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Disruptive Technologies (ICDT), International Conference on

作者： Rajakumari S Kantharaju HC Shadab Ahmad Rupa Rani Sharma S. Sudha T Ramesh Department of Mathematics R.M.D. Engineering College Department of AI & ML Vemana Institute of Technology Bengaluru Karnataka India Department of Electrical and Computer Engineering Samara University Afar region Ethiopia Department of Applied Science G.L Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Department of ECE Sri Ranganathar Institute of Engineering and Technology Coimbatore Tamilnadu India Department Of Computer Science and Engineering R.M.K Engineering College

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly.

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Smart Emergency Management System Using Raspberry Pi Pico, GPS and GSM Modules

Smart Emergency Management System Using Raspberry Pi Pico, G...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Govinda Rajulu. G L. Sharmila D. Venkatesan E. Soumya Sargu Chandra Prakash Ms. P. Priyanka Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India Deptarment of CSE St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India Department of CSE(AI&ML) St. Martin's Engineeringollege Dhulapally Secunderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This paper proposes a GSM-based children's rescue system designed for earthquake emergencies, emphasizing cost-effectiveness and simplicity while ensuring rapid child rescue. The system operates in three key stages: data acquisition, classification, and alert notification. Sensors collect temperature, pH, and heart rate data, which are then digitized and classified to assess the child's condition. If abnormal values are detected, an alert notification is sent via GSM to parents and rescue teams, providing real-time location data through GPS. The hardware implementation, based on Raspberry Pi, converts analog sensor signals into digital data for processing. The system's efficacy is validated through various scenarios, demonstrating its ability to detect critical conditions and promptly trigger alerts, enhancing child safety during disasters.

关键词： Temperature sensors GSM Chemical sensors Earthquakes Sensor systems Real-time systems Biomedical monitoring Monitoring Intelligent sensors Global Positioning System

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Integration of MCC to detect disease in plants with DL Health Industry

Integration of MCC to detect disease in plants with DL Healt...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Anubhav Bewerwal Mohammed I. Habelalmateen H. A. Alsailawi D. Venkatesan Dharmesh Dhabli Qasim Abdul Jabbar Nabaa M. Bader Ali Saad Alwan Department of Computer Science and Engineering Graphic Era Hill University Bhimtal Uttarakhand India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Chemistry and Biochemistry College of Medicine University of Kerbala Kerbala Iraq Dept.of AI&ML St.Martin’s Engineering College Secundrabad Vishwakarma Institute of Information Technology Pune India Medical Instrument Department Alkunooze University College Basra Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Business Administration Department Al kut Kut University College wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Mobile gadgets are progressively assuming a vital role in individuals’ everyday routines as they streamline several practical activities. Mobile cloud computing overcomes the constraints of memory, CPU, and battery life, and enhances its functionalities by integrating mobile and cloud computing. Big data analytics solutions facilitate the extraction of value from data by considering four key factors: volume, diversity, velocity, and validity. The study explores the use of mobile cloud computing and big data analytics in the context of networked healthcare. The focus is on the justification and development of interconnected healthcare applications and systems, highlighting the rise of cloud computing in this domain. The paper presents a cloudlet-based mobile cloud computing architecture designed specifically for big data applications in the healthcare sector. In the proposed framework the study assess two types of models that is cloud-based model and Wi-fi Cloudlet-based model, the working mechanism model describe the differentiation in processing. Furthermore, this study evaluates the results obtained and compares both mechanism based on latency in processing.

关键词： Industries Cloud computing Computational modeling Memory management Medical services Big Data applications Batteries

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Empowering Consumer Healthcare Through Sensor-Rich Devices using Federated Learning for Secure Resource Recommendation

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IEEE Transactions on Consumer Electronics 2025年

作者： Fathima, Afifa Salsabil Basha, Syed Muzamil Ahmed, Syed Thouheed Khan, Surbhi Bhatia Asiri, Fatima Basheer, Shakila Shukla, Madhu School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Data Science School of Science Engineering and Environment University of Salford Manchester United Kingdom Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India College of computer science Informatics and computer systems department King Khalid University Saudi Arabia Department of Information Systems College of computer and Information science Princess Nourah bint Abdulrahman University P.O. BOX 84428 Riyadh11671 Saudi Arabia the Department of CSE - AI ML & DS Marwadi University Gujarat Rajkot360003 India

When implementing zero-trust edge computing, offloading computational tasks and data access through traditional model training and usage approaches can lead to increased latency. Since the traditional methods often involve extensive communication with a central server, creating additional network hopping stations/nodes resulting in increased latency. The challenge is bound to allocate a befitting resource at a given consumer demand. In this proposed system, a federated learning model based data offloading and consumer medical resource recommendation of IoT is discussed and validated. The user/consumer group and local training models are aligned with edge servers for data preprocessing and customization with a series of resources demand creation and coordination. The consumer resource allocating priorities are fine-grained with the proposed blockchain based priority analyzer for recommendation and allocation. The computational parameter such as resource pool, average waiting time, energy consumption and transmission trust delays are observed and validated. The proposed framework fetches consumer resources logs and synchronizes the centralized training model for effective scheduling and allocation of resources with an accuracy of 94.92% under the 5G operating spectrum. The technique has demonstrated minimal latency in offloading the data request demand and resource allocation at the cloud servers. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Federated learning

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Predicting Malware Classification and Family using Machine Learning: A Cuckoo Environment Approach with Automated Feature Selection

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Procedia computer science 2024年 235卷 2434-2451页

作者： Niveditha S Prianka Rr Sathya K Shreyanth S Nandhagopal Subramani Balakrishnan Deivasigamani Karthikeyan S Department of Biotechnology Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India 602105 Senior Assistant Professor Department of AIML New Horizon College of Engineering Bengaluru Karnataka India 560103 Assistant Professor senior grade-1 School of Computer Science and Engineering VIT Vellore Tamilnadu India 632014 MTech in Data Science and Engineering Birla Institute of Technology and Science Pilani Rajasthan India 333031 Assistant Professor Department of CSE(AI and ML) KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India 641407 Assistant Professor Department of Information Technology KSR Institute for Engineering and Technology

Malware’s increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model’s performance.

关键词： Malware Classification Malware Family Prediction Machine Learning (ml) Random Forest Classifier (RFC) Cuckoo Environment Cybersecurity k-Nearest Neighbors (KNN) Boruta Algorithm Automated Feature Selection

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Genetic Signal Processing for Categorizing Genomic Data using Convolutional Neural Networks

Genetic Signal Processing for Categorizing Genomic Data usin...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Padmaja Pulicherla Srinivas Aluvala Munimanda Premchander Poranki V L R N Sai Sudha Revanasiddappa Department of CSE Institute of Technology and Management Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Breakthroughs in high-throughput sequencing technology have resulted in exponential growth in acquired genetic data. Classic machine learning systems have difficulties in dealing with complex genetic data, leading to investigation of deep learning methods. A unique technique for categorizing genomic data using convolutional neural networks (CNNs) is described. The genetic signal processing approach can extract unique characteristics from genomic data. CNNs' ability to comprehend subtle patterns and correlations in cleaned-up data results in improved classification accuracy. A publicly accessible dataset is used to compare the proposed technique across various criteria. The study of genetic information using digital noise processing technology is known as genetic signal processing (GSP). GSP are used to turn the nucleotide sequence into a line graph, allowing for appropriate pattern classification. From the results, it clearly shows that proposed CNN achieves better results in terms of accuracy (0.96), precision (0.96), sensitivity (0.95), specificity (0.97) and f1-score (0.95).

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Generative Adversarial Networks for 3D Scene Reconstruction

Generative Adversarial Networks for 3D Scene Reconstruction

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International Conference on Computing and Networking Technology (ICCNT)

作者： K. Senthamil Selvan Samaksh Goyal Aravindan M K Omkaresh S. Kulkarni K. Yuvaraj Nitish Vashisht Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai india Quantum University Research Center Quantum University School of Engineering and Technology Mechaical Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune INDIA Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Generative opposed Networks (GANs) are a generative model broadly utilized in device mastering, PC vision, and herbal language processing (NLP). GANs hire neural networks, a generator, and a discriminator that are trained collectively to be able to generate realistic-looking statistics, along with photographs, audio clips, or 3-D scenes. In this painting, we are aware of $3-\mathrm{D}$ scene reconstruction using GAN-based total methods. We aim to generate 3-D scenes from given 2nd pics, allowing us to benefit from insight into the structure and layout of complicated 3-D environments. To this end, we endorse leveraging a method known as inverse rendering to enhance the accuracy of the reconstructed 3-D scenes. We compare our method to the usage of artificial and real-international images, and the effects display the efficacy of our approach. Finally, we talk about capacity destiny guidelines for three-D scene reconstruction and the usage of GAN-based total strategies.

关键词： Three-dimensional displays Shape Neural networks Layout Generative adversarial networks Rendering (computer graphics) Generators Image reconstruction Painting Guidelines

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Wireless Power Transfer Technologies: State-Of-The-Art and Future Prospects

Wireless Power Transfer Technologies: State-Of-The-Art and F...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Vivek Justus S.Viveka M.Sumithra Purushothaman V V.Samuthira Pandi Desidi Narsimha Reddy Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. Recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.

关键词： Costs Reviews Smart cities Magnetic resonance Wireless power transfer Standardization Telecommunication computing Smart grids Internet of Things Tuning

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Unlocking Industry Potential Through the Convergence of ai and Blockchain Technologies

Unlocking Industry Potential Through the Convergence of AI a...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： R. Senthil Kumar Krishna Murthy Sudipta Halder Rajiv Avacharmal Maher Ali Rusho Vandana Rathore Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Centre for Media Studies School of Social Sciences Jahawarlal Nehru University New Delhi Department of Computer Science Atria Institute of Technology Bengaluru Karnataka AI/ML Risk Lead University of Connecticut USA Department: Lockheed Martin Engineering Management University of Colorado Boulder Colorado Faculty of Science and Technology Jagran Lakecity University Bhopal

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

ai and blockchain technology might make many sectors safer, more open, and more efficient. It proposes an autonomous ai-based solution that leverages blockchain and machine learning to address industry issues including theft, data security, and inefficiency. The recommended response utilizes ai to track patterns and estimate what they are. Additionally, blockchain smart contracts ensure data exchanges remain secure and unchangeable. We tested the paradigm in banking, healthcare, and supply chain management. Transaction speed, data quality, fraud reduction, and operational efficiency were the main success factors. The findings showed 32% less theft, 45% better data accuracy and security, and 28% higher company efficiency. Process latency was 20% lower than in centralized systems. The findings suggest that ai with blockchain may simplify decision-making, data security, and operations. It may be an excellent choice for firms looking to profit from the digital shift.

关键词： Industries Adaptation models Data security Data integrity Decision making Smart contracts Machine learning Real-time systems Blockchains Fraud

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Enhancing Fingerprint Security Using CNN for Robust Biometric Authentication and Spoof Detection

Enhancing Fingerprint Security Using CNN for Robust Biometri...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Munish Kumar Ankita Tiwari Shilpa Choudhary Monali Gulhane Bhawna Kaliraman Rashmi Verma Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering (AI & ML) Neil Gogte Institute of Technology Hyderabad India Department of CSE Symbiosis Institute of Technology (SIT) Nagpur Symbiosis International (Deemed University) (SIU) Pune India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

Fake fingerprint recognition needs to pose a significant problem in the quickly developing field of biometric security. The creation of creative and effective spoof detection systems has become crucial as weaknesses in traditional authentication methods have been revealed. To solve this problem, this research employs Convolutional Neural Networks (CNNs) for next-generation biometric security, a ground-breaking approach. The proposed CNN-based fingerprint spoof detection approach utilizes deep learning inherent capabilities to extract complex patterns and information from fingerprint photos. The CNN model trains on a broad dataset containing multiple spoofing attempts to distinguish precisely between authentic and fake fingerprints. There are two significant contributions from this study. First, in order to fully train and assess the CNN model, we provide two datasets covering a wide range of fingerprint spoofing scenarios. Second, by successfully adjusting to the always-changing environment of spoofing tactics, our custom-built architecture outperforms traditional approaches and establishes new standards in detection accuracy. These results highlight CNNs' better capacity to discriminate between authentic and false fingerprints, placing them as a key component of future biometric security systems. This study highlights the value of utilizing cutting-edge technologies, like CNNs, to strengthen biometric security against new threats. By combining cutting-edge deep learning algorithms with the complexities of fingerprint spoof detection, we enter a new age of next-generation biometric security that promises enhanced accuracy and resilience in the face of shifting challenges. Comparing the suggested system to earlier methods, accuracy is 98.99%.

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