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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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EMaC: Dynamic VM Consolidation Framework for Energy-Efficiency and Multi-metric SLA Compliance in Cloud Data Centers

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SN computer science 2024年第5期5卷 643页

作者： Mongia, Vikas Computer Science and Applications Department Guru Nanak College Punjab Moga 142001 India

Cloud industry is facing significant challenges related to resource under-utilization and high energy consumption. Virtual Machine Consolidation serves as an effective solution to address these issues. However, Virtual Machine Consolidation is a challenging task because the issues such as dynamic nature of workloads, the complexity of decision-making for resource optimization, the need to meet Service Level Agreements (SLAs), and the risks associated with aggressive consolidation must be addressed properly. This work proposes a multi-objective algorithm named Energy-efficient Multi-objective Virtual Machine Allocation and Consolidation (EMaC) that aims to optimize Virtual Machine Consolidation in a datacenter. The algorithm leverages server clustering approach in order to facilitate optimal Virtual Machine Allocation to streamline energy management and reduce server selection time. The algorithm further adapts to changing workloads and reduces frequent server overloading events by incorporating a dynamic threshold policy. Moreover, the proposed framework integrates SLA awareness into server selection, striking a balance between energy efficiency goals and SLA compliance. Performance of the algorithm is validated against benchmark as well as state-of-the-art policies on Cloudsim simulator by utilizing real workload traces collected from PlanetLab and Bitbrains datacenter. From extensive experimentation, the framework demonstrates encouraging results when evaluated against commonly used performance metrics. The EMaC algorithm shows a maximum reduction of 33.12% and 73.01% in energy consumption and SLA violations respectively with the traces of PlanetLab and 39.68% and 50.63% respective reduction with traces of Bitbrains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud Computing Clustering Energy Efficiency Virtual Machine Consolidation

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ML computational inference techniques and indicator metrics for analyzing uncertainties in stock market data

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Multimedia Tools and applications 2025年 1-21页

作者： Henge, Santosh Kumar Mandal, Sanjeev Kumar Sharma, Amit Upadhyay, Aditya Kundu, Shakti Iyer, Srinivasan Saini, Ashok Kumar Yadav, Manoj Kumar Singh, Bhupinder Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Bangalore India Department of Computer Applications Lovely Professional University Punjab Jalandhar India Center for Distance and Online Education Manipal University Jaipur Rajasthan Jaipur India Haryana Gurugram122413 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Jalandhar India

In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and complex natures of data sources become the issue and we need to propose an effective method for adaptive algorithms to accurately forecasting and make decision efficiently. The system framed with two stages of execution: The first stage has covered time series-based predictions for different stock values involving the LSTM Model;the second stage integrated the experimental scenarios based on trend, value, indicators, and it's supporting impacted data metrics, which experimented on various large-cap stock companies. The dataset is sourced online by yahoo finance API with consecutive days data and dependable, non-dependable influenced indicators, which helped to intend trend-based evaluation performances. The proposed model experimented on distinct stock values and test cases executed based on indicator-based prediction with a 76.92% accurate performance rate using a Random Forest Classifier and 76.92% of Logistic Regression. The value-based prediction test case has achieved a performance rate of 97.5% using SVR Regressor and 97.2% using Linear Regressor. It computed value of root mean square error for the Ten large cap companies for performance evaluation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive algorithms

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Attention-CNN-LSTM based intrusion detection system (ACL-IDS) for in-vehicle networks

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Soft Computing 2024年第23期28卷 13429-13441页

作者： Taneja, Amit Kumar, Gulshan Department of Computer Science Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Applications Shaheed Bhagat Singh State University NH-05 Punjab Ferozepur152002 India

Modern vehicles rely on electronic control units (ECUs) communicating through the controller area network (CAN) bus protocol. However, increased connectivity through Wi-Fi, Bluetooth, and onboard diagnostics (OBD) ports has heightened cybersecurity risks due to the CAN bus protocol’s inherent security vulnerabilities. Addressing this challenge requires an in-vehicle intrusion detection system (IDS) with high accuracy and minimal false alarms. Existing IDSs for in-vehicle networks often fall short due to inadequate extraction of network traffic features’ dependencies in a time-series *** overcome this limitation, this study presents ACL-IDS, a hybrid intrusion detection system for in-vehicle network traffic. Leveraging deep learning techniques like CNN, LSTM, and attention mechanisms, ACL-IDS effectively captures short-term and long-term dependencies within network traffic, enhancing intrusion detection accuracy. Extensive experiments on a real benchmark dataset demonstrate ACL-IDS’s superior performance compared to individual, ensemble, and state-of-the-art methods. With detection accuracy reaching up to 99%, ACL-IDS emerges as a robust solution for analyzing and detecting intrusions in in-vehicle network traffic. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Network intrusion

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ADASYN: Oversampling Techniques for COVID-19 Prediction

ADASYN: Oversampling Techniques for COVID-19 Prediction

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mary, L. William Raj, S. Albert Antony SRM Institute of Science and Technology Department of Computer Applications Kattankulathur Chennai India

ISBN: (纸本)9798331512965

In 2019, the highly contagious COVID-19 virus emerged and rapidly spread, causing a global health crisis characterized by inflammatory respiratory symptoms. The primary target of this disease is the respiratory system. Research has identified bats as the reservoir for the virus, but the intermediate host remains unknown. There is ongoing concern about various coronavirus variants, with the Omicron variant previously dominating globally and now attention shifting to a new variant, BA.2.86, informally known as Pirola, which is being closely monitored worldwide. However, the inherent class imbalance in COVID-19 datasets poses a significant obstacle to the effectiveness of these models. This paper proposes a novel approach combining outlier detection and Adaptive Synthetic Minority Oversampling Technique (ADASYNA) to address this issue. The Outlier-ADASYNA method identifies outliers within the minority class (COVID-19 cases) and synthesizes informative samples to rebalance the dataset. The proposed approach is evaluated using data from 5,400 patient records for both training and testing. The study employs ADASYN to effectively mitigate dataset imbalances and outstanding traditional oversampling techniques. This paper presents a comparative analysis of machine learning techniques, evaluating their performance using diverse metrics. The classification algorithm Random Forest attains the highest accuracy percentage of 90%, and RF Oversampling 92% surpassing numerous classification algorithms in predictive performance. © 2025 IEEE.

关键词： COVID-19

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Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques

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Multimedia Tools and applications 2024年 1-32页

作者： Dalal, Surjeet Lilhore, Umesh Kumar Faujdar, Neetu Simaiya, Sarita Agrawal, Akshat Rani, Uma Mohan, Anand Department of Computer Science and Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & amp Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering World College of Technology & ampManagement Haryana Gurugram India Department of Physics Kunwar Singh College Bihar Darbhanga India

The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily functions. While thyroid nodules are predominantly benign, certain cases may manifest malignancy, leading to thyroid cancer. Despite the generally favorable prognosis, early detection and intervention remain paramount for optimal outcomes. Given the limitations in clinical data, this paper introduces an innovative approach employing a modified XGBoost model that integrates both clinical and molecular properties into a unified predictive model. Utilizing fine-needle aspiration biopsies and gene expression analysis, our model aims to enhance cancer prediction in thyroid nodules. We explore the influence of incorporating high-quality clinical data on the prioritized gene set, employing a nature-inspired method for hyperparameter optimization. The modified XGBoost model emerges as highly recommended for non-parametric feature selection, extraction, and prediction due to its remarkable accuracy. Our proposed model attains an impressive 94.6% accuracy, surpassing other machine learning models such as C5.0 (92.5%), CART (88.7%), CHAID (83.4%), Quest (81.5%), LSVM (77%), and random tree (24.1%). The real-time efficiency of our model is demonstrated in the results section. Experiments showcase the superior performance of combined molecular and clinical data-based prediction models. Emphasis is placed on the importance of scrutinizing the origins of clinical data and maintaining vigilance over variable quality. This study signifies a substantial advancement in thyroid cancer prediction, offering a robust and accurate model that integrates diverse data sources, ultimately contributing to improved patient outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Intrusion Detection Using Dynamic Feature Selection: An Adaptive Bacterial Foraging Method

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SN computer science 2024年第6期5卷 679页

作者： Rajeshwari, R. Anuradha, M.P. Department of Computer Applications Bishop Heber College Affiliated to Bharathidasan University Department of Computer Science Bishop Heber College Affiliated to Bharathidasan University

The continuous area of cyberattacks demands more responsive defenses than classic, static intrusion detection systems (IDS) can provide. This research provides a unique method that exploits the skills of adaptive bacterial foraging optimization (BFO) for dynamic function selection in IDS. This strategy overcomes the constraints of static strategies by exploiting the collective intelligence and versatility of BFO, permitting real-time choice of the maximum vital attributes primarily based on current feedback and attack patterns. Utilizing chemotaxis, swarming, and elimination/dispersal strategies, the algorithm constantly refines its characteristic choice *** critiques on the CICIDS2018 dataset display the prevalence of our adaptive BFO approach. Compared to static BFO and different dynamic techniques, our technique constantly excels across multiple attack types, achieving superior accuracy, precision, and F1 score. Ablation research further emphasizes the relevance of excellent adaptive components, with swarming playing a massive role. By handing over superior detection accuracy, flexibility, and computing economy, the adaptive BFO method enhances IDS. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bacterial foraging Chemotaxis and swarming Cyberattacks Dynamic selection Intrusion detection

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Interpreting Temporal Dynamics in Distributed Fuzzy Cognitive Maps for Time Series Prediction 11

Interpreting Temporal Dynamics in Distributed Fuzzy Cognitiv...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Haritha, K. Judy, M.V. Cochin University of Science and Technology Department of Computer Applications Ernakulam India

ISBN: (纸本)9798350379136

Fuzzy Cognitive Maps (FCMs) are powerful tools for the modeling of complex systems, one of which is time series modeling. Although advocated for their interpretability, existing methods measure concept relevance based on static structural features often overlooking the dynamic influences over the time. This paper presents a novel SHapley Additive exPlanations (SHAP) framework based Distributed FCM model. The proposed approach invokes functions to determine the SHAP concept relevance scores for the Distributed Fuzzy Cognitive Maps (DFCMs) for time series prediction, marginalizing over each concept and comparing the predicted states to an expected baseline iteratively. This method is interpreted as measuring the time-varying impact of each concept through the dynamic course of the system. The SHAP-based approach improves the interpretability and application of FCMs to the modeling of complex time-course systems. © 2024 IEEE.

关键词： Fuzzy Cognitive Maps

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Enhancing Phishing Website Detection Using Ensemble Machine Learning Models

Enhancing Phishing Website Detection Using Ensemble Machine ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Baliyan, Himanshu Prasath, A. Rama Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350373783

Phishing websites remain a persistent and evolving threat to online security, employing deceptive tactics to lure users into revealing sensitive information such as login credentials, financial data, and personal details. These malicious websites often mimic legitimate ones, making it challenging for users to discern their authenticity. As traditional detection methods struggle to keep pace with increasingly sophisticated phishing techniques, the need for more robust and adaptable solutions is paramount. This research investigates the application of ensemble machine learning models for improved phishing website detection. By combining multiple machine learning algorithms, ensemble models leverage their collective strengths to achieve higher accuracy and generalizability compared to individual models. Our approach incorporates a comprehensive feature extraction process, analyzing various URL and website characteristics such as domain age, URL length, presence of special characters, and HTML and JavaScript features. Through rigorous training and testing on a diverse dataset of legitimate and phishing websites, our ensemble model demonstrated a 91 percent accuracy in identifying phishing attempts, surpassing the performance of individual models like Random Forest, Decision Tree, and Logistic Regression. These findings underscore the potential of ensemble machine learning models as a powerful tool for enhancing phishing website detection. The improved accuracy and adaptability offered by our approach can contribute to the development of more effective security measures, protecting users from online fraud and preserving the integrity of the digital landscape. Future research will explore incorporating additional features and exploring advanced ensemble techniques to further enhance detection capabilities and address emerging phishing threats. © 2024 IEEE.

关键词： Phishing

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Speculative Analysis of CNN's and ResNet50 for the Identification of Emotion From Facial Expressions 9

Speculative Analysis of CNN's and ResNet50 for the Identific...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Kumar, Bathinapatla Hemanth Gokila, S. Hindustan Institute of Technology And Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350365092

Facial recognition software has become essential in several domains, such as security and surveillance and smart device user identification. This paper looks at the effectiveness of deep learning methods for facial recognition to classify images based on the emotion, with a particular focus on Convolutional Neural Networks (CNN). A condensed CNN and ResNet50 are the two CNN architectures whose impacts on loss values, processing efficiency, and accuracy are specifically evaluated. A variety of face datasets containing images depicting seven distinct emotional states - Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral - are used to train and assess the models. Assessment metrics, such as calculation time, accuracy, and loss values, are employed to evaluate the effectiveness of every technique. © 2024 IEEE.

关键词： Convolutional neural networks

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