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Development of Multi Objective Approach for Validation of PID Controller for Buck Converter 1

Development of Multi Objective Approach for Validation of PI...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Arun Kumar, K. Vegineni, Gopi Chand Suresh, C. Chowdari Marella, Bhagath Chandra Addanki, Sireesha Chimwal, Palak Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Samayapuram621112 India Insights & Data Capgemini Department of Financial Services India System soft Technologies LLC India Bipin Tripathi Kumaon Institute of Technology Department of Electrical Engineering Almora Dwarahat India

ISBN: (纸本)9798331518578

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was evaluated. The results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness. © 2025 IEEE.

关键词： Buck converter

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IPDS: A semantic mediator-based system using Spark for the integration of heterogeneous proteomics data sources

IPDS: A semantic mediator-based system using Spark for the i...

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作者： Messaoudi, Chaimaa Fissoune, Rachida Badir, Hassan System and Data Engineering Team Abdelmalek Essaadi University Tangier Morocco

With the constant rise of data volumes in many disciplines, various new Big data management systems have emerged to provide scalable tools for efficient data integration, processing, and analysis. In this article, we provide an overview of biomedical data integration systems focusing on ontology-based semantic systems and Big data technologies based systems such as Apache Spark. We also propose a new semantic data integration system, called Integrated Proteomics data system (IPDS), which uses a mediator approach. IPDS provides users a unified interface for query processing and data exploration. This system takes advantage of the Apache Spark framework to perform the query transformation and execution needed to question the integrated data sources. We develop a domain ontology that allows the user to formulate its queries in terms defined in the ontology. IPDS is a case study of semantic proteomics data integration linking four data sources UniProt (protein annotation), String (protein-protein interaction), PDB (protein structure), and Pubmed (biomedical citation). © 2020 John Wiley & Sons, Ltd.

关键词： data integration

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics 1

A Comparative Analysis of Artificial Intelligence and Busine...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Marella, Bhagath Chandra Chowdari Vegineni, Gopi Chand Addanki, Sireesha Ellahi, Ehsan Arun Kumar, K. Mandal, Rishabh Insights & Data Department of Financial Services Capgemini India Enrollment and Eligibility Team India System soft Technologies LLC India Cleveland State University Department of Information Systems OH United States Tula's Institute Department of Computer Science and Engineering UK Dehradun India India

ISBN: (纸本)9798331518578

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system (92.6%) and the neural network (NNs) models (92.7%). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans. © 2025 IEEE.

关键词： data accuracy

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Faster R-CNN assessment for air bubbles detection in the conformal coating application

Faster R-CNN assessment for air bubbles detection in the con...

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2021 International Conference on Microelectronics, ICM 2021

作者： Zouhri, Nizar El Mourabit, Aimad El Abidine, Alaoui Ismaili Zine Ensa. Abdelmalek Essaâdi University System Data Engineering Team Tangier Morocco University Mohammed v Information Communication and Embedded Systems Team Rabat Morocco

ISBN: (纸本)9781665408394

The detection of defects in Printed Circuit Boards (PCB) during the assembly process is an important quality requirement for the electronic *** present paper emphasizes the assessment of computer vision implying Faster-RCNN object detection architecture, with the aim of air bubbles defects detection in PCB following the conformal coating *** image configurations have been used to increase artificially the training model's performance, such as air bubbles size, location (on a flat PCB's surface, between components leads) and illumination, *** reaching this cap, we used a random pattern of choice regarding the images with different configurations to properly evaluate its performance, especially the accuracy, precision and *** results showed that Faster-RCNN delivers the lowest detection performance for the air bubbles located between components leads compared to the ones located on flat PCB *** paper shows the accuracy, precision and sensitivity performance results by applying the Faster-RCNN object detection architecture for air bubbles defect detection in the conformal coating application, with aim of providing reference for future research in the field of air bubbles detection. © 2021 IEEE.

关键词： Object detection

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Clock Gating Efficiency and Impact on Power Optimization during Synthesis Flow

Clock Gating Efficiency and Impact on Power Optimization dur...

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2021 International Conference on Microelectronics, ICM 2021

作者： Attaoui, Yassine Chentouf, Mohamed Ismaili, Zine El Abidine Alaoui El Mourabit, Aimad Siemens EDA/CSD Calypto-Synthesis Solutions Rabat10010 Morocco Team Rabat10010 Morocco Abdelmalek Essaâdi University System Data Engineering Team. ENSA of Tangier Morocco

ISBN: (纸本)9781665408394

Nowadays, power optimization has become an important factor in VLSI design and various low power optimization techniques are being developed. Clock-Gating is considered one of the widely used techniques in VLSI power optimization. Gating the Clock path results in saving power by reducing wasted capacitances switching due to unnecessary activity in logic module paths, thus resulting in more wasted Switching power. Clock gating is not always beneficial, it presents some pitfalls and fallacies within its implementation in various VLSI designs, as well as it doesn't suit all kinds of VLSI logic circuits. In this paper, we measure the impact of the Clock-Gating technique on power as well as on design's performance. We have performed two separate trials, using 112 industrial designs from different technologies, the former trial enables Clock Gate insertion, and the latter does not insert Clock Gate cells in the design's circuitry. We intend to measure the impact of Clock Gating on power by measuring the Power variation and comparing metrics such Instance number Buffer Inverter, Combinational blocks as well as QoR metrics such Runtime, wire-length, TNS, and Area. Results of this experiment showed that design circuits having Memories (SRAM, DFF, FIFO..) as dominant RTL modules, have the most impact on power when CG cells are inserted. Vice-versa power is less impacted by CG cells insertion for designs that contain microprocessors and datapath blocks as dominant RTL instances. © 2021 IEEE.

关键词： Clocks

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Development of Multi Objective Approach for Validation of PID Controller for Buck Converter

Development of Multi Objective Approach for Validation of PI...

引用

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Arun Kumar K Gopi Chand Vegineni C. Suresh Bhagath Chandra Chowdari Marella Sireesha Addanki Palak Chimwal Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Samayapuram Trichy Department of Financial Services Insights & Data Capgemini India Principal software developer System soft Technologies llc Information Technology India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was *** results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness.

关键词： Buck converters Regulators Machine learning algorithms Uncertainty Heuristic algorithms Velocity control Power system stability Real-time systems Voltage control Optimization

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A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big data Analytics

A Comparative Analysis of Artificial Intelligence and Busine...

引用

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Bhagath Chandra Chowdari Marella Gopi Chand Vegineni Sireesha Addanki Ehsan Ellahi Arun Kumar K Rishabh Mandal Department of Financial Services Insights & Data Capgemini India Enrollment and Eligibility Team India System soft Technologies llc Information Technology India Department of Information Systems Cleveland State University Ohio United States Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system ( $\mathbf{92.6 \%}$ ) and the neural network (NNs) models ( ${92}.7 \%$ ). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans.

关键词： Radio frequency Machine learning algorithms Accuracy Artificial neural networks Big data Market research Classification algorithms Business intelligence Random forests Recruitment

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Towards Demystifying Android Adware: dataset and Payload Location

Towards Demystifying Android Adware: Dataset and Payload Loc...

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IEEE/ACM International Conference on Automated Software engineering - Workshops (ASE Workshops)

作者： Chao Wang Tianming Liu Yanjie Zhao Lin Zhang Xiaoning Du Li Li Haoyu Wang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China The National Computer Emergency Response Team/Coordination Center of China (CNCERT/CC) Beijing China Beihang University Beijing China

ISBN: (数字)9798400712494

ISBN: (纸本)9798331523169

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices.

关键词： Prevention and mitigation Ecosystems Loading Pressing Regulation Mobile security Security Advertising Payloads Software engineering

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Online News Extraction and Multiclass Classification Using Linear Support Vector Machines

Online News Extraction and Multiclass Classification Using L...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Apoorva Gupta Smriti Arora Niyati Baliyan Data Science Team Barclays UK Knutsford England Computer Science and Infromation System Birla Institute of Technology and Science Pilani India Computer Engineering Department National Institute of Technology Kurukshetra Kurukshetra India

Online news articles, blogs, sites are a rich source of diverse text-based data. However, the data contained in these sources cannot be manually extricated, recorded, and listed because it comes in colossal size. Accurate mapping of precise news into their corresponding category is challenging in these times. Several methods have been proposed over time for news classification when training documents for each predefined class are present readily, however such methods were tried and tested upon a small dataset. With the underlying research, the aim is to propose a method that can be used when lakhs and lakhs of instances are present. This research analysis involves the task of news classification using multiclass classifiers - OneVsRest and OneVsOne classifiers over the Linear Support Vector Classification to learn the performance of multiclass news categorization. The proposed methodology “Keyword Based Classification Technique (KBCT)” in this study was executed and concluded using Python and deployed using Google Colaboratory. The result was expressed using four distinguished news classes over a multivariate dataset of 422419 instances from the uci-news-aggregator dataset. The OneVsRestClassifier's accuracy was computed to be 95.76% that was 0.09% more than the OneVsOneClassifier's accuracy of 95.67%. The proposed prototype was compared with some of the related studies and algorithms, and the outcomes produced by the OneVsRest model were the most optimum in terms of accuracy.

关键词： Training Prototypes Support vector machine classification Organizations Search engines Real-time systems Internet

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Faster R-CNN assessment for air bubbles detection in the conformal coating application

Faster R-CNN assessment for air bubbles detection in the con...

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International Conference on Microelectronics, ICM

作者： Nizar Zouhri Aimad El Mourabit Alaoui Ismaili Zine El Abidine System & Data Engineering Team Ensa. Abdelmalek Essaâdi University Tangier Morocco Information Communication and Embedded Systems Team University Mohammed V Rabat Morocco

ISBN: (纸本)9781665408400

The detection of defects in Printed Circuit Boards (PCB) during the assembly process is an important quality requirement for the electronic *** present paper emphasizes the assessment of computer vision implying Faster-RCNN object detection architecture, with the aim of air bubbles defects detection in PCB following the conformal coating *** image configurations have been used to increase artificially the training model’s performance, such as air bubbles size, location (on a flat PCB’s surface, between components leads) and illumination, *** reaching this cap, we used a random pattern of choice regarding the images with different configurations to properly evaluate its performance, especially the accuracy, precision and *** results showed that Faster-RCNN delivers the lowest detection performance for the air bubbles located between components leads compared to the ones located on flat PCB *** paper shows the accuracy, precision and sensitivity performance results by applying the Faster-RCNN object detection architecture for air bubbles defect detection in the conformal coating application, with aim of providing reference for future research in the field of air bubbles detection.

关键词： Training Computer vision Sensitivity Printed circuits Lighting Object detection Computer architecture

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