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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Gupta, Rohit Kumar Mazumdar, Arka Prokash Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9798331531935

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache. © 2025 IEEE.

关键词： Cache memory

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Email Validator Dashboard: A Comprehensive Tool for Accurate Email Validation

Email Validator Dashboard: A Comprehensive Tool for Accurate...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chand, Syed Riyyan Ahmed, Uzair Ismail, T Mohammed Kumar, B.P. Pradeep Chand, Syed Bilal Chaitra, P. Design Atria Institute of Technology Dept. of Computer Science Bangalore India Atria Institute of Technology Dept. of Computer Science Engineering Bangalore India HostUp Cloud Technologies Private Limited Bangalore India

ISBN: (纸本)9798331538538

Email authentication is of the utmost importance in maintaining the reliability and quality of email communication, specifically in database management and bulk email marketing. The centerpiece of the project is the development of an Email Validator Dashboard, which is a very user-friendly functionality filled with other useful parts that guarantee the accurate verification of email addresses. The dashboard really presents valuable inputs quickly in terms of deliverability, risk factors, and domain reliability due to its dynamic visualization along with real-time email validation procedures. The project's structure is such that the users get real-time data in the form of interactive charts and graphs, which make them see the transformations occurring as validation is going on, thus distinguishing it from the traditional tools. It comes with cutting-edge back-end data integrity validation processes, such as mailbox verification, domain DNS checks, and syntax verification, alongside powerful analytics. This project is really useful for organizations and developers who work with large email lists and at the same time can ensure high deliverability and compliance with data standards. © 2025 IEEE.

关键词： Marketing

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Hybrid Quantum Approximate Optimization Algorithm (HQAOA) for Efficient Blockchain Transaction Scheduling 3

Hybrid Quantum Approximate Optimization Algorithm (HQAOA) fo...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Paul, Soumyadip Chakraborty, Sarit Government College of Engg. & Ceramic Technology Dept. Of Information Technology WB Kolkata700010 India Government College of Engineering and Leather Technology Dept. of Computer Science and Engineering WB Kolkata700106 India

ISBN: (纸本)9798331523893

Quantum technology offers a transformative approach to solving complex computational challenges in decentralized systems, particularly in blockchain transaction scheduling. Efficient transaction scheduling is critical in distributed ledger systems, which often face challenges like dynamic transaction loads. This work proposes a novel approach namely Hybrid Quantum Approximate Optimization Algorithm (HQAOA) which combines Multi-Agent QAOA (MA-QAOA) and Adaptive Layer QAOA (AL-QAOA) to provide a robust solution to such issues. HQAOA leverages a decentralized optimization framework where multiple agents, each representing a node in the network, optimize local transaction scheduling while considering global constraints. Additionally, HQAOA adapts to varying transaction loads by dynamically adjusting the number of quantum layers for each agent, ensuring computational efficiency under different conditions. Such dynamic layer adjustment mechanisms mitigate the adverse effects of quantum noise and ensures optimal performance in scenarios with fluctuating transaction loads. In contrast to traditional QAOA, which struggles with noise and scaling issues, HQAOA provides improved performance by balancing the complexity of the problem with available quantum resources. Experimental results signify the future potential of HQAOA to outperform QAOA, particularly in noisy environments and irregular transaction conditions. This work demonstrates the potential of HQAOA to optimize blockchain transaction scheduling in decentralized systems. © 2025 IEEE.

关键词： Qubits

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Optimizing Skin Cancer Detection Through Hybrid Convolutional and Ensemble Learning Methods

Optimizing Skin Cancer Detection Through Hybrid Convolutiona...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Sahithi, Chalamala Teja Basha Shaik, Arif Pandala, Madhavi Latha Dept. of Information Technology Vijayawada India Vit Bhopal School of Computing Science and Engineering Kothrikalan India

ISBN: (纸本)9798331529833

One of the most prevalent types of cancer is skin cancer and millions of people suffer from it across the globe with melanoma being the most dangerous type if not treated at an early stage. Accurate and timely diagnosis is crucial in the management of skin cancer but traditional diagnosis methods like dermoscopy, is largely dependent on the skills of qualified dermatologists which makes it subjective and susceptible to variation among observers. In this sense, deep learning and machine learning approaches have a lot of potential in automating skin cancer classification and enhancing its accuracy for better early intervention and patient outcomes. This paper presents the analysis of the performance of several models, i.e., CNN + Random Forest, DenseNet201, MobileNetV2 and ConvoMixer + CatBoost on HAM10000, ISIC and PAD-UFES-20 datasets. The experimental results indicate that DenseNet201 achieved an accuracy of 91.8%, MobileNetV2 reached 87%, and the combination of ConvoMixer and CatBoost obtained 66% However, the CNN + RF model outperformed all of the other models achieving 96% accuracy revealing the effectiveness of hybrid approaches for skin cancer classification on a reliable and scalable manner. Future work could include investigating transformer-based models, such as Vision Transformers (Suns et al.) and Swin Transformers (Liao et al.), which are able to allow better global context within the data. Also federated learning may be used to enable the use of multi institutional databases without compromising the privacy of patients. In addition, self supervised learning approaches could also help alleviate the reliance in a substantial amount of annotated datasets thereby increasing accuracy and robustness in practical and clinical situations. © 2025 IEEE.

关键词： Federated learning

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Self Organized Anchor Group based Routing Protocol for IoT Initial Setup 17

Self Organized Anchor Group based Routing Protocol for IoT I...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Gupta, Naveen Kumar Aggarwal, Apeksha Yadav, Rama Shankar Kumar Nagaria, Rajendra Dr B R Ambedkar National Institute of Technology Jalandhar Center of Energy and Environment Dept. of Information Technology Jalandhar India Jaypee Institute of Information Technology Dept. of CSE and IT Noida India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj India Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Comm. Engineering Prayagraj India

ISBN: (纸本)9798331531195

Geographical routing approach relies on local geographic information and performs multi-hop routing with the help of local decisions. The nodes are represented by 3D coordinates as some real IoT networks may be deployed in high buildings, hilly areas, underground or underwater. The geographical routing protocols follow the greedy path and suffer from void node problem (VNP). The traditional recovery policies follow a longer path around the boundary nodes of the void region which may go out of energy quickly. Thus, this paper proposes the concept of anchor group and presents an efficient anchor group routing protocol (EAGR) for IoT networks. The anchor group is treated as a sub-destination region where a group of nodes behaves like sub-destinations, which bypass the boundary nodes of the void to minimize the path length and divide the network traffic, in case of void in the network. The paper is also focused on considering the residual energy in the contention-based next-hop selection process. The experimental work has been carried out in the OMNET++ simulator's INET framework and observed that the proposed method is outperforming existing protocols. Moreover, it has reported a longer network lifetime and is able to survive longer than other existing geographical routing protocols on high load. © 2025 IEEE.

关键词： Routing protocols

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Cybersecurity for Industrial IoT: A Review on Vulnerabilities and Attacks 8

Cybersecurity for Industrial IoT: A Review on Vulnerabilitie...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Kumari, Reetu Kumar, Sunil Amandeep Dutta, Kamlesh Kumari, Aparna Guru Jambheshwar University of Science and Technology Dept. of Computer Science & Engineering Haryana Hisar India Guru Jambheshwar University of Science and Technology Dept. of AI & Data Science Haryana Hisar India National Institute of Technology Hamirpur Dept. of Computer Science & Engineering Himachal Pardesh India Institute of Technology Nirma University Dept. of Computer Science & Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798331520762

Internet has evolved from a network of connecting people to a network of connecting things, leading to a more complex and sophisticated network of Industrial Things, known as Industrial IoT (IIoT) today. This evolution has revolutionized the traditional Industrial system into real-time smarter systems that enhanced overall efficiency and productivity. Despite its benefits, the interconnected nature of IIoT, expanded the attack surface, exposing the systems to new crucial and complex cybersecurity threats. Several real time attacks on various industries as mentioned in this paper and the increasing cybersecurity cost over the years, clearly states the need of robust security solutions. To deeply understand the cybersecurity aspect, we provided a comprehensive study of attacks and vulnerabilities in this paper. In this paper, we did a indept. study of various IIoT related severe attacks and aspects related to these attacks, like vulnerabilities, impacts, potential solutions etc. We also provided future research directions to help the research community, to work upon and develop more robust and efficient cybersecurity solutions. © 2025 IEEE.

关键词： attacks cybersecurity etc IIoT Industrial IoT IoT vulnerabilities

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

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4th International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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Silicon nitride photonic beam splitters for O-band power and polarization management 23

Silicon nitride photonic beam splitters for O-band power and...

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23rd Slovak-Czech-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics

作者： Litvik, Ján Korček, Radovan Benedikovič, Daniel Dept. of Multimedia and Information-Communication Technologies Faculty of Electrical Engineering and Information Technology University of Zilina Univerzitna 1 Zilina01026 Slovakia

ISBN: (数字)9781510688094

ISBN: (纸本)9781510688087

Silicon nitride waveguides play a leading role in photonic system integration. Silicon nitride leverages unique low-loss passive functions, mature deposition techniques of low-pressure chemical vapor deposition and plasma-enhanced chemical vapor deposition, and compatibility with complementary metal-oxide-semiconductor fabrication. So far, a wide variety of photonic devices have been demonstrated in Silicon nitride platforms, including ultra-low-loss waveguides, filters, multiplexers, and grating or edge couplers, to name a few. Among these elements, beam splitters based on multimode interference couplers are key building blocks for complex chip-scale photonic systems, including coherent communications or advanced optical networks. This work is focused on O-band (spectral range between 1260 nm and 1360 nm) multimode interference beam splitters on silicon nitride platform for power and polarization on-chip management. It is used three-dimensional eigenmode expansion and finite difference time domain solvers, respectively. We proposed a 1x2 power and polarization splitters with a compact footprint of 2.6 μm and 5.55 μm and insertion loss as low as 0.3 dB across 400 nm wavelength range. Also, we designed multimode interference power splitters 1:4 and 1:8 with insertion loss as low as 0.5 dB with bandwidth 240 nm and 60 nm, respectively. These results are promising for the development of scalable and high-performance chip-integrated components in power-hungry and polarization-sensitive applications. For effective designing of polarization splitter based on multimode interference it is necessary using apodization techniques to avoid large footprint and very low bandwidth. © 2025 SPIE.

关键词： Silicon nitride

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Model Optimization for Personalized Health Metrics Analysis 5

Model Optimization for Personalized Health Metrics Analysis

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Perera, Madhuranga Wijesiriwardena, Amasha Pathirana, Amandi Gamaathige, Lakshan Wijesiri, Pipuni Jayakody, Anuradha Sri Lanka Institute of Information Technology Dept of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331530983

This paper investigates the development and application of four machine learning models designed to enhance personalized health management, specifically targeting young adults aged 15-30. The research addresses common health challenges, such as obesity and lifestyle-induced diseases, through data-driven methodologies that provide personalized meal plans, workout recommendations, and progress monitoring. The first model generates optimized personalized recommendations according to the user's health condition using Random Forest and Decision Tree algorithms. The second model utilizes an ensemble of Random Forest, LightGBM, and XGBoost, combined through a stacking technique with Linear Regression as the meta-model, to generate optimized personalized meal plans according to health condition. The third model generates optimized workout plans using Gradient Boosting and XGBoost classifiers, accounting for individual fitness objectives, body compositions, and medical conditions. A fourth model predicts goal achievement timelines by analyzing features such as caloric balance and hydration efficiency, providing users with actionable feedback using XGBoost. The integration of these AI-driven components into a scalable digital platform demonstrates the potential of machine learning in transforming health management. Future enhancements include improving model accuracy, enabling real-time feedback, and deploying the system as an accessible mobile application. © 2025 IEEE.

关键词： Nutrition

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