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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Mahesh, G.Sreeramulu Vijayalakshmi, B. Prakash, D. Nagesh, B.K. Dilli Babu, G. Gsss Institute of Engineering and Technology for Women Electrical and Electronics Engineering Karnataka Mysuru India Sri Parasakthi College for Women Department of Computer Science and Information Technology CourtallamTamilnadu Thenkasi India S.A. Engineering College Electronics & Communication Engineering Tamil Nadu Chennai India Reva University School of Electrical & Electronics Engineering Karnataka Bangalore India Sri Venkateswara College of Engineering Electrical & Electronics Engineering Tirupati India

ISBN: (纸本)9798331527822

This paper proposes a simulation of a 4-bus power system network, inspired by the example in 'Power System Analysis' by J.J. Grainger and W.D. Stevenson. The simulation incorporates dynamic pricing, demand response mechanisms, and power flow analysis using Pandapower, an open-source Python library for power systems modeling. The objective of this paper is to demonstrate how dynamic pricing and demand response can interact to balance generation and load in a power grid. By analyzing the effects of dynamic pricing on load profiles and generator outputs, this research aims to contribute to a deeper understanding of demand response strategies and their potential benefits in enhancing grid stability and efficiency. © 2024 IEEE.

关键词： Demand response

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Dual-Band Printed Antenna Design for Bluetooth and WLAN Applications 7

Dual-Band Printed Antenna Design for Bluetooth and WLAN Appl...

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Khan, Naveed Hamid Nawaz Feng, Botao Ding, Xiao Gu, Wenzhe Deng, Li Chung, Kwok L. Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Huizhou University School of Computer Science and Engineering Huizhou China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou516007 China

ISBN: (纸本)9798350384437

This study presents the fabrication process of a printed antenna on a FR-4 substrate with accurate measurements of 25×37×1 mm3. The antenna design, known for its simplicity, has four semicircles with four small square patches at each end. In addition, it includes two rectangular patches that are fitted with pin diode switches in order to enable the ability to operate in two different frequency bands. The antenna's ground has a carefully placed opening. The antenna being examined demonstrates resonance at both the frequencies of 2.4 GHz and 5 GHz, with reflection coefficients of -19.6 dB and -16.5 dB correspondingly. The VSWR for both working frequencies is below 2, satisfying the requirements of Bluetooth and WLAN applications. The tiny size and creative design elements of this antenna allow for versatility and adaptation in many wireless communication systems. © 2024 IEEE.

关键词： Microwave antennas

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Autonomous and Adaptive communications Systems for Smart Home Applications 5th

Autonomous and Adaptive Communications Systems for Smart Hom...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Krishnamoorthy, Ramkumar Das, Neeraj Sharma, Khushboo Jindal, Kanika Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9789819780426

Smart home packages have become more and more standard in nowadays society, giving customers the potential to without problems connect and control various devices within their domestic. Self-sustaining and adaptive communication structures are crucial for smart home packages, allowing seamless integration of these devices and efficient electricity intake. Self-sufficient and adaptive verbal exchange structures for smart home applications. It examines exclusive existing methods for clever domestic automation as well as discusses techniques inclusive of dispensed computing, simulated annealing, reinforcement studying, and game idea. Eventually, it discusses capability destiny tendencies and advances in self-sufficient and adaptive communication systems that would improve the user revel in of smart home applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart homes

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A Smart Irrigation System Powered by IoT and Machine Learning for Optimal Water Management 4

A Smart Irrigation System Powered by IoT and Machine Learnin...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Dhyani, Ritika Manne, Nikita Garg, Jyoti Motwani, Deepak Shrivastava, Ashok Kumar Sharma, Meenakshi Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Cvr College of Engineering Department of Csit Hyderabad India Ims Engineering College Department of Information Technology Ghaziabad India Amity University Department of Computer Science & Engineering Gwalior India Inderprastha Engineering College Department of Electronics & Communication Engineering Ghaziabad India

ISBN: (纸本)9798350360165

In this innovative quest for more efficient irrigation, we investigate the combined use of sensor technology and machine intelligence to optimise agricultural water usage. We collect precise data on essential variables, including water flow, temperature, humidity, soil moisture, and water level, every 15 days by promptly deploying sensors in the field to monitor them in real-time. We use the vast amount of data available to us to train machine learning models, including Recurrent Neural Networks (RNN), K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), specifically for streams that are anticipated to have a certain distribution. CNN has exceptional precision with a decoding accuracy of 94.5% in visual signal interpretation, surpassing ANN, KNN, and RNN which achieve lower accuracies of 91.1%, 88.7%, and 83.6% respectively. Our investigation uncovers a fluid interaction between sensor data and model training, leading to each model exhibiting unique characteristics. These findings not only showease the accuracy with which machine learning can distribute water, but they also signify a noteworthy progress in resource-efficient and sustainable agriculture. In addition to generating mathematical discoveries, our research establishes the foundation for advanced irrigation systems that smoothly integrate mechanical and natural processes in agricultural fields. © 2024 IEEE.

关键词： Soil moisture

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The three dimensional dosimetry imaging for automated eye cancer classification using transfer learning model 14

The three dimensional dosimetry imaging for automated eye ca...

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14th International Conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Mohammed, Murtuza Ali Mohammed, Vazeer Ali Ramakrishnan, Rakesh Mohammed, Mehmood Ali Logeshwaran, J. Maheswaran, S. Lewis University Department of Computer & Information System IL United States University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

computer vision methods utilizing transfer learning can offer a promising approach for automated eye cancer diagnosis with three-dimensional dosimetry imaging. The latest advances in AI have encouraged researchers to use deep learning models for automated cancer classification. This paper presents a novel approach using three-dimensional dosimetry imaging for automated ocular cancer classification using transfer learning. The approach uses a combination of dataset augmentation methods. The approach is adapted from the popular Inception V3 CNN architecture. Two different feature extraction approaches, namely mean and maximum pooling, are compared while extracting features from the output of the CNN layers. The extracted features are fed to the classifier which uses the SVM with a linear kernel as its decision system. The effectiveness of this method is evaluated on a set of 20 healthy ocular images and 26 suspicious ocular images. The overall classification accuracy achieved with this approach is 94.85%. The results obtained demonstrate that this approach has a promising potential for automated ocular cancer classification with an appropriate transfer learning model. © 2023 IEEE.

关键词： Classification (of information)

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Advancements in Immunization Management for Personalized Vaccine Scheduling with IoT and Machine Learning 10

Advancements in Immunization Management for Personalized Vac...

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

作者： Madheswaran, M. Dhanalakshmi, R. Ramasubramanian, G. Aghalya, S. Raju, S. Thirumaraiselvan, P. Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Immunization is essential to public health;currently, comprehensive vaccine schedules may not sufficiently address individual health profiles. The Internet of Things (IoT) collects real-time health data, and decision trees (DTs) develop personalized vaccination schedules. Wearable health monitors and electronic health records provide continuous health data and information. DTs are customizable machine learning (ML) algorithms that assess this data and create immunization regimens. Based on age, vaccination history, underlying health issues, and real-time health measurements, DTs provides individualized vaccine recommendations. More vaccination compliance and greater health needs alignment are the system's main benefits. Based on real-time data, this technique recommends vaccination administration timings. Optimization of vaccination systems reduces vaccine-preventable illness transmission. Integrating IoT data, preprocessing, and vaccination scheduling using DTs are covered in this work. It emphasizes data security and ethics while managing sensitive health information during privacy and security issues. Personal vaccination scheduling using IoT and DTs advances public health. Regulations that protect privacy and provide equal access to personalized vaccination schedules are essential as the system matures. This novel method might save lives, save healthcare costs, and improve vaccination systems. © 2024 IEEE.

关键词： Vaccines

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Deep LearningModel for Big Data Classification in Apache Spark Environment

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Intelligent Automation & Soft Computing 2023年第9期37卷 2537-2547页

作者： T.M.Nithya R.Umanesan T.Kalavathidevi C.Selvarathi A.Kavitha Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTrichyTamilnadu620009India Department of Information and Communication Engineering Anna UniversityChennaiTamilnadu600025India Department of Electronics and Instrumentation Engineering Kongu Engineering CollegeErodeTamilnadu638060India Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarurTamilnadu639113India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTrichyTamilnadu620009India

Big data analytics is a popular research topic due to its applicability in various real time *** recent advent of machine learning and deep learning models can be applied to analyze big data with better *** big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classiﬁcation *** study proposes a new sigmoid butterﬂy optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classiﬁcation in Apache *** SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of *** addition,OGRU based classiﬁcation model is employed to classify the big data into appropriate ***,the hyperparameter tuning of the GRU model takes place using Adam ***,the Apache Spark platform is applied for processing big data in an effective *** order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.

关键词： Big data apache spark classiﬁcation feature selection gated recurrent unit adam optimizer

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Cross-Layer Design Strategies for Enhancing communication Quality 5th

Cross-Layer Design Strategies for Enhancing Communication Qu...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Naval, Preeti Sharma, Mohit Kumar Singh, Prasanna Kumar Prabhu, A. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Noida India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India

ISBN: (纸本)9789819780426

Go-layer layout (CLD) is an emerging implementation paradigm for communique networks in aerospace and space systems. CLD combines the functionalities of two or greater wonderful layers inside the protocol stack, thereby making an allowance for a greater efficient and powerful communique. In CLD networks, transmission parameters, routing algorithms, and visitors allocation selections are coordinated and optimized in a holistic way, in place of one at a time in exceptional layers. This enables, for example, the supply of novel strategies, along with cooperative transmission, coding, and remarks manipulate, for effective network optimization. CLD also can result in stepped forward performance in phrases of lower latency, fewer retransmissions, higher energy efficiency, and higher resource usage. On this paper we offer a top level view of the imminent demanding situations in CLD networks in aerospace and space systems. We speak a couple of CLD techniques, consisting of the ones based totally on community coding and queue management, and describe their advantages. Ultimately, we provide key instructions and recommendations for studies in CLD networks, together with a fixed of open studies troubles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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An innovation prediction of DNA damage of melanoma skin cancer patients using deep learning 14

An innovation prediction of DNA damage of melanoma skin canc...

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14th International Conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Ramakrishnan, Rakesh Mohammed, Mehmood Ali Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. Maheswaran, S. University of the Cumberlands Department of Information Technology Union CityCA United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Lewis University Department of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350335095

There are now a variety of intriguing options for the study of genetic data thanks to recent developments in artificial neural networks and deep learning. In this study, we use a deep learning-based prediction model to find possible DNA damage in individuals with melanoma skin cancer. We create a convolutional neural network (CNN) model to forecast the DNA damage susceptibility of melanoma cancer cells using a publically available genome sequencing dataset. This model preprocesses the genomic data, extracts features, and categorises them. Comparing the results of our CNN model with those of a traditional logistic regression model, we find that our CNN reported superior performance in identifying differences between healthy and cancerous samples with an accuracy of nearly 96%. The model can be used to augment the standard clinical diagnosis of melanoma, which only uses visual assessment and histology. By intervening sooner, clinicians can put forward more personalized and informed plans of care and surveillance for each melanoma patient, reducing medical costs and improving the quality of patient diagnosis. © 2023 IEEE.

关键词： Neural network models

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A Leaky-Wave Beam Scanning Antenna with PIN Diodes 7

A Leaky-Wave Beam Scanning Antenna with PIN Diodes

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Xiao, Gengbo Feng, Botao Ding, Xiao Deng, Li Chung, Kwok L. Gu, Wenzhe Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou516007 China Huizhou University School of Computer Science and Engineering Huizhou516007 China

ISBN: (纸本)9798350384437

In this paper, a reconfigurable digital modulation leaky-wave antenna (LWA) is proposed. This antenna does not require additional ADC/DAC modules and mixing circuits, which simplifies the antenna structure design. By introducing diodes, the proposed antenna realizes digital beam scanning and digital modulation of the leaky-wave. By inputting different digital codes, the multi-beam switching functionality is achieved, thereby realizing the purpose of beam reconfigurability. A low-cost and easy-to-manufacture metasurface radiator is fabricated, which can achieve the leaky-wave radiation effect. In summary, the proposed antenna is a promising candidate for realizing beam reconfigurability in future intelligent wireless communications. © 2024 IEEE.

关键词： Scanning antennas

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