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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Jegadeesan, R. Srinivas, Dava Deivakani, M. Joshi, Sunita Dinesh, P.M. Kumar, M. Jogendra Jyothishmathi Institute of Technology And Science Department of Computer Science And Engineering Telangana Karimnagar505481 India Psna College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Dindigul624622 India Manav Rachna International Institute of Research And Studies Department of Faculty of Computer Applications Haryana Faridabad121004 India Sona College of Technology Department of Electronics And Communication Engineering Tamil Nadu Salem636005 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350365092

Fifth Era Innovation (5G) is the late portable network in wireless communications. This article presents an assessment of the advancements in portable communications. The different issues that arise at each stage are reported in our guide to state-of-the-art wearable networks. The fifth era (5G) of portable networks offers everybody admittance to high-speed web at whatever point they need, any place they are. Since to its inventive qualities, such connecting individuals and controlling contraptions, machines, and items, 5G is somewhat unique. Various levels of execution and capacities presented by the 5G versatile innovation will make new client encounters and connection new organizations. It is pivotal to comprehend where the business can utilize5G's benefits. This work examines and models a microstrip fix antenna working at 29 GHz for impending 5G communication innovation. The result of this reproduction is better than past work that was finished. It can then be used as a significant source of strength for 5G wireless technologies. The ongoing logic diary demonstrates that the proposed antenna performs better than those currently in use. This antenna may therefore be adequate for his 5G wireless connection architecture. © 2024 IEEE.

关键词： 5G mobile communication systems

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Exploring the Potential of Secure Shell (SSH) as a Wireless Network Security Tool 15

Exploring the Potential of Secure Shell (SSH) as a Wireless ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mishra, Abhinav Sahoo, Girija Shankar Sekaran, Ramesh Umamaheswari, B. Futane, Pravin R. Choudhary, Sanjay Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi University of Information Technology Maharishi School of Engineering and Technology UttarPradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

Comfy Shell is an open-source protocol used for many years to comfy community logins and communication over the net. It is primarily based on public key cryptography and is well known for being one of the maximum cozy strategies of authentication and communication. It is now being explored as a Wi-Fi network security device for defensive information transmitted over WiFi networks, including those typically determined in cafes, airports, and other public locations. Its safety capabilities, which include robust authentication and encryption, make it an excellent desire to securely access assets, shift information, and connect to far-off structures. Moreover, it can be used as a digital private network (VPN) to safely establish connections between two or extra computers in a public community. At the same time as SSH is properly-appropriate for relaxed far-off connections, it can be configured to provide additional ranges of authentication and encryption to ensure the WiFi network's safety from malicious assaults. This paper will explore the capability of SSH as a Wi-Fi network safety tool, discuss diverse implementations, and focus on the particular components of using SSH as a security device for WiFi networks. © 2024 IEEE.

关键词： Wi-Fi

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement 3

Machine Learning in Higher Education using RF and SVM for Pe...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Anand, Taruna Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Ambala India University Department of Humanities and Social Sciences Dehradun India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions. © 2025 IEEE.

关键词： Teaching

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DDA: Distillation-Driven Acceleration of the Reverse Diffusion Process for Stochastic Multi-Ship Trajectory Prediction

DDA: Distillation-Driven Acceleration of the Reverse Diffusi...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Kun Han, Qilong Yao, Jingzheng Wu, Changmao Zhang, Yuntao College of Computer Science and Technology Harbin Engineering University Harbin China Yantai Research Institute Harbin Engineering University Yantai China Institute of Software Chinese Academy of Sciences Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350368741

Modeling stochastic multi-ship trajectories is vital for maritime safety and interaction efficiency. Recent researches show that diffusion models excel in trajectory prediction, surpassing GANs and VAEs in generation quality, diversity and stability. However, their slow sampling speed remains a major limitation, as producing high-quality trajectories typically requires hundreds of denoising steps. We introduce DDA, a novel method that accelerates multi-ship trajectory generation by distilling the reverse diffusion process, progressively reducing sampling steps by half while minimizing quality loss. We use CVAE-based encoder to map multimodal inputs into state embeddings in the latent space, and use distillation diffusion in the latent space to more quickly and better represent multi-ship trajectories. The diffusion model uses Transformer-based core, and we incorporate SO(2) invariance and equivariance to enhance model representation. Validation on real-world AIS datasets shows that the student model retains high-quality trajectory generation while sampling speed is approximately 30 times faster. © 2025 IEEE.

关键词： Accelerating Sampling AIS Diffusion Model Progressive Distillation VAE

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Diabetes Prediction Using Machine Learning Algorithms and Hyperparameter Tuning for Expecting Mothers

Diabetes Prediction Using Machine Learning Algorithms and Hy...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Kumar, D. Manoj Deny, J. Nagaraj, P. Dhanveer, Kudithipudi Kumar, Vaddepally Punith Arthi, R. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar Krishnankoil India

ISBN: (纸本)9798350310061

Diabetes is a chronic disease that occurs when the blood glucose levels in the human body exceed the normal range. If left untreated, diabetes can lead to various health complications. To prevent such complications, early detection and management of diabetes are crucial. In this study, a novel system is proposed that uses hyperparameter tuning in addition to various machine learning algorithms for early detection of diabetes. This article also tunes the hyperparameters of the algorithms to optimize their performance. The results indicate that the Support Vector Machine algorithm provides the best fit for predicting diabetes. Hyperparameter tuning further improves the performance of this algorithm. Our approach can be used as a decision support tool for healthcare professionals to identify high-risk patients early on and provide appropriate care and management. Our study highlights the potential of machine learning algorithms and hyperparameter tuning for developing accurate and reliable predictive models for diabetes in expecting mothers. These models can aid healthcare professionals in taking preventive measures to improve maternal and child health outcomes. © 2023 IEEE.

关键词： Support vector machines

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A Time Series Analysis-Based Forest Disturbance Time Backtracking Method in Northeastern China, the Changbai Mountain 7

A Time Series Analysis-Based Forest Disturbance Time Backtra...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Yan, Yuxiao Zhang, Changsheng Fang, Lei Zhang, Bin Dong, Xingjun Guo, Ying College of Software Northeastern University Shenyang110819 China Environment Research Institute Shandong University Qingdao266237 China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

ISBN: (纸本)9798350350890

Forest disturbance is a crucial factor in evaluating carbon sink changes in ecological studies. Current research on forest disturbances mainly focuses on forest disturbance detection, forest disturbance classification, and other analyses and statistics of forest changes methods. These studies are mostly based on time-series images or forest remote sensing features to achieve dynamic monitoring of forest changes. However, it is often impossible to construct disturbance information with a lack of images from early remote sensing. This paper addresses this limitation by proposes a method of forest disturbance time backtracking which can get historical information on forest disturbance over a wider temporal distribution. Using this method, we ultimately achieved forest disturbance detection on early remote sensing images from the 1960s. This method is based on machine learning and deep learning algorithms to construct two forest disturbance datasets to achieve this goal. The first dataset, for categorizing forest disturbances, is the first of its kind in Northeast China. This data can be widely used in similar studies. The second dataset is the forest disturbance recovery year dataset. We estimate the timing of early forest disturbance by classifying this dataset. The experimental results showed that the first dataset has an accuracy of over 90%, and the backtracking historical disturbance results are consistent with the actual historical information. This method is significant in its ability to fill in gaps in the forest disturbance classification present in Northeast China and enhance the confidence and time span of the forest disturbance detection method. © 2024 IEEE.

关键词： Forest ecology

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Blockchain-Enabled Federated Learning Systems with Explainable AI: A Review 2

Blockchain-Enabled Federated Learning Systems with Explainab...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Potdukhe, Nayan Gourshettiwar, Palash Zade, Sujal Waghale, Atharva Datta Meghe Institute of Higher Education and Research Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Meghe Computer Science and Medical Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The convergence of Blockchain with Federated Learning (FL) technology and Explainable Artificial Intelligence (XAI) serves as a new framework to solve privacy protection problems, security issues, and interpretability needs. Model training collaborations supported by FL technology allow different entities to develop models while safeguarding raw data from exposure during training processes, thus addressing privacy needs in healthcare and finance. The combined challenges of model vulnerability to malicious attacks, data poisoning vulnerabilities, and explainability issues block broad industrial adoption. Blockchains strengthen FL security systems by providing decentralised solutions that produce unchangeable audit trails through their proof of transparency, immutability, and distributed consensus models. By enabling model interpretability, XAI methods build stakeholder trust and ensure system accountability. The study combines the technologies to analyse current systems and identify gaps that integrated systems should resolve. A detailed review examines explainable blockchain models and privacy preservation methods to tackle performance and implementation limitations within the framework. Combining these technologies builds a transformative process to create secure AI systems that maintain interpretability functions and protect data privacy during system development as researchers develop frameworks for wider application deployment. © 2025 IEEE.

关键词： Federated learning

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A Robust and Efficient Computational Offloading and Task Scheduling Model in Mobile Cloud Computing

A Robust and Efficient Computational Offloading and Task Sch...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Chandra, I. Karthikeyan, K.V. Revathi, V. Shankar, K. Tamilselvi, M. Arunkumar, J.R. Department of Computer and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of Information Technology Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science and Engineering Easwari Engineering College Chennai India Department of Electronics and Communication Engineering Srm Institute of Technology Ramapuram Campus Chennai India Department of Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India

ISBN: (纸本)9798350334357

Mobile cloud computing (MCC) has the potential to greatly enhance the computational capabilities of smart mobile devices (SMDs) and reduce their energy consumption by offloading compute-intensive workloads from SMDs onto the resource-rich cloud. Energy-efficient compute offloading remains a problem, especially when applications are under strict time constraints. In response to this issue, this research proposes an Efficient Computational Offloading and Task Scheduling model (ECOTS) strategy to cut down on power use and minimize the time it takes to finish an application. First, we formalize the ECOTS issue as a reduction of energy consumption under the constraints of a task-dependency criterion and an absolute time limit. To solve the issue of offloading computing from mobile devices, we present it as an optimization problem. We then offer two efficient offloading strategies to address the issue. Numerical experiments are used to determine how effective the suggested algorithms are. Finally, we demonstrate using data from a working testbed that the ECOTS method can effectively reduce energy expenditures by properly adjusting the CPU clock speed of SMDs in remote computation and by improving the transmission energy for wireless range circumstances in the cloud. © 2023 IEEE.

关键词： Mobile cloud computing

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di Huazhong Agricultural University Key Laboratory of Smart Farming for Agricultural Animals Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education College of Informatics Wuhan430072 China King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Hohai University College of Computer and Information Nanjing210098 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518115 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

With the rapid expansion of interactions across various domains such as social networks, transaction networks, and IP-IP networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises—such as spurious and missing nodes and edges—struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further reconstructing the Kalman filter to optimize these entropies. Extensive experiments have proved that our proposed EPM outperforms state-of-the-art methods in discovering anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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