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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Kenza Meridji Wael Hadi Khalid T. Al-Sarayreh Department of Software Engineering University of Petra Amman Jordan Department of Computer Science University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

software architecture defines the underlying composition of a software system. It includes high-level design decisions and principles that guide software application development, deployment, and operation. Identifying the appropriate architecture type for software systems is crucial in the development. Investigating the association between software defects and architecture types can provide a good insight and indication of the quality that may affect the software system. As well as it supports the software engineering empirical studies. For this purpose the International software Benchmarking Standards Group (ISBSG) development and enhancement dataset that contains a large number of software projects for different software project conducted by international companies in the world. As a result. This investigation will help industries to improve their project qualities and reduce cost. Additionally, statistical techniques such as SPSS that will help identify the strength of the relationship and formulate correct decisions for the software projects; especially by applying the impact of the results on a real life software project.

关键词： Industries Costs Statistical analysis software architecture computer architecture software quality Benchmark testing software systems software Standards

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An IT/OT Converged Cyber Range Framework Supporting Ensemble of Large Multimodal Models-Human Teaming and RAG Capability for Bigdata Information Retrieval - 6G Perspective

An IT/OT Converged Cyber Range Framework Supporting Ensemble...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Rahman, M. Abdur Islam, M. Saiful Islam, M. Repon Hossain, M. Shamim Al-Hazzaa, Selwa University of Prince Mugrin Cyber Security and Forensic Computing Department Medina Saudi Arabia UoL Worldwide University of London London United Kingdom Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna Bangladesh King Saud University Department of Software Engineering Riyadh Saudi Arabia Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

In IT/OT converged enterprise networks, the management of multimodal big data has surpassed human capabilities, presenting critical challenges in business decision-making. This paper introduces an innovative IT/OT Converged Cyber Range Framework, leveraging the power of 6G technology alongside an ensemble of large multimodal models (LMMs) equipped with Retrieval Augmented Generation (RAG) capabilities. Our proposed framework excels at parsing and retrieving information from diverse and massive multimodal datasets in near real-time, driven by the integration of LMMs and human interactions in natural language. The framework's advanced configuration with RAG achieved top performance with an average score of 80.5 across key metrics, including an 86 in answer relevancy and an 88 in faithfulness, while effectively reducing hallucinations to a score of 26. By combining 6G's connectivity with advanced LMM capabilities, we significantly improve the efficiency of automated big data processing and information retrieval, thereby transforming decision-making processes in IT/OT environments. The implementation of our framework, validated through real-life big data sources, and the encouraging test results showcase its effectiveness and transformative potential in managing and utilizing big data far beyond traditional human capabilities. © 2024 IEEE.

关键词： 6G Technology Human-LMM Teaming IT/OT Converged Network Large Multimodal Models (LMMs) Multimedia Bigdata Retrieval Augmented Generation (RAG)

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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Feature Reduction-based Intrusion Detection (FRID) in Satellite-Terrestrial Integrated Networks 5

Feature Reduction-based Intrusion Detection (FRID) in Satell...

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5th International Conference on High Performance Big Data and Intelligent Systems, HDIS 2023

作者： Yao, Lin Tian, Ziyuan Deng, Jing Wu, Guowei Li, Li DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian China School of Software Dalian University of Technology Dalian China The Department of Computer Science UNC Greensboro UNC NC Greensboro27412 United States Department of Computer and Information Science University of Macau China

ISBN: (纸本)9798350394160

Satellite-Terrestrial Integrated Networks (STINs) integrate satellite networks, the Internet, and mobile wireless networks and are able to provide powerful services to users. In STIN, satellite gateways play an important role of connecting space-based networks to terrestrial-based networks, but can be under frequent attacks such as Distributed Denial of Services (DDoS) attack. Therefore, intrusion detections become essential in STINs. In this work, we propose a feature reduction-based intrusion detection (FRID) technique with the help from deep reinforcement learning. In FRID, only features that are highly distinguishable, robust, and independent are selected for the later stages. Experimental results show the accuracy, recall, FAR, and F1 scores comparing FRID with several state-of-the-art schemes and demonstrate its superiority. ©2023 IEEE.

关键词： Intrusion detection

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Objective Full-Reference Image Assessment Metrics for Estimating the Quality of Remote Sensing Images 2

Objective Full-Reference Image Assessment Metrics for Estima...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Maruthi, R. Anusha, P. Sankar, P. Thiyagaragan, K. Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India Periyar Maniammai Institute of Science and Technology Department of Software Engineering Thanjavur India Hindustan Institute of Technology and Science Department of Electronics and Communication Engg Tamil Nadu Chennai India

ISBN: (纸本)9798350397635

Image Quality (IQ) assessment is a very complex task and it is extremely important to evaluate the images with the metrics. The metrics applied can be a full reference, partial reference or no-reference metric and it depends on the application and availability of the ground truth. Most of the IQ metrics are developed by considering the Visual System (VS) of humans. The assessment methods studied in this paper focuses on some of the Full-Reference (FR) measures and it is used to estimate the remote sensing noisy images. The effectiveness of the measures demonstrates a considerable outcome and demonstrates how well the noisy remote sensing images are being quantified. © 2023 IEEE.

关键词： Image quality

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Enhancing Brain Stroke Risk Prediction with Multi-Algorithm Evaluation and Web Interface

Enhancing Brain Stroke Risk Prediction with Multi-Algorithm ...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Muhammad Ahmed Umer Liaquat Attique Ur Rehman Sabeen Javaid Tahir Muhammad Ali Azka Mir Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Science and Technology Kuwait

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Brain stroke is the world's leading cause of death, impacting numerous lives annually. The chances of having a stroke have increased by 50% over one's lifetime, impacting one in four people worldwide. Machine learning may assist in the prior detection of strokes, allowing for more timely diagnosis which can save a person's life and disability. However, existing methods face challenges in integrating ML models with clinical applications to predict patient stroke status with optimal accuracy. This study conducts a comprehensive investigation into brain stroke risk prediction using numerous ML algorithms, including Random Forest, Logistic Regression, KNN, Naive Bayes, XGBoost, and LightGBM. Our main contribution is the development of a web app that predicts stroke based on user inputs with optimal accuracy. The selected dataset contains 5110 records of normal people and 249 records of people having strokes which is highly imbalanced. To ensure model robustness, the dataset undergoes balancing through SMOTE oversampling and KNN Imputation for handling missing values. Custom thresholds for classifiers optimize training, with comprehensive evaluations utilizing key statistics such as confusion matrices, F1-Score, recall, accuracy, and precision. Cross-validation is employed to assess potential overfitting and underfitting risks in machine learning models. Random Forest ranked as the most imposing, reaching a remarkable 99.85% accuracy on the test set with a custom threshold of 0.6 and a random state at 42. Additionally, a user-friendly Web App has been developed to predict stroke status based on user input, enhancing practical usability and accessibility for individuals seeking risk assessments.

关键词： Training Logistic regression Accuracy Predictive models Prediction algorithms Robustness Imputation

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Sumble, Malaika Aziz, Sumair Khan, Muhammad Umar Iqtidar, Khushbakht Fernandez-Rojas, Raul University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences & Tech. Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles. © 2024 IEEE.

关键词： Drones

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On Federated Multi-Armed Bandits for Mobile Social Networks

On Federated Multi-Armed Bandits for Mobile Social Networks

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International Conference on Distributed Computing Systems

作者： Kazuya Sakai Takeshi Kitamura Min-Te Sun Wei-Shinn Ku Department of Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Multi-armed bandits (MABs) are widely used for decision making processes, in which an agent tries to maximize a long-term reward by balancing exploiting and exploring actions. In this paper, we are particularly interested in federated MABs for mobile social networks (MSNs), where a collection of agents learn action values by locally observing rewards and exchanging estimated action values with other agents. Our research differs from the existing federated MABs in the underlying network setting, such that agents can communicate with each other only at opportunistic contact events. To this end, we first design the weighted-connectivity (WC) centrality to quantify the importance of agents in an MSN, and then, we propose the weighted-connectivity upper confidence bound (WC-UCB) algorithm for the MSN contexts. The key idea to reduce biases at each agent and to utilize limited opportunities of federated updates is to prioritize the estimates of action-value functions computed by the agents with a high WC centrality. In addition, the performance bound in terms of the cumulative regret is analyzed. The performance of the proposed algorithm is evaluated by simulations using real mobility traces and the results demonstrate that our WC-UCB outperforms the state-of-the-art algorithms in terms of the average reward and the cumulative regret.

关键词： Social networking (online) Simulation Computational modeling Decision making Optical wavelength conversion Complexity theory Distributed computing

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PPDMIT: a lightweight architecture for privacy-preserving data aggregation in the Internet of Things

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 5211-5223页

作者： Gheisari, Mehdi Javadpour, Amir Gao, Jiechao Abbasi, Aaqif Afzaal Pham, Quoc-Viet Liu, Yang Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science University of Virginia VA United States Department of Software Engineering Foundation University Islamabad44000 Pakistan Korean Southeast Center for the 4Th Industrial Revolution Leader Education Pusan National University Busan46241 Korea Republic of ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900-347 Portugal

Data is generated over time by each device in the Internet of Things (IoT) ecosphere. Recent years have seen a resurgence in interest in the IoT due to its positive impact on society. However, due to the automatic management of IoT devices, the possibility of disclosing sensitive information without user consent is high. A situation in which information should not be unintentionally disclosed to outside parties we do not trust, i.e., privacy-preservation. Additionally, IoT devices should share their data with others to perform data aggregation and provide high-level services. There is a trade-off between the amount of data utility and the amount of disclosure of data. This trade-off has been causing a big challenge in this field. To improve the efficiency of this trade-off rather than current studies, in this study, we propose a Privacy-Preserving Data Aggregation architecture, PPDMIT, that leverages Homomorphic Paillier Encryption (HPE), K-means, a One-way hash chain, and the Chinese Remainder Theorem (CRT). We have found that the proposed privacy-preserving architecture achieves more efficient data aggregation than current studies and improves privacy preservation by utilizing extensive simulations. Moreover, we found that our proposed architecture is highly applicable to IoT environments while preventing unauthorized data disclosure. Specifically, our solution depicted an 8.096% improvement over LPDA and 6.508% over PPIOT. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Video Surveillance-Based Intrusion Detection System in Edge Cloud Environment 3rd

Video Surveillance-Based Intrusion Detection System in Edge ...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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