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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Kumar, G. Muthu Hemanand, D. Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors all over the world. Individuals, organizations, and society as a whole will reap broad and significant advantages as a result of the capabilities of artificial intelligence (AI) both today and in the near future. Nevertheless, these very same technical advancements give rise to significant concerns, such as the question of how to ensure that artificial intelligence technology is built and implemented in a manner that is in accordance with the applicable data privacy laws and standards. The fast development of artificial intelligence presents substantial hurdles in terms of protecting customers' privacy and the confidentiality of their data. The purpose of this essay is to suggest an all-encompassing strategy for the development of a framework to solve these concerns. First, an overview of prior research on security and privacy in artificial intelligence is presented, with an emphasis on both the progress that has been made and the limits that still remain. In the same vein, open research topics and gaps that need to be addressed in order to improve existing frameworks are recognized. Regarding the development of the framework, the topic of data protection in artificial intelligence is discussed. This includes elaborating on the significance of protecting the data that is utilized in artificial intelligence models, as well as elaborating on the policies and practices that are in place to ensure the data's safety and the methods that are utilized to maintain the data's integrity. Additionally, the security of artificial intelligence is investigated, which includes an analysis of the vulnerabilities and dangers that are present in artificial intelligence systems, as well as the presentation of instances of potential assaults and malevolent manipulations,

关键词： Risk assessment

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Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection 9

Smart Energy Management System Using IoT for Real-Time Monit...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Cholla, Ravindra Raman Rao, K Nagendra Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss. © 2024 IEEE.

关键词： Energy utilization

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

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Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

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Comprehensive Analysis of Autism Spectrum Disorder Detection Using Deep Learning 4

Comprehensive Analysis of Autism Spectrum Disorder Detection...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sujatha, E. Devi, R.Suguna Pavai, D. Saranya, K. Department of Computer Science and Engineering Chennai India Department of Electronics and Communication Engineering Chennai India Department of Artificial Intelligence and Data Science Chennai India Chennai India

ISBN: (纸本)9798331540364

Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by a wide range of symptoms. Early and accurate diagnosis is crucial for effective intervention and support. This research reviews the established diagnostic tools, including clinical assessments, behavioral observations, genetic testing, and neuroimaging techniques. Additionally, we explore the potential of emerging technologies like machine learning and AI-based approaches in enhancing ASD diagnosis. By analyzing the strengths and limitations of various detection methods, this research provides a comprehensive overview of the current state of the field. The findings highlight the importance of continued research and innovation in improving ASD diagnosis for the benefit of affected individuals and their families. © 2024 IEEE.

关键词： Diseases

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational Materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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Enhancing Railway Safety with an IoT Based System for Real Time Fault Detection and Crack Monitoring 9

Enhancing Railway Safety with an IoT Based System for Real T...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Chukka, Santhaiah Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

Safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fault finding and crack detecting method, the manual method is involved which is very slow and consist of many errors. By considering all these challenges and issues in track detection method, we have implemented an advanced IoT based railway crack and fault-finding system. It uses advanced sensors, microcontroller, GPS and GSM module which can detect and report faults of the railway track. The system uses real time mechanism. The system is implemented and tested on a prototype railway track to detect crack or any fault. The internet of things technology added modern and accurate fault-finding feature in the system. The IoT plays an important role in implementing advance technologies, as it uses sensors and advance microcontroller. The accuracy rate of fault and crack detection is around 97%, which is for 2 mm crack. Along with this GPS module accurately track fault location. GSM module successfully send the alert message quickly after detecting the fault in the track. Hence the response time is very less and accurate. This system is implemented in order to ensure safety for the railway track and prevent the accidents. Hence the system is useful for improving overall transport safety. © 2024 IEEE.

关键词： Microcontrollers

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Ontological Knowledge-Based Representation System to Prevent Web Attacks in iMANET's

Ontological Knowledge-Based Representation System to Prevent...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Chandani, Priyanka Gupta, Anand Kumar Manivasagam Noida Institute Of Engineering And Technology Department Of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India Department Of Computer Science And Information Technology Bangalore India

ISBN: (纸本)9798350366846

This paper implies securing Internet-based Mobile Ad-hoc Networks (iMANETs) with semantic web techniques. To illustrate safety issues, ontologies will be utilised rather than taxonomies. These ontologies can be added to an intrusion detection system's (IDS) information repository. By employing inference based on semantic relations, intrusion detection systems can identify and incorporate upcoming attacks into their current knowledge base. © 2024 IEEE.

关键词： Ontology

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Millibots: Integrating Deep Learning Algorithms for Autonomous Control in Small-Scale Robotics 15

Millibots: Integrating Deep Learning Algorithms for Autonomo...

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

作者： Sneha, A. Angel Diwan, B. Stella Maris College Of Engineering Artificial Intelligence and Data Science Department Nagercoil India Joesphs College Of Engineering Department of Computer Science St Chennai India

ISBN: (纸本)9798350370249

Millibots, miniature robotic platforms, have emerged as pivotal tools in various domains, ranging from medical interventions to environmental monitoring. However, their diminutive size presents formidable challenges in achieving autonomous navigation and control. In this paper, we delve into the realm of millibots and propose a groundbreaking approach to tackle these challenges by integrating deep learning algorithms for autonomous control. Leveraging the power of deep learning, we address the intricate interplay between perception, decision-making, and locomotion in small-scale robotics. Our methodology not only overcomes the limitations inherent to millibots but also unlocks new frontiers in their application. Through a comprehensive review of related work, meticulous experimentation, and insightful analysis, we demonstrate the unparalleled efficacy of our approach. This paper not only accelerates the advancement of small-scale robotics but also envisions a new era of autonomous exploration and innovation. © 2024 IEEE.

关键词： Motion planning

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