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

作者： Sharma, Shubh Pathak, Disha Mohini Jain, Dhyanendra Srivastava, Somya Vats, Prashant Upadhyay, Govind Murari Department of Computer Science ABES Engineering College U.P. Ghaziabad India Department of Computer Science Engineering-AIML ABES Engineering College U.P. Ghaziabad India Dept. of CSE School of CSE Manipal University Jaipur Rajasthan Jaipur India Dept. of Computer Application Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798331530389

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity. © 2025 IEEE.

关键词： Contrastive Learning

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Bridging the gap: combining expert guidance and self-learned knowledge for efficient and robust autonomous navigation in complex settings

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Journal of Ambient Intelligence and Humanized Computing 2025年第2期16卷 425-441页

作者： Kumar, Dharmendra Computer Science and Engineering United College of Engineering and Research Naini Uttar Pradesh Prayagraj211010 India

Autonomous navigation in dynamic environments poses significant challenges due to complex agent–environment interactions, real-time decision-making, and adaptability requirements. We propose the Reinforcement-Imitation Learning for Autonomous Navigation (RILAN) algorithm, which integrates reinforcement learning, imitation learning, and uncertainty estimation for robust and adaptable navigation. RILAN combines the strengths of expert demonstrations and self-learned knowledge, overcoming individual limitations. The modular RILAN architecture comprises an imitation learning module, reinforcement learning module, and an uncertainty-aware policy selector, enabling efficient information processing and dynamic policy selection. Extensive experiments compare RILAN to state-of-the-art algorithms, including E2E-IL, CIL, DQN, and PPO. Results show RILAN outperforms these algorithms in success rate, average time to goal, collision rate, and off-road rate, demonstrating the proposed approach’s efficacy for robust autonomous navigation in applications like self-driving vehicles and robotics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Contrastive Learning

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An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction

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Neural Computing and Applications 2024年第24期36卷 15063-15074页

作者： Bongirwar, Vrushali Mokhade, A.S. Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein which is dependent on its secondary structure. In this paper, we focus on improving the accuracy of protein secondary structure prediction. To do so, a Multi-scale convolutional neural network with a Gated recurrent neural network (MCNN-GRNN) is proposed. The novel amino acid encoding method along with layered convolutional neural network and Gated recurrent neural network blocks helps to retrieve local and global relationships between features, which in turn effectively classify the input protein sequence into 3 and 8 states. We have evaluated our algorithm on CullPDB, CB513, PDB25, CASP10, CASP11, CASP12, CASP13, and CASP14 datasets. We have compared our algorithm with different state-of-the-art algorithms like DCNN-SS, DCRNN, MUFOLD-SS, DLBLS_SS, and CGAN-PSSP. The Q3 accuracy of the proposed algorithm is 82–87% and Q8 accuracy is 69–77% on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recurrent neural networks

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Efficient Heuristic Replication Techniques for High Data Availability in Cloud

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computer Systems science & engineering 2023年第6期45卷 3151-3164页

作者： H.L.Chandrakala R.Loganathan Department of Computer Science and Engineering School of EngineeringPresidency UniversityIndia Department of Computer Science and Engineering HKBK College of EngineeringIndia

Most social networks allow connections amongst many people based on shared *** networks have to offer shared data like videos,photos with minimum latency to the group,which could be challenging as the storage cost has to be minimized and hence entire data replication is not a *** replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response *** simple and adaptive replication strategies exist,the solution is non-deter-ministic;the replicas of the data need to be optimized to the data usability,perfor-mance,and stability of the application *** resolve the non-deterministic issue of replication,metaheuristics are *** this work,Harmony Search and Tabu Search algorithms are used optimizing the replication process.A novel Har-mony-Tabu search is proposed for effective placement and replication of *** on large datasets show the effectiveness of the proposed *** is seen that the bandwidth saving for proposed harmony-Tabu replication per-forms better in the range of 3.57%to 18.18%for varying number of cloud data-centers when compared to simple replication,Tabu replication and Harmony replication algorithm.

关键词： Cloud computing data replication bandwidth saving Tabu search Harmony search hybrid Harmony-Tabu search

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Self-distillation framework for improving fake speech detection in the domain variability scenario

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Neural Computing and Applications 2025年第5期37卷 3111-3127页

作者： Samhita, V. Viju, Vaishnav Bharathi, B. Department of Computer Science Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

Robust fake speech detection systems are crucial in an era where audio recordings can be easily altered or developed due to advancements in technology. The potential impact of this technology could be devastating due to its susceptibility to misuse. It can lead to political and economic instability, severe financial repercussions, the spread of misinformation, defamation, and security breaches in ASV systems and facilitate theft and fraud. Further, existing detection systems face a key issue;most state-of-the-art systems only provide results on the corpus they are trained on but fail in the domain variability scenario. The goal of this study is to enable a system to generalize across various domains, enhancing its reliability in real-world scenarios and thereby solidifying the authenticity of speech, making speech-based systems more dependable. All three audio spoofing scenarios are considered, logical access-based (LA) attacks including TTS and voice conversion (VC) techniques, spoofing attacks generated in real physical space, namely physical access (PA) attacks (replay, mimicry) and advanced deepfake technologies. The proposed system put forth in this study achieves commendable performance on three diverse test datasets. Notably, EER of 0.286, 0.337 and 0.371 is achieved on the In-The-Wild dataset with the proposed system implemented on ResNet, ECANet and SENet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Audio recordings

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Real Estate Registry Platform Through NFT Tokenization Using Blockchain 2

Real Estate Registry Platform Through NFT Tokenization Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Archana Sharma, Anshul Tripathi, Anshul Chaudhary, Aniket Abes Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350371055

The conventional real estate registry system exhibits numerous shortcomings, being both costly and time-consuming. Vulnerabilities to fraud, particularly through impersonation of false property ownership, further undermine its effectiveness. This paper proposes a methodology for accurately identifying the legitimate property owner and ensuring secure ownership transfers. Leveraging the immutable characteristics of Blockchain and Non-Fungible Tokens (NFTs), this approach addresses the deficiencies of traditional property transfer systems. The automation of processes through smart contracts, powered by the Ethereum Blockchain, streamlines the transfer of property ownership. NFTs, uniquely tied to individuals and resistant to replication, eradicate fraud and eliminate challenges associated with verifying the identity of the authentic property owner. Possession of the NFT signifies true property ownership. The integration of NFTs and blockchain not only enhances transparency and security but also accelerates and fortifies the reliability of the traditional real estate transfer process. © 2024 IEEE.

关键词： Blockchain (BC) Ethereum (ETH) Ledger (LED) Non-Fungible Tokens (NFTs) Real Estate Registry (RER) Tokenization(T)

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Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

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computer Modeling in engineering & sciences 2024年第2期138卷 1469-1483页

作者： Laila M.Halman Mohammed J.F.Alenazi Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11451Saudi Arabia

The healthcare sector holds valuable and sensitive *** amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast *** to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and *** this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe *** attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human *** can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or *** this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various *** propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS *** then evaluate the accuracy and performance of the proposed TBDC *** technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

关键词： Network resilience network management attack prediction software defined networking(SDN) distributed denial of service(DDoS) healthcare

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Enhancing Skin Disease Diagnosis: Light GBM-DMS Algorithm for Accurate Image Classification

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Kalpana, B. Senthilselvi, A. Muruganandam, S. Kumar, S. Vinodh Department of Information Technology RMD Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai India

Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin disease is subjective and time-consuming when it is performed by health experts manually. Hence, automatic skin disease prediction is required by both dermatologists and patients to enhance and accelerate treatment plans. Even though many machine-learning techniques are available to detect skin diseases, the timely prediction and accurate classification of skin lesions are still in demand. Hence, the Discrete Mycorrhizal Search Algorithm is used to optimize the novel LightGBM to meet this objective. Firstly, intrusions like noise elimination and hair removal are performed and then the image resizing takes place by utilizing the preprocessing pipelines. Subsequent to data pre-processing, the prime features are extracted using the convolutional self-attention based L2 regularization, thus minimizing the data complexity. At last, the extracted features are shifted to the final categorization phase which is done by the novel approach for effective classification of skin diseases. Two skin disease classification images, including datasets, the Skin Lesions Dermatoscopic Image dataset and Skin Cancer MNIST HAM10000, are selected to validate the classification performance. Comprehensive analyses are performed with significant measures that showed the proposed model attained better performances of 98.6%, 0.95, and 10.43 s from accuracy, intersection of union, and execution time, respectively. The stability and robustness under different environmental conditions for clinical applications in skin disease detection and classification tasks are analyzed for making decisions in real-time scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Dermatitis

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A Comparative Statistical Analysis Between ML Algorithms & DNN Techniques Using MNIST Dataset 4

A Comparative Statistical Analysis Between ML Algorithms & D...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Rastogi, Akshit Rajan Verma, Chelsea Sharma, Divya Goyal, Puneet Kumar Abes Engineering College Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9781665474368

Handwritten digit recognition has been around since a very long time and it has always been a topic of attention among the students and researchers. This job of recognizing handwritten content has great importance as it helps in recognizing various type of important information which are filled by hand but are not clearly understood. While solving this problem, the most common challenge is that the digits written by hands are of different size, orientation or thickness. Inspite of all these challenges, various Machine Learning Algorithms & Deep Neural Network Techniques are trying harder to achieve higher accuracy. In this report, we have tried to compare the accuracies of most common and popular ML Algorithms which includes SVM (Support Vector Machine), Random Forest Classifier (RFC) & KNN (K-Nearest Neighbour) with DNN Techniques (Deep Neural Network Technique) also termed as CNN (Convolutional Neural Network) using Keras. We have applied these algorithms and techniques on MNIST dataset. With the help of the results obtained, we have plotted some graphs to show differences between ML (Machine Learning Algorithms) & DNN (Deep Neural Network Techniques). In this research work, we got the accuracy of approx 97.86% using SVM, 97.10% using KNN, 97.05% using RFC while on the other hand, by using CNN, we got 99.98% accuracy on validation image data. Also, we were able to achieve accuracy approx , 96.85% using RFC, 97.73% using SVM, 96.80% using KNN while on the other hand, by using CNN, we got 98.72% accuracy on test image data. © 2022 IEEE.

关键词： Deep neural networks

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