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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M.S. Kavitha S. Bharathi S. Krishnakumari S. Vijay Shankar C. Thyagarajan Department ECE R.M.K. Engineering College Chennai Department of EEE S.A. Engineering College Chennai Department of ECE Loyola ICAM College of Engineering and Technology Chennai Department of Electrical and Electronics Engineering Sona College of Technology Salem Department of Computer Science Engineering Panimalar Engineering College Chennai

In disaster-stricken environments, the rapid and efficient rescue of individuals is of paramount importance. This abstract presents a novel approach to enhance disaster response efforts through the development of a Human Detection Robot (HDR) designed to locate and rescue people in disaster-affected areas. The proposed robot integrates Light Dependent Resistors (LDR) and the HC-SR501 Passive Infrared (PIR) sensor to detect the presence of human beings in low-visibility and challenging environments. The Human Detection Robot is equipped with a robust mechanical structure and a combination of sensors, including LDRs and the HC-SR501 PIR sensor. LDRs are employed to detect variations in ambient light, while the PIR sensor is used to detect human body heat and motion. These sensors work together to ensure reliable and accurate human presence detection, even in environments with low light conditions or obstructions such as dust, smoke, or debris. The robot's design also includes a microcontroller unit (MCU), which processes sensor data and controls the robot's movements. The MCU enables real-time decision-making and navigation to reach and rescue individuals in disaster-stricken areas. The robot is equipped with a communication system that allows it to relay information to the disaster response team, providing them with crucial data about the location and condition of the people in need of rescue. The HDR's rapid response time, when coupled with its robust mechanical design and communication system, positions it as a game-changing asset in disaster management and search and rescue operations.

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The Transformative Impacts of Artificial Intelligence on Education through Ethical Perspectives

The Transformative Impacts of Artificial Intelligence on Edu...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rakesh Ranjan Anish Kumar Vishwakarma Sundaram Nikhil Dhengre Sagar Motdhare Amarana Prabhakara Rao Department of ECE National Institute of Technology Patna Bihar India School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand Department of Electronics and Communication Engineering Government Engineering College Sheikhpura Bihar India Department of ECE Indian Institute of Information Technology Nagpur India Department of ECE Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Innovation has surged due to the emergence of artificial intelligence (AI), especially in the field of education. Traditional teaching methods are undergoing a profound transformation thanks to the influential algorithms driving AI. This technology extends far beyond the boundaries of education, penetrating various dimensions of our daily lives, enriching human activities and lifestyles. AI stimulation of human thinking and intelligence in a machine by performing tasks commonly recognized and associated with human intelligence and imagination. This research aims to synthesize diverse valuable insights of AI on educational environments. It emphasizes the ethical transition from traditional teaching and learning methods to more interactive human experiences. Furthermore, the research provides an overview of innovative elements, current challenges, concerns, and benefits of AI in education. It also highlights popular AI-powered educational tools and platforms. Remarkably, sophisticated AI platforms like ChatGPT, Squirrel AI, Google Bard (Gemini), and Grammarly take center stage. These systems, based on natural language models, can generate responses alike human interactions. The paper investigates the potential advantages of AI in education while addressing the ethical and practical considerations surrounding its implementation. By examining these technologies, the research aims to illuminate the path towards a more dynamic and effective educational landscape.

关键词： Learning systems Ethics Technological innovation Education Natural languages Internet Mentoring Artificial intelligence Predictive analytics Robots

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Near-Far Field Three-Stage Beam Training for RIS-Assisted Wideband OFDM Communications

Near-Far Field Three-Stage Beam Training for RIS-Assisted Wi...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Cheng, Zhichao Zhang, Shupei Fu, Shu Di, Boya School of Electronics Engineering and Computer Science Peking University Beijing China School of Electronics Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Beijing China Chongqing University Department of Microelectronics and Communication Engineering Chongqing China

ISBN: (纸本)9798350368369

Large-scale reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for signal strength enhancement and coverage extension in 6G networks. However, as the antenna scale and the bandwidth increase, large-scale RIS-assisted wideband orthogonal frequency division multiplexing (OFDM) communication systems face new challenges due to the near-field range expansion and the beam split effect, complicating the acquisition of channel state information (CSI). To tackle these challenges, in this paper, we present a wideband beam training scheme by utilizing the beam split effect to bypass the CSI acquisition. Specifically, by analyzing the beam split effect in RIS-assisted OFDM communication systems, we propose a beam-split-aware codebook capable of covering both the near and far fields. The coverage of codewords in the proposed codebook is expanded benefiting from the beam split effect, leading to fewer codewords compared to conventional narrow-band codebooks. Utilizing such a codebook, a three-stage beam training mechanism is performed to obtain the optimal codeword with low time overhead, thereby facilitating the beamforming. Simulation results demonstrate that the proposed scheme outperforms existing codebook-based beam training schemes in terms of training overhead and sum rate in the hybrid near-far field. © 2025 IEEE.

关键词： Signaling

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Enhancing Smart Grid Security: Detecting Electricity Theft through Ensemble Deep Learning 8

Enhancing Smart Grid Security: Detecting Electricity Theft t...

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8th International Conference on Communication and electronics Systems, ICCES 2023

作者： Sowmya, C.S. Vibin, R. Mannam, Praveen Mounika, Lakkakula Kabat, Subash Ranjan Patra, Jyoti Prasad Presidency University Department of Ece Karnataka Bengaluru India Cms College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India Kkr&ksr Institute of Technology and Sciences Dept of Electrical and Electronics Engineering Andhra-Pradesh Guntur India Sr University Warangal Lakkakula Mounika School of Computer Science and Artificial Intelligence Telangana India Radhakrishna Institute of Technology and Engineering Department of Electrical Engineering Odisha Bhubaneswar India Faculty Electrical Odisha University of Technology and Research Outr Government of Odisha Mahalaxmi Vihar Ghatikia Techno Campus Odisha Bhubaneswar India

ISBN: (纸本)9798350396638

Theft of electricity is a major problem that causes financial losses and inconsistent service for paying consumers for power distribution companies all over the world. The safety of the power grid depends on the ability to identify and stop electricity theft. The use of deep learning techniques has shown great promise in recent years, particularly in the areas of computer vision and natural language processing. This study recommends a random forest-based ensemble deep learning method for identifying cases of electricity theft. The proposed ensemble deep learning model leverages the best features of many kinds of deep learning architectures, including stacked Convolutional Neural Networks (CNN)and Long Short-Term Memory (LSTM). Each architecture has its own strengths when it comes to monitoring normal and abnormal electrical use for signs of theft. The final forecast is derived by adding together the predictions of the different models in the random forest ensemble. The ensemble model is trained using a massive dataset of energy usage records and theft information. Information about consumption patterns is extracted using feature engineering methods once the dataset has been preprocessed to get rid of noise and outliers. This preprocessed dataset is used to train the ensemble model, which then optimizes its parameters to reduce prediction errors. We use many measures, including accuracy, precision, recall, and F1-score, to assess the proposed ensemble deep learning model's performance. Experiments are run against both conventional machine learning methods and standalone deep learning models to prove that the ensemble method is superior. The findings demonstrate that the ensemble model is more accurate and has a greater detection rate, making it suitable for spotting energy theft. © 2023 IEEE.

关键词： Internet of things

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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LDCP: A Novel Approach to Predict Fake Reviews in Online Social Network by using Learning based Data Classification Principle

LDCP: A Novel Approach to Predict Fake Reviews in Online Soc...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S.Sudha Mercy S. Venkatesan Bharaneedharan M Nayagan. S L. Venkateswarlu Nimmagadda Padmaja Department of Computer Science and Design R.M.K. Engineering College Chennai Department of Electrical and Electronics Engineering QIS College of Engineering and Technology Ongole AndhraPradesh Department of ECE S.A. Engineering College Chennai Department of CSE Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electrical and Electronics Engineering QIS College of Engineering and -Technology Ongole Andhra Pradesh Department of ECE School of Engineering Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of online reviews has significantly impacted consumer decision-making processes, making the detection of fake reviews crucial for maintaining trust in e-commerce platforms. This study presents a novel approach for detecting fake reviews using machine learning (ML) and deep learning (DL) models, with a focus on achieving high accuracy through advanced feature extraction techniques and data augmentation. Various models, including classical ML algorithms such as Support Vector Machine (SVM) and Random Forest, alongside DL models like Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Cascaded Multi-Layer Perceptron (MLP), were evaluated. Feature extraction methods like TF-IDF, GloVe, and combined approaches were used to enhance performance. The DL model RoBERTa achieved the highest accuracy of 99.5%, while Cascaded MLP reached 99.2%. Classical models like Random Forest and SVM also performed well, achieving accuracies of 97.1 % and 96.8%, respectively. Data augmentation techniques, such as back-translation, further improved the models' ability to handle imbalanced datasets. The results demonstrate that combining DL architectures with advanced feature extraction leads to superior performance in fake review detection.

关键词： Support vector machines Deep learning Accuracy Social networking (online) Feature extraction Data augmentation Data models Convolutional neural networks Fake news Random forests

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Design of circular photonic crystal fiber for OAM extraction SDM applications

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Optical and Quantum electronics 2022年第12期54卷 1-23页

作者： Kamruzzaman, M.M. Mhatli, S. Arun Kumar, U. Roopa Jayasingh, J. Sivasakthiselvan, S. Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia ISI Kef Université de Jendouba Jendouba Tunisia SERCOM Ecole Polytechnique de Tunisie Tunis Tunisia Department of Electrical and Electronics Engineering SRM Institute of Science And Technology Ramapuram Campus Tamilnadu Chennai 600089 India Department of ECE Karunya Institute of Technology and Sciences Coimbatore India Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Optical fibers are widely used for long links (intercontinental, terrestrial optical backbone) and short scopes (data center, access network). Certain fibers, called optical fibers specialty, also play an essential role in other fields such as medicine (e.g., endoscopy), sensors, laser applications, etc. The constant proliferation of Internet services, combined with the growth in users, makes it necessary to increase the current capacity of networks to optical fiber. Today, the fibers installed and used for transmission at very high-speed use only the fundamental mode (denoted LP) to transmit information: we speak of fiber's single-mode optics. As they now reach the nonlinear limit of Shannon, one of the ideas for increasing the capacity of optical networks is to implement spatial multiplexing (SDM: Space Divison Multiplexing) and to simultaneously use different modes in a so-called slightly multimode fiber (generally supporting a few dozen modes) or different cores in a multicore fiber. Since 2010, several studies have been developed in this direction, mainly on the fibers supporting the LP (Linearly Polarized) modes and, more recently, the OAM (Orbital Angular Momentum) modes, that is, circularly polarized and helical phase modes. In the latter case, the phase and polarization properties limit the coupling between the modes. The design of a suggested circular photonic crystal fibre with support for 18 OAM modes is discussed in this research. The numerical analysis demonstrates that the proposed fibre has very good values for the fibre parameters, including a low containment loss of less than 810–4 dB/m at a wavelength of 1.9 m, a dispersion flat chromatic with a dispersion dissimilarity for the OAM modes that ranges from 75 to 77 ps/*** for a wavelength of 1.3 to 1.9 m, and a noteworthy effectual index partition. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： MMF OAM PCF SDM

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Efficient Multiple UAV Positioning and Resource Allocation in Intelligent Wireless Network

Efficient Multiple UAV Positioning and Resource Allocation i...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Sonia Jenifer Rayen J. Harirajkumar Sreeja Vijay S. Vijayalakshmi M. Benisha Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai Department of Electronics and Instrumentation Engineering Sona College of Technology Salem Department of ECE S.A. Engineering College Chennai Department of ECE R.M.K. Engineering College Kavaraipettai Chennai Department of ECE Jeppiaar Institute of Technology Kunnam Chennai Tamilnadu India

Intelligent Wireless Network (IWN) is a trending technology and it is used in maximum of intelligent communication in order to improve its performance Unmanned Aerial Vehicles (UAVs) is incorporated with the IWS network. In recent times UAVs handles certain drawbacks like and predictable mobility in the random topology. In order to overcome this drawback’s in this article Efficient Multiple UAV Positioning and Resource Allocation (EMURA) is developed. The core idea of this process is the creation of effective system model, channel model, queue based resource allocation and effective multi UAV positioning process. Through this processes an effective communication can be attained by the reduction of delay and power utilization among the devices. The model is implemented using ns2, and the study focuses on calculating the end-to-end delay and as well the vehicles energy consumption characteristics. From results it is proven that the proposed EMURA attains lower delay and energy consumption which leads to improve the communication quality of the proposed EMURA.

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Compressed Data Aggregation Using Regulated Duty Cycle for WSN-IOT Networks 1

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International Conference on Advances in Communication Technology and computer engineering, ICACTCE 2023

作者： Gupta, Anuradha Gulati, Tarun Bindal, Amit Kumar Kumar, Karan Ramachandra, A.C. Seleck, Július ECE Department M. M. Engineering College M. M. Deemed to be University Ambala Mullana India Department of Computer Science and Engineering M. M. Engineering College M. M. Deemed to be University Ambala Mullana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengalore India Faculty of Management Comenius University in Bratislava Odbojárov 10 Bratislava 2582005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

IoT sensors can send the data using different modes i.e. periodically/one time only/continues streaming using different application-types i.e. periodic/single shot/streaming etc. and resource consumption may also vary as per requirement. In order to utilize resources in optimal way, for each application-type, there is need to regulate the data aggregation. It may be affected by other parameters also i.e. transmission range, Signal-to-Noise-Ratio (SNR) etc. This paper introduces a compressed data aggregation approach (using Zlib library) that takes care of application behavior by enforcing a transmission duty cycle and its performance will be analyzed using different parameters (SNR/Throughput/Packet delivery ratio/Residual Energy etc.). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Signal to noise ratio

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Retraction Note: Harnessing quantum power using hybrid quantum deep neural network for advanced image taxonomy

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Optical and Quantum electronics 2024年第11期56卷 1-2页

作者： Kiran, Ajmeera Rao, TDNSS. Sarveswara Gopatoti, Anandbabu Deshmukh, Ram Ramesh, Janjhyam Venkata Naga Krishnamoorthy, R. Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India Department of ECE Sri Vasavi Engineering College Tadepalligudem India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Coimbatore India School of Engineering SR University Warangal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India

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