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International Conference on Computing Methodologies and Communication (ICCMC)

作者： A. Sathiya A. K. Velmurugan P. Santhuja G. Indra Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science And Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering R.M.K College of Engineering and Technology Chennai Tamil Nadu India

Nowadays, Wireless Sensor Networks (WSNs) are extensively distributed in environmental monitoring, operational health monitoring, and industrial monitoring. It is an inexpensive application. It contains small, less energy-consuming, lower-cost devices combined with restricted computation, observation, and radio transmission capacities. People nowadays use fire alarms with sensors to detect fires. Fire sensors are unreliable. With the fast growth of WSNs, people can now remotely watch their surroundings to ascertain the current status. A standard fire alarm system is only able to detect one of the environmental parameters at a time. It is possible that there is a fire if the detection value is greater than the threshold; this would indicate that there is a fire. Copper, insulated, or cables are used to make the connections between detectors and alarms in a traditional fire alarm system. Due to the presence of a large number of elements at the site of the fire, the smoke alarm system will be unable to make an accurate determination as to the location of the flames. This article proposes a cognitive fire alert framework (CFAF) for realizing fire in unfriendly environments using WSN. It has a phase for wireless transmission, a phase for cognitive fire recognition, and a phase for the Random Forest Method (RFM). This approach utilizes the RFM to classify the fire by data classification. The results of the experiments show that the suggested technique minimizes the number of false alarms by evaluating the delay value by utilizing data samples and comparing the temperature, humidity, pressure, and dust level.

关键词： Temperature sensors Temperature measurement Wireless communication Wireless sensor networks Temperature distribution Humidity Forestry

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PRIVACY-PRESERVING data LINKAGE ACROSS PRIVATE AND PUBLIC dataSETS FOR COLLABORATIVE AGRICULTURE RESEARCH

arXiv

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arXiv 2024年

作者： Zafar, Osama González, Rosemarie Santa Wilkins, Gabriel Morales, Alfonso Ayday, Erman Department of Computer and Data Sciences Case Western Reserve University United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States College of Letters & Science University of Wisconsin–Madison United States

Digital agriculture—the use of technology in agricultural practices—plays an integral role in agricultural research, with the potential to improve crop yield, disease resilience, and long-term soil health. However, it raises privacy concerns and risks, such as adverse pricing, price discrimination, increased insurance costs, and manipulation of essential agricultural resources. These concerns deter farm owners and operators from sharing their data because it could be used against them. This study addresses the need for privacy-preserving frameworks that enables secure data sharing for digital agriculture practices. We propose a privacy-preserving framework designed to mitigate privacy risks while enabling comprehensive data analysis. The proposed tool allows the agricultural community to benefit from research driven policy making that establishes meaningful connections across public and private digital agriculture datasets. Our privacy-preserving algorithm behind the framework can be used to: (i) identify similar farmers based on specified attributes using multiple private datasets, (ii) provide aggregate information such as time and location, (iii) determine price and availability trends of target products, and (iv) correlate these trends with relevant public policy databases, such as food insecurity data, without compromising privacy. We validate the framework using real-life datasets from Farmer’s Market. Also, we demonstrate the framework’s efficacy and practicality by training various machine learning models on linked privacy-preserved private and public data. The numerical experiments showcase how the framework can support policymakers and researchers in implementing digital agriculture related to food insecurity and pricing. Our work addresses critical privacy concerns, thereby facilitating more effective and secure agricultural research. It represents a significant contribution to digital agriculture by enabling the secure integration and analysis of data, p

关键词： Differential privacy

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Verification and Rectification of Error in Topology of Conserved Networks

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IFAC-PapersOnLine 2022年第30期55卷 43-48页

作者： Satya Jayadev P Arsh Bawa Nirav Bhatt Department of Electrical Engineering Department of Metallurgical and Materials Engineering Department of Biotechnology Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) prospective Center for Network Systems Learning Control and Evolution Indian Institute of Technology Madras Chennai India

The knowledge of the underlying topology is essential for understanding and manipulating power grids, water distribution networks, biological networks. At times, the topology may be reported (or recorded) erroneously, mostly owing to human mistakes in reporting. The networks can be represented as a graph in which entities are represented as nodes and interactions among nodes as edges. This work focuses on the study of a specific type of error in topology that occurs when the incidence of an edge in the network is incorrectly reported. We propose a methodology to detect, isolate, and rectify this type of error using a single noisy measurement of flows along all the edges of a conserved network. We first show that this type of error generates specific error signatures, which enables error diagnosis, when the data is noise-free. An approach based on a series of statistical tests is developed to handle noisy data for online error detection and rectification. Simulation studies are performed to test the robustness of the proposed methodology.

关键词： Network topology Error detection Networks Verification Flow data Statistical hypothesis testing

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Computational access point selection based on resource allocation optimization to reduce the edge computing latency

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Measurement: Sensors 2022年 24卷

作者： Kumaran, K. Sasikala, E. Research Scholar SRM Institute of Science and Technology Chennai600089 India Department of Information Technology Easwari Engineering College Chennai600089 India Department of Data Science & Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Mobile Devices (MD) and the Internet of Things (IoT) transfer compute tasks to the client through Computational Access Point (CAP) through the extensive implementation of Local Area Networks (LAN). If all IoT or mobile devices choose the same ports of entry to offload their work, then the calculation of offloading leads to an increase in operating costs. In a network of different CAP to Edge Computing (EC), this research proposes a resource allocation optimization technique and a computation offloading strategy that aims to reduce network costs by recommending the best transmit power distribution, bandwidth assignment, and computation task planning. The optimization problem of NP-hard solved by the proposed technique separates from sub-problems of resource allocation and offloading technique. An algorithm called Dynamic Weighed Quantum Arithmetic Optimization Algorithm (DWQAOA) was proposed for this offloading problem NP-hard. To achieve the ideal solution, a transition probability introduced the optimal Pareto connection during the optimization process. Numerous simulation results show that the edge task latency could be reduced from 3 to 25% relative to competing solutions. © 2022 The Authors

关键词： Resource allocation

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The efficient employment of the space division multiplexing technique with OADMs for more flexibility and scalability in optical systems

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Journal of Optical Communications 2024年第0期45卷 s1577-s1591页

作者： Rashed, Ahmed Nabih Zaki Ramkumar, Govindaraj Srinivasan, Maratta Lakshmi Narasimman Muppala, Chiranjeevi Ahammad, Shaik Hasane Hossain, Md. Amzad Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of ECE Saveetha School of Engineering Chennai India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Kunnam Chennai India Department of ECE Gokula Krishna College of Engineering Andhra Pradesh Sullurupeta India Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Institute of Theoretical Electrical Engineering Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum44801 Germany Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh

The optical add drop multiplexers (OADMs) to be modeled and analyzed depends up on the simulation variables are demonstrated. Transmitted power per channel variations are studied against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. Received power from each channel variations are demonstrated against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. The bit error rate variations are clarified against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. The optical link map through fiber cable core is clarified. Total pulse broadening variations are illustrated against both fiber length and number of transmitted channels at receiver bandwidth of 40 GHz and temperature of 25 °C. The lighted signal/noise ratio variations are demonstrated against both receiver bandwidth and number of transmitted channels at transmission distance of 200 km and temperature of 25 °C. Channel capacity variations are also demonstrated against both receiver bandwidth and number of transmitted channels at transmission distance of 200 km and temperature of 25 °C. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Bit error rate

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Prediction of Fruit Texture Features Using Deep Learning Techniques

Prediction of Fruit Texture Features Using Deep Learning Tec...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Sangeetha S.Rajes Kannan Sampath Boopathi J Ramya Mohammad Ishrat G. Sabarinathan Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer science and Engineering Chennai institute of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Muthayammal Engineering College Rasipuram Tamil Nadu India Department of Computer Science and Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

In this research, a prototype has been created to automatically collect the photos of fruits using a smartphone camera and a thermal camera, generating two distinct datasets: thermal and RGB. Fruit classification and bruise detection have both been accomplished using image processing, machine learning, and deep learning. To assess quality and shelf-life, a second set of RGB and thermal picture datasets was created. The suggested study enhances fruit categorization and flaw detection systems by utilizing multiple Grey level co-occurrence matrix (GLCM)-based texture characteristics. Using color-texture characteristics, pictures are classified using RF and KNN algorithms. RGB features were classified with 97.24 percent accuracy by K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers, while GLCM-based features were classified with 96.40 percent accuracy by the RF classifier. Using thermal pictures, the KNN and RF algorithms' prediction accuracy was 81.70 percent and 84.12 percent, respectively. KNN and RF algorithms have kappa (percentage) values of 79.97 and 81.86, respectively. The random forest (RF) technique is hence the most appropriate for classifying fruit using a thermal picture dataset.

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Current Implicit Policies May Not Eradicate COVID-19

arXiv

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arXiv 2022年

作者： Jadbabaie, Ali Sarker, Arnab Shah, Devavrat Institute for Data Systems and Society MIT United States Department of Civil and Environmental Engineering MIT United States Department of Electrical Engineering and Computer Science MIT United States

Successful predictive modeling of epidemics requires an understanding of the implicit feedback control strategies which are implemented by populations to modulate the spread of contagion. While this task of capturing endogenous behavior can be achieved through intricate modeling assumptions, we find that a population's reaction to case counts can be described through a second order affine dynamical system with linear control which fits well to the data across different regions and times throughout the COVID-19 pandemic. The model fits the data well both in and out of sample across the 50 states of the United States, with comparable R2 scores to state of the art ensemble predictions. In contrast to recent models of epidemics, rather than assuming that individuals directly control the contact rate which governs the spread of disease, we assume that individuals control the rate at which they vary their number of interactions, i.e. they control the derivative of the contact rate. We propose an implicit feedback law for this control input and verify that it correlates with policies taken throughout the pandemic. A key takeaway of the dynamical model is that the "stable" point of case counts is non-zero, i.e. COVID-19 will not be eradicated under the current collection of policies and strategies, and additional policies are needed to fully eradicate it quickly. Hence, we suggest alternative implicit policies which focus on making interventions (such as vaccinations and mobility restrictions) a function of cumulative case counts, for which our results suggest a better possibility of eradicating COVID-19. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear control systems

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Despise Detection through LOA Using Fs with 1D-CNN in Public Forum

Despise Detection through LOA Using Fs with 1D-CNN in Public...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： V Revathi B. Gunapriya Balasubramanian Prabhu Kavin E. Gangadevi Balamurugan Balusamy Shilpa Gite New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Computer Science Loyola College Chennai India Shiv Nadar University Delhi India Symbiosis Institute of Technology Pune. Symbiosis International (Deemed University) Pune India

Hate speech is any speech intended to incite hatred in others. Hate speech is escalating quickly and unpredictably in this digital age where everyone is connected via social media. Due to a lack of understanding of the distinction among hate speech numerous people do not realise they are engaging in hate speech when they criticise somewhat on social media. Victims experience a sense of social alienation as a result, and those who propagate it frequently end up in legal trouble. To combat people’s ignorance, it is crucial to detect any instances of hate speech in the sentences. A machine learning technique is frequently used to help classify each sentence in order to detect such sentences. Additionally, it is a difficult duty because to the enormous volume of rising hate speech on social media from diverse sources. This work offers a Lion optimisation Algorithm (LOA) with Processing Based Hate Speech Detection (HSD) and Classification model in response to this objective. The main goal of the proposed LOA-based HSD model is to recognise and categorise instances of hate speech on social media platforms. The described model uses data preprocessing at numerous phases, such as tokenization, vectorization, etc., to achieve this. In addition, the organisation of social media text into three categories–neutral, offensive, and hate language–is done using a convolutional neural network (ID-CNN) model. The proposed model is experimentally validated, and the outcomes are assessed from several angles. The results of the experiment showed that the LOA-ID-CNN model achieved favourably compared to current state-of-the-art techniques

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Modeling viral evolution:A novel SIRSVIDE framework with application to SARS-CoV-2 dynamics

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hLife 2024年第5期2卷 227-245页

作者： Kaichun Jin Xiaolu Tang Zhaohui Qian Zhiqiang Wu Zifeng Yang Tao Qian Chitin Hon Jian Lu State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangdongChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangdongChina Macao Center for Mathematical Sciences Macao University of Science and TechnologyMacaoChina Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacaoChina

Understanding evolutionary trends in emerging viruses,such as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is crucial for effective public health management and ***,extensive debates have arisen concerning viral evolutionary trends,particularly the interplay between transmissibility,pathogenicity,and immune *** this context,we have developed a novel computational model named SIRSVIDE(Susceptible-Infected-Recovered-Susceptible-Variation-Immune Decay-Immune Escape)to simulate the transmission and evolutionary dynamics of viral *** simulation results indicate that under conditions of high mutation rates,elevated transmission rates,and larger susceptible host populations,viral populations exhibit prolonged increases in transmissibility and immune escape,accompanied by reductions in pathogenicity and noticeable short-term fluctua***,when the total susceptible population size and mutation rate decrease,substantial uncertainty in the evolutionary trends of viral populations becomes *** summary,the SIRSVIDE model establishes a comprehensive framework for generating both short-and long-term viral epidemiological and evolutionary *** simulation outcomes align with existing evidence indicating that SARS-CoV-2 is undergoing selection for heightened transmissibility,decreased pathogenicity,and enhanced immune ***,the model sheds light on the possible evolutionary dynamics of other viruses.

关键词： viral evolution severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) transmissibility-pathogenicity trade-off immune escape

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things

Enabling Protection for Critical Infrastructure through Secu...

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International Conference on Communication and Electronics systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption Security Stakeholders Protection Computer crime Industrial Internet of Things

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