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Diffusive molecular communication for bacterium propagation over human gut track

International Journal of information Technology (Singapore) 2024年第5期16卷 3101-3108页

作者： Asim, Masood Jaiswal, Raghvendra Chugh, Urvashi Agarwal, Prabhakar Singh, S. Pratap Galgotia College of Engineering & amp Information Technology KIET Group of Institutions Meerut Road UP Ghaziabad 201206 India Electrical Electronics and Communication Engineering Galgotias University Sector-17A Yamuna Expressway GB Nagar UP Greater Noida 203201 India Symbiosis Institute of Technology Nagpur Campus Department of Computer Science & amp

Diffusive Molecular communication (DMC), is the most common approach employed in analysis and replication of different types of communication systems existing in nature. Exchange of Molecular information (MI) using Bacterium, which is based on DMC and may be termed as Bacterial communication (BC), is being used to model detection and cure processes of various decreases due to the Bacteria. DMC is being developed to serve almost each and every aspect of humanity. Also, various approaches to detect and cure the Bacterial disease are proposed in literature. It is noticeable that the complete Human Gut Track (HGT) is responsible to grade the severity of the disease. And therefore, literature presents the propagation of Bacteria in the HGT. Further, it is most important to mention that the impulse response of the system which characterizes bacteria propagation per unit area (f(y)) plays a major role in modeling viral transmission over the (HGT). In this work we analyzed bacterial concentration (N(y)) at different time points (t = 0.3, 0.5, 0.7, 0.9, 1.2). Our findings show that as time (t) increases, N(y) also increases. Additionally, we examined bacterial distribution (V(y)) for varying values of v (λ = 0.2, 0.3, 0.4, 0.6, 0.8). Results indicate that as v increases, V(y) decreases due to fixed distance, where mucus influences bacterial concentration. Increasing mucus flow rate for a fixed distance results in a decrease in Bacteria distribution. In particular we provided analytical expressions of Bacterial concentration (N(y)) and Bacterial distribution (V(y)) under certain impulse responses. Also, the effect of different physical parameters on (N(y)) and (V(y)) have been quantified with the help of numerical simulation. Presented analysis shows perfect agreement with the theoretical background. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Bacteria Propagation Bacterial Distribution Bacterium Concentration Diffusive Molecular communication Human Gut Track

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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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Advances in Medical Image Processing for Liver Tumour Recognition: A Comprehensive Survey

Advances in Medical Image Processing for Liver Tumour Recogn...

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computer, communication and Control (IC4), International Conference on

作者： B. Deepa C. N. Marimuthu S. Geetha S. Maheswari Faculty of Information and Communication Engineering Anna University Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India

The last several decades have seen a lot of activity in the medical image processing domain especially when it comes to the segmentation of liver and liver tumours. The survival rate of liver cancer is rather low when compared to some other types of cancer. Nevertheless, early detection greatly improves the prognosis of liver cancer patients. As a consequence, multiple researchers have developed automated deep-learning (DL) systems for forecasting the development of cancer cells using various medical imaging modalities. A summary of some previously published studies on liver cancer diagnosis is currently lacking in review publications. But new mechanisms and architecture in the prognosis of liver cancer were beyond the possibility of these investigations. This review emphases on the topologies of DL for liver cancer diagnosis. The survey that follows examines the datasets used, describes existing DL-based designs, assesses the benefits and drawbacks of earlier studies, and discusses image processing techniques. Furthermore, a thorough examination of several imaging modalities, performance metrics and results, challenges and future research goals are provided. The comparison study of different methods reveals that HFCNN has the highest accuracy, at 97.22%.

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Mutual Funds Investment Risk Analysis Using Machine Learning 2

Mutual Funds Investment Risk Analysis Using Machine Learning

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Dasgupta, Anupam Ravuri, Nandhika Jhansi Nallakaruppan, Musiri Kailasanathan Gite, Shilpi Natesan, Deepa Balusamy, Balamurugan Vellore Institute of Technology School of Electronics and Communication Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Information Technology and Engineering Vellore India Symbiosis Institute of Technology Aiml Department Pune India Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur Chennai India Shiv Nadar Institution of Eminence Noida India

ISBN: (纸本)9798350384277

The involvement of machine learning (ML) in the finance sector has ushered in a new era of investment management, profoundly influencing the mutual funds landscape. It has redefined strategies and optimized financial decision-making. This paper provides a comparative analysis of ML technologies such as AdaBoost Regressor, Support Vector Machines, Gradient Boosting, and Random Forest. The positive results indicated by the paper highlight the pivotal role of ML in revolutionizing mutual funds management in India, reflecting the nation's aspirations for financial innovation and inclusion aligning it with global levels of efficiency in investment strategies. The efficiency of the models was tested with performance metrics such as Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Performance Error (MAPE). Our evaluation concluded Random Forest was the best performer with an R2 Score of 0.925, MSE of 0.246, RMSE of 0.496, MAE of 0.271, and MAPE of 0.096. © 2024 IEEE.

关键词： Decentralized finance

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Hybrid VLC Systems with Terahertz communication: A Performance Analysis 100

Hybrid VLC Systems with Terahertz Communication: A Performan...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Rajahrajasingh, Hanojhan Jayakody, Dushantha Nalin K. Muthuchidambaranathan, P. Dinis, Rui Copelabs Lusofona University Lisbon1749-024 Portugal Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Ciet Faculty of Engineering Malabe Sri Lanka National Institute of Technology Tiruchirappalli Department of Electronics and Communication Engineering India Instituto de Telecomunicações FCT-UNL Caparica2829-516 Portugal

ISBN: (纸本)9798331517786

This paper investigates the performance of dual-hop UAV-assisted communication system, employing a decode and forward relay architecture. The system utilizes Terahertz (THz) communication in the primary hop and Visible Light communication (VLC) in the secondary hop. We conduct a thorough analysis by deriving closed-form expressions for end-to-end bit error rate (BER). Furthermore, a Monte Carlo approach will be employed to generate best fitting curves and validate the numerical expressions with simulation results. The outcomes of this work offer valuable insights into the feasibility and limitations of UAV-assisted THz-VLC systems. © 2024 IEEE.

关键词： Bit error rate

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An Efficient Object Detection and Classification from Restored Thermal Images based on Mask RCNN 6

An Efficient Object Detection and Classification from Restor...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Thenmozhi, E. Karunakaran, A. Arunkumar, J.R. Chinnammal, V. Kalaivanan, C. Anitha, G. Panimalar Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Electronics and Communication Engineering Chennai India Modern Institute of Technology and Research Centre Department of Computer Science and Engineering Rajasthan Alwar India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

In recent years, thermal cameras are extensively employed in several industries, including biometrics, intelligent surveillance, and health monitoring. The thermal cameras' exorbitant price, meanwhile, makes them difficult to obtain. Furthermore, blurring brought on by camera movement, object movement, and focus settings is a problem with thermal photos. There haven't been many research on thermal image-centered picture restoration that focus on such issues. Additionally, it is critical to accelerate the processing capability of image treatment technologies in order to work in tandem with techniques like object tracking and activity detection that make use of temporal data from thermal recordings. Furthermore, no research has been done on the use of thermal pictures for super-resolution rebuilding and deblurring. Due to the inability to discern reflected on the soil surface or wall caused by the heat emitted by the item, previous research on object recognition using thermal imaging include inaccuracies. This paper suggests a deep learning-based technique for thermal image reconstruction that combines deblurring and super-resolution reconstruction in one step. Recent advances in deep learning have shown that approaches based on generative adversarial networks (GANs) perform well in image-to-image translation challenges because they can maintain texture features in pictures and produce finer, more convincing textures than traditional feed forward encoders. This research study suggests a deblur-SRRGAN for thermal image restoration in light of the benefits of GAN. Additionally, a MR-CNN is also recommended to perform object recognition in the thermal image reconstruction. © 2022 IEEE.

关键词： Image reconstruction

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Enhancing Fintech Fraud Detection through Graph Convolutional Neural Networks 1

Enhancing Fintech Fraud Detection through Graph Convolutiona...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavan, V. Omprakash, Omprakash Bugge, Bhagya Prasad Thangamani, S. Perada, Anvesh Suneetha, Sampathirao Manipal Academy of Higher Education Manipal Law School Manipal India Jain University School of Law Bengaluru India S.R.K.R Engineering College Department of Electronics and Communication Engineering Bhimavaram India Nandha Engineering College Department of Information Technology Erode India Drexel University Department of Electrical and Computer Engineering PhiladelphiaPA United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331529246

The swift advancement of fintech has transformed financial services, offering users seamless digital experiences. Nonetheless, this expansion has also presented intricate cybersecurity difficulties, especially in fraud detection. This research investigates novel methods for addressing fintech fraud through the utilization of GCNN and the ResNet101-C model. GCNN adeptly addresses dynamic fraud trends via graph-based feature aggregation, whereas ResNet101-C, augmented with dropout and fully connected layers, attains enhanced classification performance. Preprocessing procedures, such as imputation, noise reduction, and feature standardization, enhance data quality, whereas feature extraction emphasizes essential transaction variables like frequency and value. Assessment of synthetic datasets reveals that ResNet101-C achieves an accuracy of 93.46%, surpassing leading models. These results underscore the transformational capability of GCNN and ResNet101-C in tackling the dynamic nature of fraud, providing scalable, precise, and privacy-preserving solutions for the financial sector. © 2025 IEEE.

关键词： Noise abatement

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An Infrastructure-Less communication Platform for Android Smartphones Using Wi-Fi Direct

An Infrastructure-Less Communication Platform for Android Sm...

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International Conference on IoT, Intelligent Computing and Security, IICS 2021

作者： Malar, A. Christy Jeba Kanmani, R. Priya, M. Deva Nivedhitha, G. Divya, P. Surya, T. S. Pavith Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamilnadu Coimbatore India Department of Electronics and Communication Engineering SNS college of Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789811981357

Real-time indoor positioning systems provide information about localization of devices required for communication. This paper focuses on Device-to-Device (D2D) communication among smartphones for device discovery and communication. Wi-Fi Direct (Wi-FiDi) is a type of Wi-Fi where a smartphone can connect with another without internet or Access Point (AP). There is no demand for an infrastructure, and this peer-to-peer network doesn’t permit multi-hop communication like ad-hoc network. In a Wi-FiDi network, each device acts as an AP for connecting with other devices. Wi-FiDi allows devices to communicate without Wi-Fi routers. Each AP in the WLAN network is configured with its location in geospatial coordinates (latitude, longitude and altitude) and civic address. Fine Time Measurement (FTM) protocol is implemented and Round Trip Time (RTT) is measured. FTM determines accurate position of a mobile device from an AP. New version of Android OS supports FTM protocol. It allows a mobile device to make a request either in scheduled or immediate measurement mode. WiDiSi simulator is used for implementing Wi-FiDi networks. Devices at proximity can be discovered and connected at higher speed when compared to other wireless technologies. The speed of communication is improved and can be used to locate devices that are few meters away with greater accuracy. The range can also be extended to 1 km covering the entire smart building. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Indoor positioning systems

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Framework for Implementation of Smart Driver Assistance System Using Augmented Reality 7th

Framework for Implementation of Smart Driver Assistance Syst...

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7th International Conference on Big Data and Cloud Computing Challenges, ICBCC 2022

作者： Baskar, K. Muthuraj, S. Sangeetha, S. Vengatesan, K. Aishwarya, D. Yuvaraj, P.S. Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Tholurpatti India Department of Information Technology Sanjivani College of Engineering Kopargaon India Kongunadu College of Engineering and Technology Tholurpatti India Department of Computer Engineering Sanjivani College of Engineering Kopargaon India Department of Electronics and Communication College of Engineering and Technology Kattankulathur Chennai India Department of AIDS Panimalar Engineering College Chennai600123 India

ISBN: (纸本)9789819910502

This research is to investigate momentum innovation for constantly detecting street signs from a moving vehicle. The most encouraging innovation for perceptive vehicle frameworks is vision sensors and image preparation;therefore, this is examined the most thoroughly. Various handling calculations and study the world over concerned with sign acknowledgment are investigated. A functional framework has also been implemented using a regular web camera installed in a testing car. This framework is limited to speed signs and achieves outstanding displays due to rapid but hearty computations. The division is based on shading data, and the recognition is based on a model coordinating computation. The human–computer interface is a voice that announces which sign has been discovered. Driver carelessness is a key element that commonly results in distortion of surroundings, for example, erroneous recognition or disregard of street and traffic signs. Although significant progress has been made in developing a vehicle capable of autonomous guided driving, development has been slow because to concerns of speed, safety, and the ever-changing complexity of the on-road situation. Street plays a key role for the motorist in gathering data from the sign and then acting in a similar fashion. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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A Comprehensive Analysis of Identifying Patients with Chronic Kidney Disease

A Comprehensive Analysis of Identifying Patients with Chroni...

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computer, communication and Control (IC4), International Conference on

作者： S. Geetha C. N. Marimuthu S. Maheswari B. Deepa Faculty of Information and Communication Engineering Anna University Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India

Chronic Kidney Disease (CKD) is a prevalent and progressive health condition affecting millions of individuals worldwide. Early identification and management of CKD are critical to mitigate its adverse effects on patients’ health and reduce the burden on healthcare systems. This study presents a comprehensive analysis of various methodologies and approaches for identifying patients with Chronic Kidney Disease. The research encompasses a detailed review of traditional and emerging diagnostic techniques, by exploring the sensitivity, specificity, and accuracy of these methods, considering their applicability in diverse patient populations and healthcare settings. Furthermore, this analysis delves into the role of artificial intelligence (AI) and machine learning in enhancing CKD detection and risk prediction. The synthesis of this comprehensive analysis provides valuable insights into the current landscape of CKD identification and offers recommendations for optimizing diagnostic strategies.

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