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BIT Conference (BITCON), International

作者： I. Sapthami Ch. Srilakshmi Batta Saranya N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology VNR VJIET Bachupally Hyderabad India AIDS Department CBITGandipet Hyderabad India Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.

关键词： Squamous cell carcinoma Prevention and mitigation Mortality Melanoma Machine learning Data augmentation Skin Classification algorithms Convolutional neural networks Skin cancer

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A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning

A Novel Forward Filter Feature Selection Algorithm Based on ...

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International Conference on Advanced computer Control, ICACC

作者： M A Anitha K K Sherly Assistant Professor of Computer Science College of Engineering Cherthala Kerala India Associate Professor of Information Technology Rajagiri School of Engineering & Technology Kerala India

ISBN: (纸本)9781665439206

In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.

关键词： Machine learning algorithms Redundancy Filtering algorithms Feature extraction information filters Minimization Filtering theory

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E-Buddy for Rescue Child Labour

E-Buddy for Rescue Child Labour

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BIT Conference (BITCON), International

作者： Ajmeera Kiran Prithiviraj Ananth P Veera Prakash N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Sona College of Technology Salem Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

Ebuddy is a ground breaking platform dedicated to rescuing and rehabilitating child laboures. By leveraging advanced technology, this initiative effectively connects with at-risk children, providing them with crucial education and support services. With a mission to break the vicious cycle of poverty and exploitation, Ebuddy collaborates with local communities and organizations to raise awareness about child labor and advocate for policies that safeguard children's rights. This platform offers comprehensive support services aimed at addressing the children's physical, emotional, and psychological well-being. By providing counseling, rehabilitation, and necessary medical assistance, Ebuddy empowers children to leave their past behind and embrace a better future.

关键词： Employee welfare Pediatrics education Psychology Morphology Organizations

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Hand Gesture Identification and Voice Command Based Hardware Reduction

Hand Gesture Identification and Voice Command Based Hardware...

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IEEE Punecon

作者： Amogh Godbole Vishal Gondke Kailas K. Devadkar Dept. of Information Technology Sardar Patel Institute Of Technology (SPIT) Mumbai India Dept.of Information Technology Sardar Patel Institute Of Technology (SPIT) Mumbai India Ph.D (Technology) in Computer Engineering Associate Professor Dept. Of Information Technology Sardar Patel Institute Of Technology (SPIT) Mumbai India

ISBN: (纸本)9781665498982

The use of physical devices like mouse and keyboard to communicate to our computer has hindered the natural interface between the computer and its user. To eradicate this barrier, we have designed a virtual mouse which will act upon the various hand gestures the user provides and will help them interact with the computer in a better way. The virtual mouse control of our system will keep a track of the movements of the fingers and palm, thus providing us with a specific output based on the recognized action of our hand. The proposed model will recognize both static as well as dynamic hand gestures and the results will show that the model that has been proposed and built will intuitively interact with the computer and it can be achieved the minimum usage of any hardware requirements. Our model will also have a major advantage over the current existing models, which is, it also has the feature of voice command recognition, meaning that even if the user if not able to provide appropriate gestures using their hand due to some medical condition, they will still be able to interact with the computer via voice commands.

关键词： Medical conditions Tracking Computational modeling IEEE Sections Keyboards Speech recognition Control systems

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IoT Based Human Biometric System Using Alcohol Level Detection for Driver Security

IoT Based Human Biometric System Using Alcohol Level Detecti...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Anurag Sinha Nitish Kumar Amrita Ticku Vyom Modi Meghal Shah Kapil Shrivastava School of Computing and Information Sciences IGNOU New Delhi India Department of Computer Science Lovely Professional University Phagwara Punjab India Bharti Vidyapeeth's College of Engineering India India Faculty of Engineering Marwadi Education Foundation Rajkot Gujarat India Department of Computer Science & Engineering Charotar University of Science & Technology Gujarat India Assistant professor Department of computer engineering and applications GLA University mathura India

In today's society, driving after intoxication has become a major factor in traffic accidents. It is common knowledge that operating a vehicle while intoxicated is a major contributing factor in a significant number of accidents. The effects of drunk driving go beyond the drunk driver; they also affect other drivers and passengers in the same vehicle. As a result, this growing threat needs to be addressed immediately. Our project's goal is to create a system that uses the Internet of Things (IoT) to find traces of alcohol in someone's breath and apply preventive measures in order to effectively address this issue. Although there are laws against drunk driving, it is difficult to enforce them widely because law enforcement cannot constantly be present on the roads to spot drunk drivers. This restriction increases the chance of accidents significantly. In order to reduce future risks, a trustworthy system that can recognize and discourage drunk drivers is urgently needed. Drunk driving deaths are increasing, so we need to figure out how to reduce these kinds of deaths. The breath analyzing sensor used in the suggested system is the MQ-3, which calculates the breath alcohol content of an individual. The amount of alcohol that is allowed in the blood in India is 0.03. The engine won't start if the person's blood alcohol content is higher than this limit, making it impossible for them to drive. Additionally, the system will send an alert, possibly through a Raspberry Pi, to a friend or relative, providing the exact location using GPS coordinates. This notification serves as a precautionary measure to keep others informed about the situation. Implementing such a system can contribute significantly to reducing accidents related to alcohol consumption. In conclusion, detectors like the one proposed in this project hold immense significance in preventing the loss of life and property caused by reckless driving under the influence of alcohol.

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Ultra-Low Energy 5G IoT Devices with Updatable AI Models: A Dynamic AI-IoT Perspectives

Ultra-Low Energy 5G IoT Devices with Updatable AI Models: A ...

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Communications and information Technologies (GCCIT), Global Conference on

作者： Dharmesh Dhabliya K. Yuvaraj Rahul Chauhan Dikshit Sharma Kala Priyadarshini G Shankar Prasad S Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Ultra-Low-Power (ULP) Internet of Things (IoT) devices are becoming more important in the fast developing 5G and possible 6G networks, and this study tackles the problem of incorporating dynamic AI capabilities into these devices. We present the Dynamic AI-IoT design, a new framework that aims to do away with the tedium of firmware upgrades. This design makes use of Narrowband IoT (NB-IoT) to make cloud interactions run smoothly and includes specialized firmware modifications to make dynamic interactions with TinyML models possible. Many sectors rely on deep learning that is based on massive amounts of high-quality data. The information accumulation of the framework relies on real-time acquisitions, which makes it difficult to directly implement deep learning in a real-time system. But such systems' analytical activities can't be done by collecting data for a lengthy period since they need to be executed in real time. This research presents a novel progressive deep-learning architecture and performs experiments on picture identification to address the challenges of high-quality data collection, high-timeliness data processing, and the difficulties of directly integrating deep-learning algorithms in real-time systems. The experimental findings demonstrate the efficacy, performance, and ability to get a conclusion comparable to the deep-learning architecture using large-scale data. An advanced method for managing memory is presented for the effective handling of AI tasks; it is based on memory alignment and the dynamic resolution of AI activities. Through the use of ULP IoT gadgets on a 5G testbed, empirical investigations show that the implementation of a Dynamic AI-IoT network is feasible. With an average inference duration of around 46 ms and model updates averaging less than one second, the findings are impressive.

关键词： Deep learning 5G mobile communication Heuristic algorithms Tiny machine learning Data collection Real-time systems Internet of Things Artificial intelligence Microprogramming Narrowband

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A Method for the Identification of Multiple Labels in Social Media Text using CNN Approach

A Method for the Identification of Multiple Labels in Social...

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Communications and information Technologies (GCCIT), Global Conference on

作者： Dharmesh Dhabliya Vikrant Sharma N. Thangarasu Prakriti Kapoor V. Mahalakshmi Sampangi Rama Reddy B.R Department of Information Technology Vishwakarma Institute of Technology Pune India Computer Science and Engineering Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

People are able to communicate their thoughts and perspectives with one another through various online settings, including social networks, news websites, and blogs. Because so many individuals instantaneously post their thoughts on the internet, the number of submissions that have been submitted has increased dramatically. It is critical for establishments and organizations to do an effective and speedy analysis of the massive amounts of data at their disposal in order to provide summaries that discuss the perspectives or sentiments of individuals. In this article, we offer a scalable framework that makes sentiment classification by assessing the compound probability scores of the most generally used methods in sentiment analysis using a fuzzy inference mechanism in a CNN-style way. This evaluation is carried out in order to make the framework applicable to a broad range of situations. The valance of an individual's attitude may be estimated by the designed fuzzy inference system by calculating the compound scores of the valance aware dictionary, word embedding, and count vectorization procedures. The suggested technique differs from the convolutional neural networks (CNNs) in that it permits weighting of base learners and combines the strengths of each algorithm using fuzzy rules. Another distinction is that the proposed method is more general. The process of estimating sentiment based on textual data can be handled as either a two-class (positive and negative) or a three-class (positive, neutral, and negative) problem. The former has less challenges than the latter. We carried out the experimental study on four different tagged social network data sets, and discovered that the suggested strategy delivers gains in accuracy for both 2-class and 3-class classifications.

关键词： Fuzzy logic Training Sentiment analysis Social networking (online) Standards organizations Organizations Media information and communication technology Convolutional neural networks Compounds

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Procedia computer Science 2025年 258卷 3580-3589页

作者： Haewon Byeon Pramod Kumar Ihtiram Raza Khan Faisal Yousef Alghayadh Maher Ali Rusho Mukesh Soni Inje University Medical Big Data Research Center Gimhae 50834 Republic of Korea Associate professor Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Kablana Bhadana Haryana 124104 Computer science department Jamia Hamdard Delhi Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method’s capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design.

关键词： Multi-attribute decision-making Bounded rationality Strategy manipulation unsupervised Single-Valued Neutrosophic Sets Cloud model clustering

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AI-Based Cybersecurity Frameworks for 7G-Enabled Virtual Therapy Platforms

Cyber Security and Applications

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Cyber Security and Applications 2025年

作者： A. Jayanthiladevi Jayapandian Natarajan KP Arjun L. Godlin Atlas M. Arvindhan Daniel Arockiam Professor Department of Computer Engineering Marwadi University Rajkot Gujarat India Associate Professor Department of Computer Science and Engineering School of Engineering and Technology Christ University Kengeri Campus Bangalore Karnataka India Department of Computer Science and Engineering GITAM University Bengaluru Campus Karnataka India Associate Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu Assistant Professor Amity School of Engineering & Technology Computer Science and Engineering Gwalior Madhya Pradesh India Associate Professor CSE Amity University Madhya Pradesh Gwalior 474005 India

The modern technology network plays an important role. Artificial intelligence to provide a higher solution for network security. As 7G networks emerge, virtual therapy platforms have begun to take center stage, offering fast, real-time interaction and high-definition immersive experiences. While the convergence of security in health care and mobility is in many ways even more integrated into our lives, it is fraught with cybersecurity risks. This article proposes an AI-based cybersecurity framework for a 7 G-based virtual therapy platform. The framework employs advanced machine learning (ML) algorithms, predictive analytics, and adaptive threat intelligence to protect the confidentiality, integrity, and availability of data. The article covers the core building blocks like anomaly detection, secure 7 G-based virtual therapy platform authentication protocols, and privacy-preserving techniques. AI-GN gives an overview of attack mitigation using AI with a comparative analysis of existing 5G/6G frameworks and solutions and their unique aspects needed for 7G environments. This article mainly focuses on threat detection using a deep learning method, also it aims to provide a continuous authentication process. The proposed model response time is decreased by nearly 30 percent, at the same time detection rate is increased to 98.5 percent in a 7G network.

关键词： AI-based cyber security framework Adaptive threat intelligence Anomaly detection Predictive analytics Threat detection

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Toward a Secure Wireless Sensor Network in Intrusion Detection System Utilizing Central-Smoothing Hypergraph Neural Network Optimized With Clouded Leopard Optimization Algorithm

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International Journal of Communication Systems 2025年第11期38卷

作者： Geerthik S Ramachandran A Jegan J Ishwarya M. V Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence & Data Science Saveetha Engineering College Thandalam Chennai India Assistant Professor Department of Computer Science and Engineering School of Computing SRM Institute Of Science And Technology Tiruchirappalli (Deemed To Be University) Trichy Tamilnadu India Associate Professor Department of Artificial Intelligence & Data Science Agni College of Technology Chennai India

Wireless sensor networks (WSNs) is the essential component of wireless technology that provides effective solutions for various monitoring applications. WSN is vulnerable to several security risks as intrusions, attacks, and suspicious activities. Therefore, this paper proposes a Secure WSN in intrusion detection system using Central-Smoothing Hypergraph Neural Network Optimized with Clouded Leopard Optimization Algorithm (WSN-IDS-CSHGNN-CLOA). Here, the input data is taken from WSN-DS database. The gathered data is pre-processed by regularized bias-aware ensemble Kalman filter (RBAEKF) for data cleaning and normalization. The pre-processed data is given into feature selection using Memetic Salp Swarm Optimization Algorithm (MSSOA) to select optimal features. The selected features are given into CSHGNN for classifying the IDS as denial of service (DoS), black hole, gray hole, flooding, and scheduling attacks in WSN. The CLOA is implemented to optimize the hyperparameters of CSHGNN. The performance of the proposed WSN-IDS-CSHGNN-CLOA approach attains 24.39%, 35.71%, and 25.55% higher accuracy; 24.44%, 34.28%, and 14.44% higher precision when compared to the existing techniques.

关键词： Central-Smoothing Hypergraph Neural Network Clouded Leopard Optimization Algorithm Memetic Salp Swarm Optimization Algorithm regularized bias-aware ensemble Kalman filter wireless sensor network

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