Mask Identification model for COVId-19

Machine Learning and Deep Transfer Learning approaches were ...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Rao, B. Srinivasa Aarthi, d. Revathi, R. Verma, Shanti Nandankar, Praful V. Radhakrishnan, P. Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Karpagam College of Engineering Coimbatore Department of Computer Science and Engineering Tamil Nadu Tiruppur India Karpagam Academy of Higher Education Department of Computer Science Tamil Nadu Coimbatore India L J University Department of Computer Applications Gujarat Ahmedabad India Government College of Engineering Department of Electrical Engineering Maharashtra Nagpur India Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

COVId-19 pandemic has led to an international health emergency the WHO considers wearing a face mask an appropriate form of public health protection. This work will describe a face mask identification model that incorporates both deep and traditional machine learning techniques. Parts of the suggested model can be divided into two. Using Resnet50, the initial part of the system is set up for feature extraction. The second component classifies face masks using decision trees, support vector machines (SVMs), and the ensemble approach. The research will focus on three face-masked datasets. The Real-World Masked Face dataset Includes three datasets: real-world masked faces, simulated faces, and wild faces (lFW). 99.64% of RMFd's SVM classifier is accurate throughout testing.. It achieved a 99.49% accuracy rate in SMFd and a 100% accuracy rate in lFW. © 2022 IEEE.

关键词： COVId-19

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An Enhanced Packet delivery Ratio Improvement Protocol design over 5G Assisted Wireless Sensor Network Environment 2

An Enhanced Packet Delivery Ratio Improvement Protocol Desig...

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

作者： Balaji, T.S. Charaan, R.M.dilip Rajalakshmi, J. Samuthira Pandi, V. Shobana, d. Priya, J. lakshmi Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India S.A.Engineering College Department of Cse Tamilnadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Mechatronics Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388916

Sensors are pervasive in modern life, and Wireless Sensor Networks (WSNs) will play an increasingly important role in the years to come. Broadband, energy, coverage, and route optimization resource shortages are WSN's main drawbacks. Optimizing the packet delivery ratio is a crucial aspect of wireless sensor network (WSN) architecture. In order to enhance WSNs over the long term, previous approaches used a secure zone-based routing mechanism. For packet transmission, a new criterion was developed in multi-hop communication. It was built into the routing metric to protect against flooding, message manipulation, and dropping attacks. Congestion in WSN increases and reduces packet delivery ratio, even while energy conservation and attack resilience are accomplished. Our proposed solution, Advanced Path Selection with Traffic Estimation (APSTE), is a new way of routing data packets that takes flow differentiation into account. This should help increase the packet delivery ratio and solve this issue. In order to determine how effective, the suggested method is, it is cross-validated using the standard routing model known as link Consistency with Path Evaluation (lCPE). In order to improve the WSN's QoS parameters and extend the network's lifespan, the APSTE approach reduces the energy consumption of sensor nodes while maintaining or improving their logical throughput and packet acquisition rates. © 2024 IEEE.

关键词： 5G mobile communication systems

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