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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Sohil, C. Siva Subramaniam, D. Gowtham, R. Sundareswari, K. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Insider attacks on the electronic healthcare device category can result in a deceptive inspection of patients' fitness information, ensuing in unaccountability of records intake along with huge low-budget prices as an outcome of facts fissures in the Digital-Healthcare device without an effective detection technique. As a result, several health centers have confronted prison and reputational implications. A green technique must be proposed to address this challenge, especially for eHealth structures inside the cloud surroundings, in which activities are now carried out via cloud be cloud-based as a result of it. Even as anticipating such options proposed, health records may be centered, which may additionally bring about poorly affected person remedy thanks to disinformation, and accordingly individual mortality. This research is being driven by way of this necessity. This paper proposes a brand-new context for figuring out insider threats upon the usage of extraction in watermarking and detection in logging strategies which is primarily based on Cloud-based health care systems. The method generated a file that included numerous acts carried out through users as well as an audit path of criminal and un legal entries into the device. The technique displayed an immoderate degree of precision, recall, and accuracy inside the assessment finishedat the cease of the studies, indicating that its performance is extremely good to undertake. © 2022 IEEE.

关键词： Budget control

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Fake Product Identification Using Deep Learning

Fake Product Identification Using Deep Learning

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Setty, Tejas Adarsh Rag, S. Mohapatra, Saumendra Kumar Nair, Vishnu G Dsa, Joyline G Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangalore India Sri Sri University Faculty of Engineering & Technology Odisha Cuttack India Manipal Institute of Technology Department of Aeronautical & Automobile Engineering Manipal India

ISBN: (纸本)9798331517892

Digital images play a critical role across various domains, including journalism, criminal forensics, and medical imaging. However, the growing availability of advanced image editing tools has raised concerns regarding the authenticity of digital photographs, particularly in contexts where integrity is paramount. Image forgery, a predominant form of manipulation, involves altering or concealing key image details by copying and pasting portions of the image, often without visible signs of tampering. Such manipulations pose significant challenges for digital forensics in validating image authenticity. This poses significant challenges for digital forensics in authenticating images. To address this, we propose a deep learning-based approach for detecting image forgeries. Using a Convolutional Neural Network (CNN), the method extracts image features and classifies them as authentic or manipulated. The proposed approach addresses the growing sophistication of digital forgery techniques, offering enhanced accuracy and reliability in image authentication and contributing to the advancement of forensic analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques 2

Predictive Analysis of Consumer Behavior Using Supervised Le...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Rekha, K. Sashi Amutha, T. Usharani, R. Pushparani, S. Sivagami, V.M. Saveetha Institute of Medical and Technical Sciences Simats Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence School of Computing Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace. © 2024 IEEE.

关键词： Self-supervised learning

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Deep Reinforcement Learning for Autonomous Drone Navigation in Cluttered Environments 5

Deep Reinforcement Learning for Autonomous Drone Navigation ...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Chandrashekhar, A. Rawate, Amit Dhanamathi, A. Dasari, Rakeshnag Shinkar, Rajeshri Pravin Pavithra, G. Icfai Foundation for Higher Education Faculty of Science & Technology Department of Mechanical Engineering Telangana Hyderabad India Maharashtra Institute of Technology Department of Electronics and Computer Engineering Maharashtra Aurangabad India Roever Engineering College Department Of Computer Science and Engineering Tamilnadu Perambalur India Acharya Nagarjuna University Department of CSE Nagarjuna Nagar Guntur India SIES College of Arts Science and Commerce Department of Computer Science Sector 5 Nerul Navi Mumbai India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350351378

The application of Deep Reinforcement Learning (DRL) to enhance the autonomous navigation capabilities of drones in environments that are complex and congested. The complications involved with guiding drones across restricted places, overcoming obstructions, and navigating among clutter are addressed in this article. Through the utilization of DRL techniques, the framework that has been proposed gives drones the ability to learn and alter their navigation strategies on their own through the process of trial and error, hence optimization of real-time decision-making. The incorporation of deep neural networks for the processing of sensory data makes it possible for drones to comprehend their environment, which in turn makes it easier for them to make educated judgments with regard to safe navigation. In order to demonstrate the potential of DRL in boosting the autonomy and robustness of drone navigation systems in demanding environments, the effectiveness of the technique is evaluated using simulations and experiments conducted in the real world. The findings make a contribution to the development of autonomous drone technology, which has implications for a variety of applications including environmental monitoring, search and rescue operations, and surveillance. © 2024 IEEE.

关键词： Drones

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ASIS Edge Computing Model to Determine the Communication Protocols for IoT Based Irrigation

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Journal of Mobile Multimedia 2022年第3期18卷 885-916页

作者： Premkumar, S. Sigappi, A.N. Department of Computer Science & Engineering Faculty of Engineering & Technology Annamalai University Tamilnadu India

Internet of Things (IoT) provide a promising Smart irrigation facilitator for continual monitoring and control of environmental parameters, thereby leading to a huge volume of data to be efficiently collected, transferred, processed and stored. The deployment of cloud-based infrastructure with on-field connectedness, allowing information exchange among IoT nodes, and the usage of energy scavenging (e.g., solar power) in feeding them, become necessary, since agricultural fields are in lack of wired energy supply and, often, a reliable (Internet) network coverage. Therefore, these issues can be addressed through the integration of Edge computing in IoT scenarios. An efficient strategy is required to select the best communication technology with a motive of increasing the network performance between the IoT devices, Edge device and cloud. Application Specific Infrastructure Selection (ASIS) is an edge computing model developed to select the appropriate communication protocols according to the infrastructure requirements of three different real time scenarios namely: Assembly line automation, Smart parking system and Automatic irrigation system are deployed to get the most suitable application specific protocol from ZigBee, LoRa (Long Range) and LoWPAN (Low-Power Wireless Personal Area Network) to implement in real-time basis. ASIS model is proposed as a network resource manager that is capable of sensing, acting, signal processing, and/or communication abilities to perform a protocol selection according to their physical and technological limitations. Further Edge based ASIS model is developed to enhance the network performance even better when compared with cloud-based model. Automatic irrigation system is extended in the Edge based ASIS model. The overall ASIS system is evaluated by means of network parameters such as network usage, network delay and power consumption. The ASIS model and Edge based ASIS model is deployed in iFogSim simulator that compares each protoc

关键词： Cloud infrastructure Edge computing IFogSim Internet of things Irrigation system LoRa LoWPAN Network protocols Zigbee

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration 4

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Hemalatha, S. Ramasamy, Jayabharathi Selvi, K. Anandan, P. Meenakshi, R. Maniraj, S.P. Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S. A. Engineering College Faculty of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management. © 2024 IEEE.

关键词： HTTP

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Design of a Magneto-Electric Monopole Antenna

Design of a Magneto-Electric Monopole Antenna

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Nakamura, Shunta Sano, Makoto Kuse, Ryuji Fukusako, Takeshi Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (纸本)9798350364774

This paper presents a compact magneto-electric (ME) monopole antenna. Based on the image theory, the lateral size of the conventional ME dipole antenna is reduced by half, by placing a vertical ground plane. The ME monopole antenna was designed at 2.5 GHz. High front-to-back ratio of 20 dB and low cross polarization are observed. © 2024 IEEE.

关键词： Monopole antennas

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Industry 4.0 – Applications, challenges and opportunities in industries and academia: A review

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Materials Today: Proceedings 2023年 79卷 389-394页

作者： Rana, Bharti Rathore, Sanjay S. Department of Computer Science University of Delhi Delhi India Skill Faculty of Engineering and Technology Shri Vishwakarma Skill University Dudhola Palwal Haryana India

Industrial revolutions have always influenced the other sectors since the first industrial revolution. "Industry 4.0 (I4.0)", the fourth industrial revolution, in particular, has impacted the ecosystem of the industries where the focus is on developing cyber-physical systems (CPS). The ultimate goal of I4.0 is building "smart" machines that can predict and take intelligent/smart decision(s) using enablers of I4.0 like data analysis, communication, and internet-of-things. I4.0 has applications in many sectors like healthcare, agriculture, wood, food, and education. It has boosted not only these sectors but also new concepts like Smart Material, Agriculture 4.0, Health 4.0, Smart Operator 4.0, and many more have evolved in the literature. Adopting I4.0 also highlighted creating a new educational paradigm that focuses on developing the Smart Workforce, Smart Operators, Smart Material and Smart Project Managers and boosted the concept of Education 4.0. The objective of the present work is to review the research works of I4.0, its applications, and challenges in the above-listed prominent sectors. The focus of the work is to report (i) the current state of various technologies of I4.0 in the industries, (ii) the present status of I4.0 in the academia ecosystem, and (iii) key identified challenges in the adoption of I4.0. It was observed that technologies, mainly Artificial Intelligence, IoT, machine learning, CPS, and robotics, are popularly used. However, the popularity of technologies varies according to the sector or application area. Also, an attempt was made to report the various opportunities for the researchers and how Academia can support the demands of I4.0. © 2023

关键词： Industry 4.0

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Dual-Channel MEMS Actuated Tunable Optical Filter Based on Polarization Insensitive Compound Ring Resonator for DWDM Networks 11th

Dual-Channel MEMS Actuated Tunable Optical Filter Based on P...

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11th EAI International Conference on Advancement of science and Technology, ICAST 2023

作者： Ayalew, Hailu Dessalegn Talabattula, Srinivas Annamalai, Pushparaghavan Electrical and Computer Engineering Faculty Bahir Dar University-Institute of Technology Bahir Dar Ethiopia Department of Electrical Communication Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9783031641503

One of the key components of wavelength division multiplexing (WDM) networks is tunable optical filters (TOFs). This paper focused on the theoretical analysis and design of a new dual-channel micro-electromechanical system (MEMS) actuated tunable optical filter based on polarization insensitive compound ring resonators (PICRRs) for dense wavelength division multiplexing (DWDM) optical networking applications. It is basically structured on an optimally designed capacitive type microcantilever beam, which is fixed at both ends, with built-in PICRRs, which are placed at both fixed ends of the beam to gain linearly varying longitudinal strain and stress on the resonators. Also, the dimensions of the rings are properly chosen to achieve a full width at half maximum (FWHM) of 0.4078 nm with a flat pass band at 1 dB of 0.3626 nm and a good shape factor for both channels. It also shows a free spectral range of about 20.05 nm for the first channel and 20.046 nm for the second channel, with a high extinction ratio that allows filtering of adjacent channels of 50 GHz DWDM systems. Thus, when the electrostatic attraction force (EAF) is applied, the beam will bend, which induces strain and stress in the rings, which brings a change in effective refractive index of the rings and also affects the perimeter and coupling coefficients of the rings that leads to a change in the output spectrum shift, providing the tunability as high as 4.779 nm/μN, and it is capable of tuning up to 355 GHz with negligible variations of FWHM and channel spacing within the tunability range of the two channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dense wavelength division multiplexing

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Lasso-Based Fast Residual Recovery For Modulo Sampling 48

Lasso-Based Fast Residual Recovery For Modulo Sampling

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Shah, Shaik Basheeruddin Mulleti, Satish Eldar, Yonina C. Weizmann Institute of Science Faculty of Mathematics and Computer Science Israel Bombay Department of Electrical Engineering India

ISBN: (纸本)9781728163277

In practice, Analog-to-Digital Converter (ADC) is used to perform sampling. A practical bottleneck of ADC is its lower dynamic range, leading to loss of information. To address this issue, researchers suggested folding operation on the signal using a modulo operator before passing it as an input to ADC. Though this process preserves the signal information, an unfolding algorithm is required to get the true samples from the folded samples. Noise robustness and computational time are two key parameters of an unfolding algorithm. In this paper, we propose a fast and robust algorithm for unfolding. Specifically, we first show that the first-order difference of the residual samples (the difference between the folded and true samples) is sparse by deriving an upper bound on its sparsity, and can be recovered from its partial Fourier measurements by formulating a sparse recovery problem. We demonstrate that the proposed algorithm is robust to noise and computationally efficient compared to the existing methods. © 2023 IEEE.

关键词： Recovery

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