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6th National Conference on Advances in Multidisciplinary Design, Analysis and Optimization, NCMDAO 2023

作者： Kiran, Rakesh Sharma, Deepak Anand, Ashish Department of Mechanical Engineering Indian Institute of Technology Guwahati Assam India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam India

ISBN: (纸本)9789819611577

We propose a dual branch Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) model to differentiate between Parkinson’s disease (PD) patients and healthy subjects using gait data. Spatial and temporal features are extracted from the left and right feet gait data. The obtained feature map is combined using an LSTM layer, and the attention layer is used to concentrate features specific to a time zone. We use a publicly available PhysioNet data, which contains the vertical ground reaction force of the left and right feet of subjects. Results demonstrate that the proposed model performs better than existing techniques, giving a test set accuracy of 99.3%. Additional experiments are conducted to find out the best suitable time step, which again helps to increase the test set accuracy to 99.7%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware Strategies in Wireless Sensor Networks

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Priya, Naincy Sinha, Priyanka Singh, Pranita Jain, Arpit Kumar, Munish GLA University Department of Computer Engineering & Applications U.P. Mathura India Muzaffarpur Institute of Technology Department of Computer Science & Engineering Bihar Muzaffarpur India Government Engineering College Khagaria Department of Computer Science & Engineering India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP India

ISBN: (纸本)9798350394412

In today's surplus world, wi-fi sensor networks are essential for many systems. This network encapsulates sensor nodes powered by irreplaceable batteries, preserving a fixed topology for displaying specific locations. Their efficiency makes it crucial for large data packages. Wireless sensor networks continuously analyze physical factors such as temperature, pressure, ambient soft, sunlight and humidity, dominating the process Energy consumption monitoring is a key task in this area. To address this framework, this paper proposes a different approach: a modified controlled, privacy-controlled approach to record collection. The method aims to reduce redundant sensitivity, thus increasing system lifetime by reducing the frequency of data transfer between nodes and large sinks A comparative study with existing methods shows how the proposed method is effective. Performance findings show that the proposed bundling method (MEBPP) provides high packet transmission values while consuming less power and extending network lifetime. © 2024 IEEE.

关键词： Data aggregation

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Intense Triad Defender for End-User Security in Cyber Physical System 2

Intense Triad Defender for End-User Security in Cyber Physic...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Sheryl Sharon, G. Maranco, M. Nidhya, R. Sivakumar, M. Coimbatore Institute of Technology Department of Computer Science and Engineering Coimbatore641014 India SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

The rapid advancement of technology has undoubtedly brought comfort to humanity, but it also necessitates robust authentication measures to ensure security in the ever-expanding e-world. This research aims to address the critical aspects of security, focusing on end-user authentication. The proposed solution incorporates multiple factors, including location-based authentication, text-based authentication with enhanced encryption standards, and facial recognition authentication. Location-based authentication serves to restrict access geographically, mitigating the risk of impersonation by enforcing strict geographic limits on facility access. Strong text passwords, utilizing a unique algorithm with a classic salt method, add an extra layer of complexity for potential attackers, making it challenging to compromise user credentials. The incorporation of facial recognition technology acts as a deterrent against non-human entities attempting unauthorized access. Leveraging deep learning and API servers ensures precision and scalability in the model, enhancing its effectiveness in real-world scenarios. These multi-factor authentication techniques collectively contribute to a secure, reliable, and forward-looking solution to address the evolving landscape of digital security threats. In adhering to the age-old adage "prevention is better than cure,"the research emphasizes the importance of proactive measures to safeguard against potential security breaches. © 2024 IEEE.

关键词： Network security

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A Multi Model Deep Learning Framework for Sarcasm Detection in Regional Social Media: An Overview 1

A Multi Model Deep Learning Framework for Sarcasm Detection ...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Bhadgale, Anilkumar M. Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Sarcasm detection in social media is a challenging task due to its inherent reliance on contextual cues, tone, and cultural nuances. In recent years, multi-model deep learning frameworks have emerged as a powerful approach for addressing these challenges, particularly in regional social media, where language variations and local idiomatic expressions complicate the detection process. This survey explores the latest developments in multi-model deep learning frameworks for sarcasm detection, focusing on their application in regional social media. The survey begins by reviewing foundational techniques in sarcasm detection, including traditional machine learning approaches that rely on handcrafted features. These methods, although effective in certain contexts, often fail to capture the subtleties of sarcasm in informal, region-specific languages. The advent of deep learning has led to significant advancements, particularly through models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. These architectures, combined with Natural Language Processing (NLP) techniques, have enhanced the ability to identify sarcasm through text analysis. However, single-modal approaches focusing solely on text fail to fully capture sarcasm's multimodal nature, especially on platforms where users often express themselves through a combination of text, images, emojis, and video. This has led to the development of multi-model frameworks that integrate various data modalities, such as text, image, and user behaviour, to better understand the context of sarcastic expressions. In regional social media, where local language and cultural symbols play a crucial role, these multi-model approaches prove even more valuable. This survey highlights key multi-model frameworks, emphasizing their use in regional settings. By examining datasets, model architectures, and evaluation metrics, the survey underscores the importance of combining textual and non-textual

关键词： Convolutional neural networks

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An Approach to Radiotherapy Treatment Planning based on Machine Learning Algorithms 4

An Approach to Radiotherapy Treatment Planning based on Mach...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Divya, Komati Bhargavi, C.H. Abirami, M. Arjun, Ponnaganti Padmaja, Pabbathi Dhanwanth, Batini Panimalar Institute of Technology Department of Computer Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India St.Peter's Institute Of Higher Education And Research Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350385793

Customizing radiation treatments for each patient is a formidable obstacle in the fight against cancer. Because they rely on human intervention and generalization, traditional methods often provide less-than-ideal results and unwanted side effects. Utilizing patient-specific anatomical and tumor-specific data, this research analyzes the revolutionary possibilities of machine learning for planning radiation therapy. The goal of this research work is to forecast radiation doses, identify tumor boundaries, and reduce the exposure to healthy tissue using Deep Learning (DL), Support Vector Machine (SVM) and Random Forest (RF) models. Radiation planners must prioritize data heterogeneity, model interpretability, and clinical validation if machine learning has to be widely used. Improved patient outcomes and quality of life may be achieved via the use of Machine Learning (ML) to improve the radiation efficacy and accuracy, as shown in this research study that synthesizes existing research. © 2024 IEEE.

关键词： Radiotherapy

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Enhanced Web based Multi-Platform E-voting Solution 5

Enhanced Web based Multi-Platform E-voting Solution

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Nallapareddy, Anusha Swapna, T. Shanmugam, Sundaramurthy Shabu, Jany Refonaa, J. Hyderabad India G Narayanamma Institute of Technology and Science Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331504403

In democratic nations such as India, the act of voting is a basic right that grants individuals the power to choose their leaders. Traditionally, voting has typically taken place at specific locations called polling booths, either in centralized or distributed. Every Indian citizen above 18 years of age has the right to vote and participate in choosing their representatives. Voters go to the polling booths where they can cast their votes under the observation of authorized individuals. However, this traditional voting system has various challenges such as low voter turnout and instances of fraudulent voting. To address these issues, a secure multi-platform E-Voting system is proposed in this research, developed using the Flutter framework and Firebase backend services. The system aims to verify each voter before they can cast their vote, employing multiple levels of verification. The proposed application consists of an administrator (admin) interface and a voter interface. The admin interface allows for the addition of elections, candidates, and constituencies, while the voter interface requires voters to register, log in, and cast their vote. The verification process includes One Time Password (OTP) authentication, face recognition, fingerprint verification, and validation through government-issued identification cards. Two commonly used identification cards in India are the Aadhaar card and the Permanent Account Number (PAN) card, which are unique to each citizen. By utilizing this proposed online voting application, eligible voters can conveniently cast their votes from anywhere in the world, using internet-connected devices like mobile phones or tablets. The primary objectives of the proposed system are to increase voter turnout and reduce instances of fraudulent voting. Furthermore, conducting elections through this application also helps reduce the required workforce. © 2024 IEEE.

关键词： Authentication

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Enhancing Energy Efficiency with a Dynamic Trust Measurement Scheme in Power Distribution Network

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computers, Materials & Continua 2024年第3期78卷 3909-3927页

作者： Yilei Wang Xin Sun Guiping Zheng Ahmar Rashid Sami Ullah Hisham Alasmary Muhammad Waqas Zhejiang Electric-Power Corporation Research Institute Zhejiang310014China Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Trusty Cloud Technology Co. Ltd.Beijing100022China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and EngineeringTopi23640Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir18050Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia

The application of Intelligent Internet of Things(IIoT)in constructing distribution station areas strongly supports platform transformation,upgrade,and intelligent *** sensing layer of IIoT comprises the edge convergence layer and the end sensing layer,with the former using intelligent fusion terminals for real-time data collection and ***,the influx of multiple low-voltage in the smart grid raises higher demands for the performance,energy efficiency,and response speed of the substation fusion ***,it brings significant security risks to the entire distribution substation,posing a major challenge to the smart *** response to these challenges,a proposed dynamic and energy-efficient trust measurement scheme for smart grids aims to address these *** scheme begins by establishing a hierarchical trust measurement model,elucidating the trust relationships among smart IoT *** then incorporates multidimensional measurement factors,encompassing static environmental factors,dynamic behaviors,and energy *** comprehensive approach reduces the impact of subjective factors on trust ***,the scheme incorporates a detection process designed for identifying malicious low-voltage end sensing units,ensuring the prompt identification and elimination of any malicious ***,in turn,enhances the security and reliability of the smart grid *** effectiveness of the proposed scheme in pinpointing malicious nodes has been demonstrated through simulation ***,the scheme outperforms established trust metric models in terms of energy efficiency,showcasing its significant contribution to the field.

关键词： IIoT trusted measure energy efficient

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Sentiment Classification for Depression Detection: Integrating Capsule Networks with CNNs on Review Data

Sentiment Classification for Depression Detection: Integrati...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Sagili, Sreenivas Reddy Shibi, B. Rvs, Praveen Anjana, P. Rochester Institute of Technology Department of Computer Science United States Sona College of Technology Computer Science and Engineering Tamil Nadu Salem India LTI Mindtree Digital Engineering India Awh Engineering College Computer Science and Engineering Kerala India

ISBN: (纸本)9798331507541

This research is focused on the analysis of how Capsule Networks should be combined with Convolutional Neural Networks (CNNs) for improving the sentiment classification with the target of depression detection based on the data offered by the reviews. Other methodologies of analysing the sentiment of a phrase may encounter disability in the process of analysing texts in natural language following depression since they do not analyse the text deeply enough to consider the context. The proposed method intends to enhance accuracy and resistance when compared to state-of-the-art techniques by combining the attributes of Capsule Networks, that effectively maintain the spatial hierarchy and the connections within the data, with CNNs' feature extraction ability. The proposed hybrid model helps overcome disadvantages of the plain sentiment analysis because it identifies intricate patterns and dependencies in the review data and enhances the depressive sentiments' detection. This paper assesses the propriety of the integrated Capsule-CNN through an evaluation on review datasets in relation to its ability in the successful differentiate between depressive and non-depressive sentiments. The findings reveal a considerable increase in method accuracy and present a leap of progress in the field of automatic depression detection, which in turn helps to develop more accurate and efficient tools for monitoring patients' mental status. © 2025 IEEE.

关键词： Emotion Recognition

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Genetic Algorithm Based Deterministic Workflow Scheduling with Load Balancing 2

Genetic Algorithm Based Deterministic Workflow Scheduling wi...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Mittal, Punit Kumar, Satender Sharma, Swati Department of Computer Science and Engineering Quantum University Roorkee India Department of Information Technology Meerut Institute of Engineering and Technology Meerut India

ISBN: (纸本)9798331530389

A crucial problem in cloud computing is load balancing, which makes it challenging to guarantee that services operate as intended in accordance with quality of service (QoS), performance reviews, and service contracts that cloud service providers (CSPs) demand of businesses. The main goal is to distribute workloads among computer resources in order to improve performance. Load balancing is categorized as an "NP-hard" problem due to the ample solution space and the time needed to identify the optimal solution. While only a few approaches may provide an exact answer in polynomial time, prior research shows that metaheuristic-based tactics can find correct solutions in a fair amount of time. We use a framework that integrates Neural Network Task Classification (N2TC) and Genetic Algorithm Task Assignment (GATA) for load balancing. By taking load balancing and task completion time into account, this approach seeks to maximize load distribution and reduce process execution time. Extensive simulations of application operations were carried out to evaluate the effectiveness of the suggested load-balancing strategy. Results have shown that the proposed framework outperforms tournament selection genetic algorithm (TSGA), Heterogeneous Earliest Finish Time (HEFT), and Performance Effective Task Scheduling (PETS) heuristics in terms of load balancing up to 4.52% and makespan by 6.47%. © 2025 IEEE.

关键词： Genetic algorithms

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Intelligent E-Commerce Chatbot for Personalized Product Recommendations Using Text Mining and Machine Learning 2

Intelligent E-Commerce Chatbot for Personalized Product Reco...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Minu, M.S. Adithya Kavya SreeRanjini Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering with Specialization in Big Data Analytics SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

Innovative technology solutions have been developed in response to the growing need for effective and customized client contact on e-commerce platforms. This work introduces an intelligent chatbot system that uses machine learning and text mining techniques to improve user satisfaction and answer customer concerns. Advanced Natural Language Processing (NLP) models have been trained on large datasets, allowing for precise user intent interpretation and pertinent product recommendations. A strong recommendation engine is included into the system architecture to aid in decision-making, ultimately enhancing the customer experience by providing individualized, real-time help. The study also highlights the significance of data privacy by using privacy-preserving techniques to safeguard user data. The system's scalability and performance benefits are highlighted through thorough comparisons with current models. By showcasing the useful uses of machine learning and text mining in enhancing customer contact and service delivery, this study makes a substantial contribution to the development of AI-driven solutions in e-commerce. © 2024 IEEE.

关键词： Big Data Chatbot Cost Data Mining e-Commerce Hadoop Machine Learning Product Purchasing Recommendation

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