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Aluminium-Metal oxide (Al2O3) Nanofluid Cooling Technique: Enhancing the Efficiency of Crystalline Silicon Cell Panels

Aluminium-Metal oxide (Al2O3) Nanofluid Cooling Technique: E...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kukreti, Sanjeev Dahiwade, Mandakini Shnain, Ammar Hameed Dixit, Krishna Kant Upadhyay, Shrikant Ramya, R. Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Basic Engineering Indira College of Engineering and Management Pune India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering GLA University Mathura India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Saveetha school of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350355338

Solar power is rapidly becoming more popular due to its enormous potential as a long-term energy solution. The efficiency of a photovoltaic panel decreases with increasing temperature. The power output of the PV module decreases as the temperature rises unless the heat is removed. Module cooling has the potential to increase the efficiency of photovoltaic panels. This system uses an aluminium-metal oxide (Al2O3) nanofluid for cooling purposes. By keeping a constant temperature, a solar energy conversion system that combines photovoltaic (PV) and aluminium-metal oxide (Al2O3) nanofluid (Aluminium-metal oxide (Al2O3) nanofluid) modules can achieve greater efficiency. Investigations into the nano fluidity of aluminium-metal oxide (Al2O3) have led to the development of a model demonstrating the potential efficacy of PV-Aluminium-metal oxide (Al2O3) combo modules. The research shows that a cooling system made of aluminium-metal oxide (Al2O3) nanofluids can increase the efficiency of electric conversion in a photovoltaic array. A photovoltaic module's output power drops with increasing temperature if the heat is not dissipated. © 2024 IEEE.

关键词： Solar power generation

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AI Ethics In Neurology: A Systematic Review on Ethical Concerns in Healthcare

AI Ethics In Neurology: A Systematic Review on Ethical Conce...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Rufina Hussain Safdar Tanweer Sherin Zafar Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

One of the most exciting technological developments in the coming years is Artificial Intelligence (AI), and the healthcare industry stands to gain the most from its integration. Today in the whole World the use of AI in healthcare is expanding. The phrase “Artificial intelligence” (AI) refers to the use of technology and computers to mimic human-like critical thinking and intelligent behavior. A few of the new applications of AI/ML are disease identification, screening, diagnosis, medical imaging, intelligent health operations management, personalized medicine, digital public health surveillance, outbreak prediction, and drug discovery. Applications AI in Neurology are starting to move from a bright future to a clinical reality. AI has the potential to revolutionize Neurologic clinical practice, impacting care delivery, affordability, and quality. The potential of AI in neurology is exemplified by epilepsy, where a wide range of new applications, including prognostic, therapeutic, and diagnostic ones, are being developed at a rapid pace. Further, as AI technologies get further developed and applied in clinical decision making, it is important to have processes that discuss accountability in case of errors for safeguarding and protection. The main objective of such AI-assisted System is to provide the benefit to the greatest number of average people in a safe and highly precise manner. Therefore, it is crucial to understand the ethical ramifications of research design and algorithm decisions that can inadvertently contain AI in order to guarantee that the integration of AI into neurology care does not cause harm to patients.

关键词： Ethics Neurology Technological innovation Surveillance Decision making Medical services Artificial intelligence Protection Sustainable development Systematic literature review

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Algorithmic Approaches to Securing Cloud Environments in the Realm of Cybersecurity 10

Algorithmic Approaches to Securing Cloud Environments in the...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Devipriya, A. Rosaline, R. Anto Arockia Prabhu, M. Ramkumar Nancy, P. Karthick, V. Kadumbadi, Vijayakumar Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Chengalpattu India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai Thandalam India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This paper introduces a systematic methodology for enhancing cloud security in information systems by applying mathematical algorithms in cybersecurity. Using the most sophisticated computational methods, our approach strives to boost the security posture of cloud-based platforms by not only identifying but also blocking, and rectifying various cyber threats. We start with a detailed review and analysis of various algorithms, which contribute to the identification of gaps in the current research landscape and solutions already existing. We proceed by performing the requirement analysis during which we set up security targets for the cloud environment to be optimized. The second step consists of deliberation on what the best algorithms are and is involved in a process that will meet the identified requirements. We did the development work for a prototype of the security solutions described beforehand. It was integrated and tested on a cloud environment for the best KPI result. Performance metrics, including detection accuracy (98.5%), false positive rate (0.2%), response time (25 milliseconds), and resource utilization (CPU: The JSON-LD, Microformats, and MAP-trees (10%, 30%, and 40%) algorithms were assessed to determine the functionality of the algorithms. Thus, the security effectiveness values will be obtained in the following way: malware detection (99%) - intrusion detection (97%) - data breach prevention (95%) and insider threat detection (98%) will be determined through comprehensive testing with numerous cyber threats. One more thing I would like to mention is the scalability parameter of the security solutions they function both for 1,000 concurrent users and handle 1TB of data per day and 1,000 requests per second. The team calculated the scalability which was found out to be 95% which is over 90% thus highly scalable to meet the growing demand. Besides, user satisfaction ratings were obtained which were;(4.5), (4.3), (4.7) and (4.6) for usability, reliabi

关键词： Cybersecurity

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Analysis of Elliptic Curve Cryptography with AES for Protecting Data in Cloud with improved Time efficiency 2

Analysis of Elliptic Curve Cryptography with AES for Protect...

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2nd International Conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Sivajyothi, Mithakala Devi, T. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9781665466431

Aim: Data is secured in the cloud using Elliptic Curve Cryptography (ECC) compared with Advanced Encryption Standard (AES) with improved time efficiency. Materials and Methods: Encryption and decryption time is performed with files stored in the cloud. Protecting data with improved time efficiency is carried out using ECC where the number of samples (N=6) and AES (N=6), obtained using the G-power value of 80%. Results: Mean time of ECC is 0.1683 and RSA is 0.7517. Significant value for the proposed system is 0.643 (p > 0.05). Conclusion: Within the limit of study, ECC performs faster in less consumption time when compared to AES. © 2022 IEEE.

关键词： Public key cryptography

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Advancements in AI-based Crime Detection and Prediction

Advancements in AI-based Crime Detection and Prediction

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： E. Monika T. Rajesh Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

A community’s security is its first concern, so governments must act appropriately to lower the crime rate. As a result, there has been a lot of study done in the important field of artificial intelligence (AI) and crime prediction. With the use of big data, machine learning, and predictive analytics, AI-based crime detection and prediction systems have developed quickly to improve public safety. These technologies, which work together to prevent and lessen criminal activity, include facial recognition, geographical analysis, behavioral analysis, natural language processing, and predictive policing. But the use of these AI tools presents important ethical issues that need to be resolved. Since algorithms trained on skewed data have the potential to disproportionately target minority communities, bias and fairness are important concerns. The massive data collection and surveillance required for these systems to operate well give rise to privacy concerns. Considering that AI decision-making processes are frequently opaque, transparency and accountability are also issues. Furthermore, the absence of informed permission and the possible abuse of technology for repressive or political purposes. Technology improvements must be balanced with ethical considerations, and this can be achieved through the implementation of policies, procedures, public involvement, oversight, and ongoing review. To ensure ethical use, it can be helpful to develop algorithms that are conscious of fairness, to set clear regulations, to involve communities in the deployment of AI, and to create independent oversight agencies. Sustaining AI systems’ efficacy and equity requires regular evaluation of their impact and performance. It is conceivable to maximize the advantages of AI in crime detection while preserving individual rights and public confidence by giving these ethical principles top priority

关键词： Ethics Law enforcement Reviews Face recognition Surveillance Public security Natural language processing Regulation Security Artificial intelligence

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Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

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computers, Materials & Continua 2022年第11期73卷 2223-2239页

作者： Abdul Saboor Mohd Fadzil Hassan Rehan Akbar Erwin Susanto Syed Nasir Mehmood Shah Muhammad Aadil Siddiqui Saeed Ahmed Magsi High-Performance Cloud Computing Centre(HPC3) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia School of Electrical Engineering Telkom UniversityBandungIndonesia KICSIT Institute of Space Technology(IST)IslamabadPakistan Department of Electrical and Electronics Engineering Universiti Teknologi PETRONASSeri IskandarPerakMalaysia

Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud ***,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location ***,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud ***,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security *** this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and *** underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center *** preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.

关键词： Root of Trust(RoT) Trusted Platform Module(TPM) cryptoprocessor microservices Hardware Security Modules(HSM) DevOps

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Green Computing for Industrial Wireless Sensor Networks: Energy oriented Cross Layer Modelling

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Recent Patents on engineering 2022年第3期16卷 97-109页

作者： Ram, Mahendra Kumar, Sushil Kumar, Arvind Kharel, Rupak School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi110067 India Department of Computer Science and Engineering in School of Computing Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh Greater Noida India Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom

Background: Enabling industrial environment with automation is growing trend due to the recent developments as industry 4.0 centric production. The industrial wireless sensor network environments have a number of constraints, including densely deployed nodes, delay constraint for mechanical operation, and access constraints due to node position within instruments. The related literature have applied existing models of wireless sensor network in industrial environment without appropriate updating in the different layers of communication, which results in performance degradation in realistic industrial scenario. Method: This paper presents a framework for Energy Oriented Cross Layer Data Dissemination Path (E-CLD2P) towards enabling green computing in industrial wireless sensor network environments. It is a cross-layer design approach considering deployment of sensors at the physical layer up to data dissemination at the network layer and smart services at application layer. In particular, an energy centric virtual circular deployment visualization model is presented focusing on physical layer signal transmission characteristics in industrial WSNs scenario. A delay centric angular striping is designed for cluster based angular transmission to support deadline constrained industrial operation in the WSNs environments. Algorithms for energy centric delivery path formulation and node’s role transfer are developed to support green computing in restricted access industrial WSNs scenario. Results: The green computing framework is implemented to evaluate the performance in a realistic industrial WSNs environment. Conclusion: The performance evaluation attests the benefits in terms of number of metrics in realistic industrial constrained environments. © 2022 Bentham Science Publishers.

关键词： Sensor nodes

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Multimodal Medical Image Registration and Fusion for Quality Enhancement

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computers, Materials & Continua 2021年第7期68卷 821-840页

作者： Muhammad Adeel Azam Khan Bahadar Khan Muhammad Ahmad Manuel Mazzara Department of Electronic Engineering The Islamia University of Bahawalpur63100Pakistan Department of Telecommunication Engineering The Islamia University of Bahawalpur63100Pakistan Department of Computer Science National University of Computer and Emerging SciencesChiniot35400Pakistan Institute of Software Development and Engineering Innopolis UniversityInnopolis420500Russia

For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human ***,most of the time,medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited *** overcome this problem,a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early *** the proposed method,a Multi-resolution Rigid Registration(MRR)technique is used for multimodal image registration while Discrete Wavelet Transform(DWT)along with Principal Component Averaging(PCAv)is utilized for image *** proposed MRR method provides more accurate results as compared with Single Rigid Registration(SRR),while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational *** proposed method is tested on CT and MRI brain imaging modalities of the HARVARD *** fusion results of the proposed method are compared with the existing fusion *** quality assessment metrics such as Mutual Information(MI),Normalize Crosscorrelation(NCC)and Feature Mutual Information(FMI)are computed for statistical comparison of the proposed *** proposed methodology provides more accurate results,better image quality and valuable information for medical diagnoses.

关键词： Multimodal registration fusion multi-resolution rigid registration discrete wavelet transform principle component averaging

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Residual Learning for Enhanced Seismic Signal Quality with Denoising CNN and Kalman Filtering

Residual Learning for Enhanced Seismic Signal Quality with D...

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International Conference on Circuits, Communication, Control and Computing

作者： S.K. Aruna N. Deepa Devi. T Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

This study introduces an innovative approach to enhance seismic signal quality through the integration of Denoising Convolutional Neural Networks (DCNN) and Kalman Filtering (KF). Seismic data often suffers from pervasive noise, impacting the accuracy of signal interpretation. In our methodology, Denoising CNN serves as a robust feature extractor to effectively suppress noise, while Kalman Filtering contributes adaptive noise tracking to refine the denoising procedure. Extensive studies were carried out to evaluate the efficacy of our strategy. on real-world seismic datasets. A comparative analysis was performed against conventional denoising algorithms are compared with Wavelet Transform (WT), Sparse Coding (SC), and Non-Local Means (N-LM). The results indicate that our proposed framework significantly outperforms these benchmark algorithms, achieving noteworthy improvements in accuracy, noise reduction, and signal preservation (SP). Our approach demonstrated an accuracy enhancement of up to 94.33% compared to existing methods, showcasing its superior performance in preserving crucial seismic signal information. This study not only advances seismic data processing but also provides a practical solution for reliable signal interpretation in exploration activities. The synergistic combination of Denoising CNN and Kalman Filtering establishes a potent strategy for superior seismic noise suppression, offering promising prospects for enhancing the overall quality of seismic data analysis.

关键词： Accuracy Filtering Geology Computational modeling Noise reduction Noise Earthquakes Feature extraction Convolutional neural networks Kalman filters

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Solving Relative Measurements on Finite Graphs

Solving Relative Measurements on Finite Graphs

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IEEE SENSORS

作者： Titan Yuan Kristofer S.J. Pister Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Sensor networks often rely on relative, or differential, measurements between sensing nodes due to the absence of an absolute reference at each node. This distributed measurement model can be represented as a graph, where each node denotes an absolute “potential” to be estimated and each edge corresponds to a measurement of the relative potential difference between the two incident nodes. Solving for the absolute node potentials can be formulated as a matrix equation involving the graph's Laplacian matrix. For finite graphs where all of the relative measurement processing is handled by a single node, we propose a modified iterative Jacobi algorithm with a priority queue to solve for the node potentials. Using the eigendecomposition of the Laplacian matrix, we then derive an expression for the error variance of the estimated node potentials and show how it can be adapted for different boundary conditions.

关键词： Jacobian matrices Laplace equations Measurement uncertainty Boundary conditions Mathematical models Iterative algorithms Sensors

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