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Analysing Artificial Intelligence's Effect on Improving Electrical Engineering Management and Education 1

Analysing Artificial Intelligence's Effect on Improving Elec...

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

作者： Chawla, Namita Rane, Dattatraya Pandurang Nath, Reena Partha Anute, Nilesh Bhinge, Monalisa Gawhale, Sumita MCA PCET's Pimpri Chinchwad University Pune India SNDT Women's University's Maharshi Karve Model College for Women Maharashtra Shriwardhan Raigad India Sinhgad Institute of Business Administration and Computer Application Lonavala Pune India ASM'S Institute of Business Management and Research Pune India Sinhgad Institute of Mangaement Pune India

ISBN: (纸本)9798331518578

In the field of electrical engineering, artificial intelligence (AI) has become a game-changing instrument that is transforming management and education. This study examines the various ways AI can improve project management, educational strategies, and electrical engineering procedures. Through the use of predictive analytics, resource allocation optimisation, and real-time electrical system recording, AI-powered systems in management improve decision-making. These elements ensure the drawn-out execution of undertakings, enormously decline margin time, and increment functional viability. Through virtual labs, modern showing structures, and customized opportunities for growth, simulated intelligence driven advancements are reforming instructing and learning draws near. Calculations for versatile learning fulfill the needs of every individual student and give a more profound understanding of complex electrical designing thoughts. Moreover, by giving understudies genuine encounter without the limits of actual gear, virtual labs and recreation apparatuses assist with shutting the hole among hypothetical comprehension and certifiable application. To completely use computer-based intelligence, this concentrate likewise examines issues that should be settled, including information insurance, moral worries, and the abilities hole. Electrical designing can possibly arrive at up 'til now unfathomable degrees of imagination, effectiveness, and availability through the joining of man-made intelligence in administration and training. The outcomes feature how significant computer-based intelligence will be in moving electrical designing improvements later on, making the way for additional shrewd frameworks and profoundly qualified specialists. A worldview change is shown by the joint effort among artificial intelligence and electrical designing administration and schooling, which will make the field more powerful, versatile, and in accordance with the necessities of a world that

关键词： Resource allocation

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Analysing the Relationship Between CSR and Financial Performance Using AI and Machine Learning Techniques 1

Analysing the Relationship Between CSR and Financial Perform...

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

作者： Moholkar, Nilambari Duggal, Meenakshi Ingale, Devyani Awate, Sadhana Ganesh Raje, Harshal Dande, Mangesh Purushottam Dr.D.Y.Patil Institute of Technology Pimpri Pune India JSPM's Rajarshi Shahu college of Engineering Pune India RMDSSOMS Pune India Sinhgad Institute of Business Administration and Computer Application Lonavala India Global Business School and Research Centre Dr. D.Y Patil Vidyapeeth Maharashtra Pimpri Pune India Indira School of Business Studies PGDM Pune India

ISBN: (纸本)9798331518578

Corporate Social Responsibility (CSR) has arisen as a significant subject in contemporary business, with many organizations carrying out CSR techniques to fulfil partner assumptions and work on their standing. But since the monetary impacts of CSR drives are as yet indistinct, scholastics are investigating the way in which they connect with monetary execution. With regards to overseeing unpredictable, non-direct associations, conventional insightful procedures are restricted. New advancements in computerized reasoning (simulated intelligence) as well as AI (ML) give solid instruments to more exact examination of CSR and monetary execution. With an accentuation on anticipated exactness, cognizance, and reasonable consequences, this study investigates the manners by which man-made intelligence and ML approaches may be utilized to look at the association between monetary execution and CSR drives. In every industry, the Keras-trained MLP consistently yielded favourable results. The target of 900 epochs, or MLP (900), was used to train the MLP. It illustrates how training the same model over prolonged periods of time greatly improves performance. The LSTM model does not seem to generate better results, despite showcasing exceptional accuracy in natural language processing and long log security-related problems. As per the discoveries, artificial intelligence driven examination can give definite bits of knowledge into how corporate social responsibility (CSR) influences firm productivity, helping organizations in going with informed choices on their CSR strategies. © 2025 IEEE.

关键词： Artificial Intelligence CSR Financial Performance Machine Learning

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Analysing Artificial Intelligence's Effect on Improving Electrical Engineering Management and Education

Analysing Artificial Intelligence's Effect on Improving Elec...

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

作者： Namita Chawla Dattatraya Pandurang Rane Reena Partha Nath Nilesh Anute Monalisa Bhinge Sumita Gawhale MCA PCET's Pimpri Chinchwad University Pune SNDT Women's University's Maharshi Karve Model College for Women Shriwardhan Raigad Maharashtra India Sinhgad Institute of Business Administration and Computer Application Lonavala Pune ASM'S Institute of Business Management and Research Pune Sinhgad Institute of Mangaement Pune

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In the field of electrical engineering, artificial intelligence (AI) has become a game-changing instrument that is transforming management and education. This study examines the various ways AI can improve project management, educational strategies, and electrical engineering procedures. Through the use of predictive analytics, resource allocation optimisation, and real-time electrical system recording, AI-powered systems in management improve decision-making. These elements ensure the drawn-out execution of undertakings, enormously decline margin time, and increment functional viability. Through virtual labs, modern showing structures, and customized opportunities for growth, simulated intelligence driven advancements are reforming instructing and learning draws near. Calculations for versatile learning fulfill the needs of every individual student and give a more profound understanding of complex electrical designing thoughts. Moreover, by giving understudies genuine encounter without the limits of actual gear, virtual labs and recreation apparatuses assist with shutting the hole among hypothetical comprehension and certifiable application. To completely use computer-based intelligence, this concentrate likewise examines issues that should be settled, including information insurance, moral worries, and the abilities hole. Electrical designing can possibly arrive at up ‘til now unfathomable degrees of imagination, effectiveness, and availability through the joining of man-made intelligence in administration and training. The outcomes feature how significant computer-based intelligence will be in moving electrical designing improvements later on, making the way for additional shrewd frameworks and profoundly qualified specialists. A worldview change is shown by the joint effort among artificial intelligence and electrical designing administration and schooling, which will make the field more powerful, versatile, and in accordance with the necessities of a world that

关键词： Electrical engineering Productivity Training Technological innovation Gears Metaheuristics Lattices Turning Artificial intelligence Stress

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Analysing the Relationship Between CSR and Financial Performance Using AI and Machine Learning Techniques

Analysing the Relationship Between CSR and Financial Perform...

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

作者： Nilambari Moholkar Meenakshi Duggal Devyani Ingale Sadhana Ganesh Awate Harshal Raje Mangesh Purushottam Dande Dr.D.Y.Patil Institute of Technology Pimpri Pune JSPM's Rajarshi Shahu college of Engineering Pune RMDSSOMS Pune Sinhgad Institute of Business Administration and Computer Application Lonavala Global Business School and Research Centre Dr. D.Y Patil Vidyapeeth Pimpri Pune Maharashtra India Indira School of Business Studies PGDM Pune

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

关键词： Training Productivity Industries Ethics Instruments Organizations Machine learning Solids Natural language processing Long short term memory

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Gaurav Kumar G. Devayani C Saraswathi R. Melba Kani Sandeep Dongre M. S. Mohamed Mallick School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Master of Business Administration Mar Ephraem college of Engineering and Technology Kanyakumari India Symbiosis Institute of Business Management (SIBM) Constituent of Symbiosis International (Deemed University) Nagpur India Department of Computer Science Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences.

关键词： Consumer behavior Accuracy Data preprocessing Transforms Market research Electronic commerce Convolutional neural networks Integrated circuit modeling Artificial intelligence Long short term memory

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An empirical study of test cases in software testing

An empirical study of test cases in software testing

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Navnath Shete Avinash Jadhav Sinhgad Institute of Management Pune Sinhgad Institute of Business Administration & Computer Application Pune

Software is a set of instructions executed by a computer which are designed to perform a particular task. Software Development Life Cycle is used to develop the software. Software Testing is the important phase of software development life cycle (SDLC). Software testing is a part of SDLC. Testing fulfills the customer's requirement. In addition to that testing process finds and removes the bugs of the software. Developing organization tries to show that the software they developing is quality software and process used to develop as well as to test software are quality processes. To test any software, tester writes test cases based on Software Requirement Specification (SRS). SRS contain all the functional and non-functional requirements of the software. Individual component (Unit) requirement specifications are written in detail. Test engineer and / or Tester used SRS to write test cases. Test cases are used to test the software thoroughly in manual testing. All small loop holes of the software could be identified by test cases. This paper focus on the significance of test cases and their role to test software used in IT industries. The researcher has concluded that without test cases testing would not be possible.

关键词： Software Software testing computer bugs Graphical user interfaces Educational institutions Customer satisfaction

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Choosing right database system: Row or column-store

Choosing right database system: Row or column-store

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Anuradha S. Kanade Arpita Gopal Sinhgad Institute of Management and Computer Application India Sinhgad Institute of Business Administration and Research Kondhwa India

The new generation of applications like business Intelligence, data warehouses, Social networking etc. requires processing of terabytes or petabytes of data. This is achieved by distributed processing. This is the major reason for the power of web companies such as Google, Amazon, and Salesforce etc. In this case, relational databases are found to be inadequate in distributed processing involving very large number of servers and handling big data applications. The systems like columnar database have attempted to address the problems in storing information using traditional database system. The database system design should be chosen to handle large pool of data to get analytical performance. The present study focuses on identifying the problems in the relational database system and highlighting the benefits gained by usage of columnar database system in area of business intelligence or data warehouse systems.

关键词： Servers business Data warehouses Relational databases Indexes

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An Incentive Mechanism Considering Privacy Metric for Personalized application

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IEEE Transactions on Consumer Electronics 2025年

作者： Xie, Yun Li, Peng Zhang, Jindan Attar, Razaz Waheeb Arya, Varsha Gupta, Brij B. Nanjing Vocational College of Information Technology Jiangsu Province Service Customization Network Application Engineering Research Center Jiangsu Province Service Customization Network Application Engineering Research Center Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Xianyang Polytechnic Institute China Princess Nourah bint Abdulrahman University College of Business Administration Management Department Riyadh11671 Saudi Arabia Hong Kong Metropolitan University Hong Kong Hong Kong Center for Interdisciplinary Research Dehradun India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune India Korea University School of Cybersecurity Seoul Korea Republic of Kyung Hee University Kyungheedae-ro Seoul26 Korea Republic of

Edge-based federated learning (EFL) is a solution for personalized applications. It addresses the limitations of cloud-based consumer service systems, such as data privacy and network latency. To further enhance the EFL system’s ability to protect data privacy, differential privacy (DP) is introduced. It is crucial to motivate high-quality collaboration among participants for optimal performance of the DP-EFL system. Researchers primarily focus on efficient learning algorithms, optimized resource scheduling, and user-oriented incentive mechanisms. Existing research exhibits notable limitations in modeling and analyzing the heterogeneity of resource costs (e.g., communication costs), particularly failing to comprehensively account for the additional costs imposed by participants’ privacy protection mechanisms‌. To address this gap, we propose a novel market transaction model based on Stackelberg game theory. This framework integrates incentive-driven reward allocation mechanisms to rigorously investigate the collaborative behaviors and strategic interactions between edge nodes (ENs) and the cloud server (CS)‌. We incorporate privacy costs into the multidimensional costs of participants with privacy metric, and based on this, derived collaboration strategies between ENs and CS, thus solving the Stackelberg game problem by theoretical analysis. Furthermore, numerical experiments validated the rationality of our proposed model, the impact of strategies on the performance of collaborators, and the advantages over other incentive mechanisms. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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A Computational study of pre-synaptic re-uptake of dopamine on phosphorylation of DARPP-32

A Computational study of pre-synaptic re-uptake of dopamine ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shesharao M. Wanjerkhede Raju S. Bapi Sinhgad Institute of Business Administration and Computer application Pune India University of Hyderabad Hyderabad India

The activity of mid brain dopamine (DA) neurons resembles the reward prediction error signal of the temporal difference (TD) model. Dopamine acting at D1 receptor activates adenylate cyclase through the coupling of D1 receptors with G s/olf . The complex of adenylate cyclase activates and increases the intracellular levels of cAMP and thereby stimulating cAMP dependent protein kinase and increasing the state of phosphorylation of DARPP-32 (dopamine and cyclic AMP-regulated phosphoprotein, M, = 32,000). DARPP-32 plays an important role in autophosphorylation of calcium calmodulin dependent protein kinaseII (CaMKII) which is crucial for prolongation of long term potentiation (LTP). Here we verified the effect of pre-synaptic re-uptake of dopamine by dopamine transporter on the phosphorylation of DARPP-32 and found that the phosphorylation of DARPP-32 is unaffected by pre-synaptic re-uptake. Further we have seen that the DARPP-32 phosphorylation does not change with the change in the DA or D1R concentrations.

关键词： Neurons Calcium Proteins Neurotransmitters Brain modeling Predictive models Timing Biochemistry Biological neural networks computer networks

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