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A Hybrid Deep Learning Models: Optimization for Forecasting Wireless 6G IoT Network 1

A Hybrid Deep Learning Models: Optimization for Forecasting ...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, B.P. Venkadeshwaran, Kuthalingam Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar District India

ISBN: (纸本)9798331518578

New specifications for 6G mobile networks will surpass the performance targets of 5G networks. As more 5G networks are deployed, their limits become obvious, encouraging the exploration of networks using 6G as the future solution. This paper covers 6G technology's core privacy and security concerns. Monitoring real-time structures need secured wireless sensor networks (WSNs). Denial of service (DoS) outbreaks increase secure risks for WSNs and potentially undermine the whole system. This study presents a new approach to control and optimize the security of 6G wireless networks based on blockchain technology by means of a machine learning method. Utilizing a blockchain users datagram transit protocol in conjunction with reinforcement projection regression, this research manages security of organized 6G wireless sensor net. The network is then optimized via artificially democratized cuckoo glowworm remora optimizations. Numerous network characteristics pertaining to accuracy, end-to-end latency, packet delivery ratio, energy efficiency, and throughput have formed the basis of the simulation findings. Reduced network traffic may be achieved by figuring out which node and route are best for data transfer. 98% of Throughput, energy effectiveness of 96%, 97% accuracy, end to end latency of 51%, and packet delivery ratio of 95% were all achieved by the suggested method. © 2025 IEEE.

关键词： 5G mobile communication systems

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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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Sustainable Civil engineering Practices: Implementing Green Building Technologies with AI Assistance 1

Sustainable Civil Engineering Practices: Implementing Green ...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Monisha, S. Suriya, Pa. Johny, Sujith Biju, Abel Scaria, Neol M Aarupadai Veedu Institute of Technology Department of Civil Engineering Paiyanur Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

Due to its substantial energy use, greenhouse gas emissions, and resource depletion, the construction sector is one of the biggest causes of environmental deterioration. Sustainable building techniques have become a vital strategy for mitigating these issues and lowering the environmental impact of civil engineering projects. Green buildings are ones that preserve or enhance the local standard of living via the use of sustainable construction techniques. Artificial Intelligence (AI) in Green Construction is used to analyse data collected from monitoring the construction site and utilising predictive analytics to make decisions that impact a project's quality, safety, profitability, and timeline. The goal of the planned study was to assess the potential for AI in Green Building Construction (AI-GBC) to lower utility costs and carbon emissions. RF and GA are used by artificial intelligence to lower carbon dioxide emissions and energy consumption. The accuracy of the AI-GBC is assessed using a number of statistical measures, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Mean Squared Log Error (RMSLE). With prediction accuracies over 96%, both Machine Learning (ML) models produced favourable outcomes. With an R2 of 0.96, GA models performed rather well in terms of predicting Co_2. 98% will undertake a k-fold cross-validation analysis, and 96% will finish a performance analysis. In order to avoid overfitting and guarantee the accuracy of the results obtained from the extended modelling technique, cross-validation is employed. © 2025 IEEE.

关键词： Green buildings

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Enhanced DeepFake Detection Using CNN and EfficientNet-Based Ensemble Models for Robust Facial Manipulation Analysis 1

Enhanced DeepFake Detection Using CNN and EfficientNet-Based...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Das, Subhranil Kumari, Rashmi Tripathi, Utkarsh Parashar, Shivi Dubey, Adetya Yadav, Akanksha Singh, Raghwendra Kishore Dehradun India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798331518578

Deepfakes are generally powered by Generative adversarial networks (GANs) that has been newly introduced a new dimension of realism in the manipulation of digital media particularly in some delicate features such as eyes, lips, and skin texture. These sophisticated manipulations pose significant threats to digital security, privacy, and trust in media. Detecting these alterations, especially in still images, has become a critical focus of research. This paper presents a Convolutional neural network (CNN)-based approach to detecting facial feature manip- ulations in deepfake images. Leveraging CNN architectures like ResNet, we focus on identifying subtle pixel-level inconsistencies in facial regions. Preprocessing steps, including face cropping, alignment, and normalization, are applied to enhance the model's focus on manipulated features. Datasets such as Face Forensics++ are utilized for training and evaluation. Our model demonstrates a high level of accuracy in distinguishing real from fake images based on facial feature manipulations, outperforming baseline models in several key metrics, including precision and recall. The results highlight the effectiveness of CNNs in detecting fine- grained alterations and their potential for real-world application in combating deepfake threats. This research contributes to the ongoing efforts in deepfake detection by proposing a robust and scalable method to detect facial manipulations, ultimately enhancing the security and authenticity of digital media. Future work will explore the integration of multimodal detection tech- niques to further improve the robustness of the system. © 2025 IEEE.

关键词： Generative adversarial networks

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Anti-Spoofing: A Bio Finger Print Recognition Method using Pattern Matching Algorithm

Anti-Spoofing: A Bio Finger Print Recognition Method using P...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Harshvardhan, A.S. Prasad, Kokkula Shiva Sindhuja Reddy, A. Rahul, N. Rutvik Varasai, P. Srinivas, B. Nalla Malla Reddy Engineering College Department of Aids Telangana Hyderabad India Nalla Malla Reddy Engineering College Department of Ece Telangana Hyderabad India

ISBN: (纸本)9798331529833

Biometric authentication plays a key role in smart environments, with finger print recognition being one of the most common methods. Fingerprint readers scan and store fingerprint data, comparing it during login attempts to verify identity. While effective, these systems are vulnerable to spoofing using materials like wax, clay, or gelatine. Criminals sometimes exploit this technology to access personal information. Two main algorithms, minutiae matching and pattern matching, are used for recognition. Minutiae matching compares unique points on fingerprints, while pattern matching analyzes surface thickness, curvature, and ridge patterns. However, injuries to the fingercan make identification challenging. advances now integrate sensors to detect pulse and sweat glands, enhancing security. Fingerprint recognition is widely employed in legal systems for identity management, but the technology relies heavily on intact surface patterns for accurate matching. © 2025 IEEE.

关键词： Palmprint recognition

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5G to 6G Shaping and Effect of the Wireless Data Transmission 1

5G to 6G Shaping and Effect of the Wireless Data Transmissio...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Walia, Anita Das, Neeraj Department of CETT Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

Digital advancement and smart production have been made possible via artificial intelligence (AI) and computing in the cloud (CC) because of the fifth-generation wireless network (5G), which is a key enabler of the fourth industrial revolution. Nevertheless, beyond 5G (B5G) is seen as a watershed moment that will radically alter current worldwide tendencies in mobile phone practices and impact the lives of many. Physical and digital worlds will eventually merge, according to B5G. The focus of this research is on the future of technology for wireless communications, specifically the evolution from 5G into 6G. Still, mankind hasn't arrived to the age of instantaneous contact with aliens, lightning-fast internet, plus zero latency, despite a number of advancements. In order to accomplish these more elevated goals, this article investigates the potential obstacles and hurdles that the shift from 5G to 6G may encounter. The article lays out the plan for 6G, the enabling infrastructures, the problems, and the research that will lead to the realization of the 'technology for humanity' goal and improved assistance for the poor. © 2025 IEEE.

关键词： 5G mobile communication systems

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An M/M/s/c Traffic Congestion Queuing Model for Toll Plazas Employed with Artificial Neural Network (ANN) 1

An M/M/s/c Traffic Congestion Queuing Model for Toll Plazas ...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Ujarari, Chandan Singh Chaube, Shshank Bhadula, Rakesh Chandra Kumawat, Yaghvendra Graphic Era Hill University Department of Mathematics Uttarakhand Dehradun248002 India Telangana Hyderabad509217 India Jaipur Engineering College and Research Centre Department of Mathematics Rajasthan Jaipur India

ISBN: (纸本)9798331518578

In recent time traffic congestion in the road system is a very big problem for vehicles. It is seen that many toll plazas are highly loaded with traffic and hence an appropriate analysis under queuing theory is required. At the toll plazas vehicles wait before paying the toll which causes long queues and unnecessary delays for which we develop a mathematical model for the management of traffic intensity. In this study, we analyze the system performance in the context of the parameters: average queue length, average waiting time, and total time spent in the system, and we discuss also how arrival rate, service rate, and number of servers affect queue length and waiting time. Moreover, we evaluate the lane-jockeying probability of vehicles in this study by adopting the M/M/c/c queuing model. The novelty of the study is to verify the lane-jockeying behaviors existing in the traffic system by using the 'Levenberg-Marquardt Backpropagation' training algorithm in ANN. © 2025 IEEE.

关键词： Traffic congestion

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AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud Computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

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2025 international conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Tidke, Bharat Pragadeeswaran, S. Lakshmi, R. Poornima Bhuvaneswari, G. Manikandan, G. Murugan, S. University Nagpur Campus Pune India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering college Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Kavaraipettai Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities. © 2025 IEEE.

关键词： Sustainable city

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Reimagining Mythology with Virtual Reality: Postmodernist Techniques for Immersive Retellings in Educational Platforms 1

Reimagining Mythology with Virtual Reality: Postmodernist Te...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Subramanian, M. Gupta, Rajeev Kumar Vijay Karthic, R. Jadhav, Kaustubh Pandurang Tabuena, Almighty Cortezo Tabuena, Yvon Mae Hilario Madurai India Allenhouse Business School Kanpur India St. John's College Affiliated to Manonmaniam Sundaranar University Department of English Palayamkottai Tirunelveli India Pimpri Chinchwad College of Engineering Department of General Science and Humanities Pune India Philippines St. Stephen's High School Manila Philippines

ISBN: (纸本)9798331518578

Traditional narratives has been redefined by virtual reality that transform the way of experiencing mythology through interactive and immersive storytelling. The study explores the postmodernist techniques including nonlinear storytelling, intertextuality and fragmentation that can be applied for retelling in educational platforms for mythological stories. Ability of virtual reality in multi-sensory engagement reimagine the mythologies to provide personalised and participatory experience to the learners and fosters deep critical thinking and cultural understanding. The study examines the role of virtual reality in re-imagining mythology in educational platform through analysis of various existing platform and case studies to integrate AI-driven interactions, adaptive narratives and gamifications. The study findings show the digital retelling the postmodernist techniques with virtual reality. VR act as a powerful tool in modernised myth making positioned with dynamic medium to bridge the past, present and future using immersive learning experience. © 2025 IEEE.

关键词： Virtual environments

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Optimization of Electric Discharge Machining Process Parameters Using Simulated Annealing Optimization Algorithm 1

Optimization of Electric Discharge Machining Process Paramet...

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1st international conference on advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Gaur, Harshit Kumar, Ravi Kumar, Vinod Upreti, Manish Kandpal, Ankit Rikhari, Priyanshu Bipin Tripathi Kumaon Institute of Technology Department of Mechanical Engineering Dwarahat India Bipin Tripathi Kumaon Institute of Technology Department of Electronics & Communication Engineering Dwarahat India

ISBN: (纸本)9798331518578

The Simulated Annealing (SA) Optimisation Algorithm is used in this project to optimize the Electrical Discharge Machining process parameters. The primary goal is to improve the Material Removal Rate (MRR) while reducing Surface Roughness (SR), which are two essential performance indicators in EDM. Because MRR and SR are competing objectives, the optimisation attempts to strike a balance by selecting optimal parameter choices. The objective function is developed using Regression Analysis (RA) and the SA algorithm. When machining settings are optimised, MRR and SR show considerable improvements. The workpiece is made of AISI 4340 steel, and the electrodes are made of copper. The SA approach produced results that closely matched experimental data. A comparative analysis of outcomes from the Genetic Algorithm (GA), SA, and experimental methods is also presented, demonstrating the effectiveness of the proposed optimization strategy. © 2025 IEEE.

关键词： Simulated annealing

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