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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sheeba, Adlin Swetha, P. Vetri Selvi, S. Subasini, C.A. Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9798331522667

Underwater data transmission forms a feasible method for underwater communication. However, laboratory experiments significantly differ from natural aquatic settings due to physical size constraints. During the last few decades, artificial scattering agents have been used to simulate underwater data communications under various conditions, but the similarity between experimental water and natural water remains unreliable, especially in frequency domain characteristics. This paper introduces a framework for the evaluation and optimization of frequency domain characteristics of data transmission across seawater channels under experimental conditions. This research study systematically analyse different agents and particle sizes to bridge the gap between experimental setups and real-world applications. This research further proposes a real-time communication system that can transmit biomedical data from sea researchers to a monitoring system onboard vessel. Through comparative analysis, novelty is established, and its practical implications for marine research and underwater exploration are emphasized. © 2025 IEEE.

关键词： Seawater

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A Comprehensive and Systematic Analysis of One-Way Hashing and Its Advancements 3

A Comprehensive and Systematic Analysis of One-Way Hashing a...

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

作者： Chakraborty, Abhilash Biswas, Anupam National Institute of Technology Silchar Department of Computer Science and Engineering Assam Silchar788010 India

ISBN: (纸本)9798331523893

Cryptographic hash functions are fundamental to secure digital interactions. This paper surveys one-way hashing, covering theoretical foundations, historical developments, cryptanalytic challenges, and widely trusted standards like SHA-2, SHA-3, and BLAKE2/BLAKE3. It also explores experimental designs and proprietary functions enhancing password protection, IoT security, and blockchain mechanisms. The study examines tensions between performance and security, classical versus post-quantum resilience, and theoretical rigor versus practical engineering. Hashing is both a mature discipline and an evolving frontier, poised to adapt to changing computational paradigms and sophisticated adversarial methods while meeting growing demands for data integrity and authenticity guarantees. © 2025 IEEE.

关键词： Authentication

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Classification of imbalanced datasets utilizing the synthetic minority oversampling method in conjunction with several machine learning techniques

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Iran Journal of computer science 2024年第1期8卷 51-68页

作者： Datta, Shrayasi Ghosh, Chinmoy Choudhury, J. Pal Department of Information Technology Jalpaiguri Government Engineering College West Bengal Jalpaiguri 735102 India Department of Computer Science & amp Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri 735102 India Department of Computer Science & amp Engineering Narula Institute of Technology West Bengal Agarpara 700109 India

Pharmacological datasets like Yeast and Escherichia coli (E. coli) have a massive impact on the healthcare industry for the production of human drugs. Yeast is well recognized as a significant constituent in the production of pharmaceuticals for human use, owing to its cellular structure that bears resemblance to that of human cells. E. coli is widely regarded as the preferred expression host within the biotechnology industry for the efficient and cost-effective production of proteins on a large scale. This preference is particularly evident in the context of non-glycosylated proteins, which are of significant interest for their therapeutic applications. This study aims to determine the efficacy of machine learning (ML) methods in classifying pharmacological datasets like Yeast and E. coli. As Yeast and E. coli both are imbalanced datasets by nature, to handle the imbalanced classification problem, the synthetic minority oversampling technique (SMOTE) is used in this study. The application of the combined feature subset selection (CFSS) approach has been also employed to find out the overall classification performance, but classification with CFFS has not been very promising. According to the findings, classification on SMOTE-oversampled data gives better results and from several performance and evaluation metrics, random forest achieves the best overall prediction performance for both Yeast and E. coli datasets with an accuracy of 87% and 97%, precision of 87% and 97%, recall of 87% and 98%, and f1 score of 85% and 98%, respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification E. coli Feature selection SMOTE Yeast

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Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern Optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. This early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. Thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. The method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. The classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. The method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

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A New Method for Image Tamper Detection Based on an Improved U-Net

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Intelligent Automation & Soft Computing 2023年第9期37卷 2883-2895页

作者： Jie Zhang Jianxun Zhang Bowen Li Jie Cao Yifan Guo Department of Computer Science and Engineering Chongqing University of TechnologyChongqing40005China

With the improvement of image editing technology,the threshold of image tampering technology decreases,which leads to a decrease in the authenticity of image *** has also driven research on image forgery detection *** this paper,a U-Net with multiple sensory field feature extraction(MSCU-Net)for image forgery detection is *** proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or ***-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the features of different perceptual fields use residual propagation and residual feedback to recall the input feature information and consolidate the input feature information to make the difference in image attributes between the untampered and tampered regions more obvious,and introduce the channel coordinate confusion attention mechanism(CCCA)in skip-connection to further improve the segmentation accuracy of the *** this paper,extensive experiments are conducted on various mainstream datasets,and the results verify the effectiveness of the proposed method,which outperforms the state-of-the-art image forgery detection methods.

关键词： Forgery detection multiple receptive fields cyclic residuals U-Net channel coordinate confusion attention

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Detection of Deceptive Hotel Reviews Through the Application of Machine Learning Techniques 2nd

Detection of Deceptive Hotel Reviews Through the Application...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Hossain, Md Jakir Chowdhury, Md Tanvir Rahman, Habibur Hossain, Md Shohrab Shara, Shabrina Akter Choudhury, Tashfia Chowdhury, Maleha Israt Synthia, Tasnim Israk Talha, Abu Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Textile Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819735938

In the contemporary digital landscape, online reviews play a pivotal role in shaping tourists’ decisions, leveraging the pervasive influence of the internet. However, discerning the authenticity of reviews has become an intricate challenge, blurring the line between genuine and deceptive evaluations. This ambiguity provides fertile ground for individuals seeking to exploit the uncertainty, as witnessed in the proliferation of false reviews and the spread of unfavorable rumors through social media platforms. The consequences are profound, with travelers and hosts alike navigating a landscape where traditional verification systems prove insufficient, leaving consumers vulnerable to misinformation. To address this pressing issue, we propose a novel machine learning-based approach. Our strategy employs classifiers such as Random Forest, Support Vector Machine, and Decision Tree, each exhibiting robust validation accuracy values of 92.19%, 87.19%, and 86.56%, respectively. This innovative framework serves as a powerful tool to distinguish between authentic and fraudulent reviews, providing a more reliable foundation for decision-making in the travel sector. By leveraging the capabilities of machine learning, we aim to create a system that not only identifies deceptive practices but also contributes to restoring trust in online reviews. As the travel industry contends with the challenges posed by the digital era, this approach offers a promising avenue for mitigating the impact of false information and enhancing the overall integrity of the review ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Analysis and Impact of Electromagnetic Field Leakage in WPT on Central Nervous System: A Neural Network Approach 4

Analysis and Impact of Electromagnetic Field Leakage in WPT ...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Shukla, Garima Awasthi, Vanshaj Goswami, Prafull Singh, Sofia Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India Indian Institute of Information and Technology Department of Computer Science and Engineering Sonipat India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350383997

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies © 2024 IEEE.

关键词： Inductive power transmission

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MCANet: Multimodal Caption Aware Training-Free Video Anomaly Detection via Large Language Model 27th

MCANet: Multimodal Caption Aware Training-Free Video Anomaly...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Dev, Prabhu Prasad Hazari, Raju Das, Pranesh Machine Learning Laboratory Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9783031781247

Towards Video Anomaly Detection (VAD), existing methods require labor-intensive data collection and model retraining, making them costly and domain-specific. The proposed method, termed as Multi-modal Caption Aware Network (MCANet), introduces a novel paradigm that identifies anomalies in video sequences without requiring prior domain knowledge. This training-free VAD approach dynamically generates and analyzes textual descriptions of video frames by utilizing off-the-shelf vision-language model (VLM), audio-language model (ALM) and large language model (LLM). MCANet has four primary modules. The first module utilizes image-text similarities to clean noisy captions generated by the image captioning model, while the second module applies audio-text similarities to refine noisy captions produced by the audio captioning model. The third module employs a LLM to consolidate scene dynamics over time. Finally, the fourth module enhances the results by aggregating scores from semantically similar frames based on video-text similarity. To validate the effectiveness of the proposed method, experiments are conducted on two large-scale benchmark datasets (UCF-Crime and XD-Violence). Experimental results demonstrate that MCANet surpasses existing unsupervised and one-class approaches without requiring any training or data collection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Detection and Prognosis of Pulmonary Tuberculosis Using ML Classifiers with Soft Computing 5

Detection and Prognosis of Pulmonary Tuberculosis Using ML C...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Ganesh, D. Kumar, Santosh Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Internet of Things Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Several intriguing research works on soft computing techniques have recently been presented and for the creation of a pulmonary TB diagnosis system, researchers continue to build an accurate and trustworthy intelligent decision-making approach. The current diagnostic testing system processes are cumbersome and time-consuming to analyse. Since increased sensitivity and specificity can be attained, the diagnosis of TB still has to be improved upon using new speedy and accurate diagnostic models and procedures, which will help with disease prevention and control. Therefore, this review paper's significance lies in its ability to discriminate between the many soft computing methodologies used to help the diagnosis, identification, prediction, and intelligent categorization of pulmonary TB illness. Researchers and medical professionals anticipate employing soft computing methods in the field. 'Artificial neural networks, genetic algorithms, support vector machines, fuzzy logic', and others are a few of these. The most recent diagnostic techniques involve artificial neural networks. Predictive inference accuracy and easy-to-optimize, adoptable non-linear modelling of large data sets are some additional advantages of artificial neural networks, showing that they could be an important decision support tool in a variety of industries, including medicine. © 2024 IEEE.

关键词： Artificial Neural Network Fuzzy and Fuzzy Logic Genetic Algorithm Pulmonary Tuberculosis Support Vector Machine

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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