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13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Ghosh, Partha Kumar, Prashant Paul, Soumyadip Phadikar, Santanu Department of Information Technology Netaji Subhash Engineering College MAKAUT Kolkata India Department of Computer Science and Engineering MAKAUT Kolkata India

ISBN: (纸本)9781713857938

Cloud Computing has made resources and services accessible from any place of the world at any time. But with increasing popularity of Cloud Computing, it has also become vulnerable to intrusions. To defend the Cloud from attacks, Intrusion Detection Systems (IDSs) are developed. IDSs are deployed in the system or network to identify intrusions and report about them to the administrative system. Here the authors have proposed a novel IDS to provide security in the Cloud. For Feature Selection (FS), a Mutual information-information Gain based modified Ant Colony Optimization (MI-IG based modified ACO) method has been used which resorts to a feature scoring method. After FS, a hybrid multilevel Logistic Regression-Neural Network (LR-NN) classifier has been used for classification purpose where LR executes anomaly detection through binary classification and NN performs misuse detection. NSL_KDD dataset has been selected for conducting the experiments. From the experiments, it is observed that the proposed model has produced results with a high rate of accuracy. The results prove the excellence of the constructed IDS in Cloud Environment. © Grenze Scientific Society, 2022.

关键词： Ant colony optimization

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Hybrid Autoregressive Integrated Moving Average and Bi-directional Gated Recurrent Unit for Time Series Forecasting 2

Hybrid Autoregressive Integrated Moving Average and Bi-direc...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Nelson, S. Yallamelli, Akhil Raj Gaius Alkhayyat, Ahmad Saranya, N. Naga Sowmya, M. V.S.B. Engineering College Department of Information Technology Karur India Amazon Web Services Inc Seattle United States College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Nitte Meenakshi Institute of Technology Department of Ai & Ds Bengaluru India

ISBN: (纸本)9798350373110

Forecasting in time series is given special attention in manufacturing, containing sophisticated systems in which patterns of behavior are being recorded through time series data. But it has to be mentioned that the non-strictly linear and non-stationary processes of these predictive systems make the process much more challenging in terms of accurate forecasting. A hybrid method used for searching an appropriate function by combining the outcomes from the linear model and the nonlinear model is proposed. The developed method has three phases: (i) a linear model is used for time series (ii) a nonlinear model is used for error series and (iii) hybridization of both linear and non-linear models. Autoregressive Integrated Moving Average (ARIMA) method is used in step 1 and Bi- directional Gated Recurrent Unit (Bi-GRU) is introduced and used in step 2. By using six real-world time series, the proposed method is evaluated using performance metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error. The obtained results exposed that the proposed hybrid method outperformed than existing methods by achieving low measures with performance metrics OF 26.3 MSE, 10.3 MAE and 17.5 MAPE. © 2024 IEEE.

关键词： Autoregressive moving average model

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Centralized RSU Deployment Strategy for Effective Communication in Multi-hop Vehicular Adhoc Networks (VANETs) 6th

Centralized RSU Deployment Strategy for Effective Communicat...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Rashid, Sami Abduljabbar Audah, Lukman Hamdi, Mustafa Maad Abdulsattar, Nejood Faisal Mutar, Mohammed Hasan Alkhafaji, Mohamed Ayad Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia Department of Computer Engineering Technology Al-Maarif University College Al-Anbar Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna Baghdad66002 Iraq National University of Science and Technology DhiQar Iraq

ISBN: (纸本)9789819940707

Due to the advent of smart cities, vehicular technologies are high focus to perform intelligent transmission. Road traffic control and accident control becomes a primary tasks in Vehicular Adhoc Networks (VANETs). In VANETs communication is ineffective without presence of Road Side Units (RSUs). In some places due to lack of RSUs data transmission is improve as well as end to end delay and routing overhead is increased during the time of communication so effective RSU deployment is very essential to provide effective communication in multi-hop VANETs. In this paper, Centralized RSU Deployment Strategy is proposed to control and monitor the traffic density of the vehicle. The parameters which are considered for the process of centralized RSU deployment strategy are centralization scale, past transmission rate and location identification. To analysis the performance of the network the simulation is carried out in NS2.35 and SUMO. The parameters which are taken for the performance analysis are packet delivery ratio, end to end delay, routing overhead and packet loss. In order to execute comparative analysis the recent research works are considered which are AVRCV and RDSTDV. From the outcome it is understood that the proposed CRDSMV approach achieves 70–200 ms lower end to end delay, 7–14% improved packet delivery ratio, and 50–300 packets lower routing overhead and 50–240 Kbps improved throughputs when compared with the earlier methods AVRCV and RDSTDV. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Roads and streets

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Cloud-Supported Bayesian Networks for Predicting Plant Stress in Adaptive Agricultural Practices

Cloud-Supported Bayesian Networks for Predicting Plant Stres...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pandey, Pramod Devi, R. Aiyshwariya Sree Southry, S. Balasubadra, K. Rajmohan, M. Muthulekshmi, M. Nagpur Campus Maharashtra Pune India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

To maximize yields and optimize resource allocation in modern agriculture, planning for and managing plant stress is crucial. An innovative approach to plant stress prediction in adaptive agriculture is presented in this paper, which makes use of cloud-supported Bayesian networks (BNs). It combines real-time data from agricultural sensors with past records and environmental variables to build probabilistic models that can predict possible stress factors affecting plant health. The continually evolving features of agricultural ecosystems may be accommodated by using cloud infrastructure to train models and process data scalable. This technique successfully predicts degrees of plant stress, as shown in a thorough case study. This framework makes it easier to apply adaptive agricultural techniques by providing farmers with practical information based on predictive analytics. Resource-efficient practices appropriate to crop varieties and environmental circumstances also help make agricultural operations more sustainable. To overcome the challenges of modern agriculture, make informed decisions, and enhance agricultural systems. © 2025 IEEE.

关键词： Smart agriculture

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An Efficient Automated Water Quality Assessment System utilising Machine Learning and IoT

An Efficient Automated Water Quality Assessment System utili...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prabu, R. Arun, R. Arumuga Mohankumar, B. Ragavan, K. Department of Information Technology Vel Tech Multi Tech Dr.RR & Dr.SR Engineering College Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350305708

Water plays a fundamental position within side the existence of each organism. Without water no existence may want to survive and plenty of critical and non-critical human activities might now no longer be feasible without using wholesome watersheds. In addition, the process of monitoring and identifying its quality is very important which prevents people from various diseases and other health-related issues. To accomplish this, the proposed research work develops a framework for automating the process to predict water quality. This is achieved by integrating Machine learning techniques with the Internet of Things (IoT), where various IoT sensors are used to collect the environmental parameters related to water such as temperature, pH, and turbidity and which will be analyzed using Machine Learning techniques. Random Forest algorithm and Navie-based algorithm are utilized for predicting water quality and these results will be notified to the users on exemptional circumstances. The proposed framework helps people to analyze and monitor the quality of water without any human intervention. © 2023 IEEE.

关键词： Internet of things

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Multichannel CNN and Cross-Modal Attention Residual Network for Detection of Fake News 2

Multichannel CNN and Cross-Modal Attention Residual Network ...

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

作者： Sornalakshmi, R.R. Joshi, Bhavesh Priyanka, Bh.D.D. Chatterjee, Saptaparni Goyal, Shweta Srivastava, Prateek Saveetha Institute of Medical and Technical Sciences Saveetha School of Law TN Chennai India Graphic Era Hill University School of Management Bhimtal Campus India Srkr Engineering College Department of Information Technology Bhimavaram India Sister Nivedita University Department of Computer Science and Engineering West Bengal India Graphic Era Deemed to be University Department of Electrical Engineering Dehradun India Department of Information Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9798350364699

Social networking sites have been one of the most widely used platforms for news consumption in the past few decades. Various educational institutions have investigated automated fake news identification as a means of countering false information, given the detrimental effects that the spread of false information on social networking sites has on both people and the community. False news can propagate swiftly on social networking sites and tells a tale more effectively than textonly information when it combines textual and visual components. The many information formats offer fresh ways to identify characteristics of false news and provide more proof that news occurrences are occurring. However, maintaining every modality's characteristics while combining pertinent data from several modalities is a problem for multimodal false news identification tasks. Furthermore, the efficiency of the framework may be impacted by background data produced by the integration of data from several modalities when it comes to certain news. Regretfully, current approaches are unable to meet these difficulties. To overcome these limitations this study employs Multichannel CNN and Cross-model attention-based residual network for detecting fake news. The study uses CMAB-BiLSTM for obtaining textual characteristics, CMABCNN-RNN to recover visual characteristics, MFB for combining these two characteristics, and MLP to identify its authenticity. The research findings indicates that the suggested framework outperforms present approaches for identifying false information. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Fruit and Vegetable Preservation with Support Vector Machine and IoT Connectivity

Enhancing Fruit and Vegetable Preservation with Support Vect...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Satyaraj, D. Prabakaran, T. Blessy, Y.M. Jayanthi, L.N. Mouleeswaran, S.K. Velmurugan, S. Arjun College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Commerce Saveetha College of Liberal Arts and Sciences Tamil Nadu Chennai India Dayananda Sagar University Department of Computer Science and Engineering Innovation Campus Karnataka Bangalore India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Channai India

ISBN: (纸本)9798350382723

This paper presents a new method for extending the shelf life of perishable food items by combining the use of Support Vector Machine (SVM) algorithms with Internet of Things (IoT) connection. Problems with early spoiling detection and inefficient storage condition control plague conventional approaches to fruit and vegetable preservation. Using SVM it analyze environmental variables including humidity, temperature, and gas concentrations that impact commodities with a near-term expiration date in real time via this research. Sensors, actuators, and a command centre can all communicate to one other without a hitch thanks to the IoT. With this integration, storage conditions may be dynamically adjusted according to SVM predictions, guaranteeing the best possible preservation. In light of the ever-changing nature of farming, the suggested system provides an adaptable and scalable answer to the growing need for effective and environmentally friendly food storage solutions. The experimental findings show that the suggested method may prolong the freshness and quality of produce while reducing the amount of resources wasted. The findings of this study will help bring data-driven technology into the food business more widely and improve smart agriculture practices. It highlights the efficacy of combining IoT connection with SVM algorithms for vegetables and fruits storage. Agricultural operations and customers may both profit from the results, which show a considerable decrease in waste, an increase in freshness, and better quality control. © 2024 IEEE.

关键词： Internet of things

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A novel Cross-Layer Protocols for Resource Allocation in Mobile Networks 15

A novel Cross-Layer Protocols for Resource Allocation in Mob...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Giri, Rakesh Kumar Vittapu, Mani Sarma Dev, Anoop Shewale, Chaitali R. Majid, Mohd Aliff Abdul Kulkarni, Nandini Sunrise University Alwar Department of Computer Science and Engineering India Mallareddy Engineering College and Management Sciences Department of Computer Science and Engineering Kistapur Village Medchal Telangana Hyderabad India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Vishwakarma Institute of Technology Department of Information Technology Pune India University Teknology MARA Faculty of Hotel and Tourism Management Department of Foodservice Management Malaysia Symbiosis International University Department of Symbiosis School of Planning Architecture and Design Nagpur Nagpur India

ISBN: (纸本)9798350370249

Cross-layer Protocols for proper resource Allocation in Mobile Networks are a singular technique to enhance the efficiency of wireless networks. The method utilizes multiple layers of the radio protocol stack better to coordinate cell node scheduling and resource allocation. Using combining communication, routing, and scheduling functionalities in an unmarried protocol, pass-Layer Protocols for proper resource Allocation can offer a sizable will increase in the number of facts that may be transferred and the overall latency and throughput of the community. This technique can also enhance the radio interface's operation by lowering interference and encouraging collaborative communications. Eventually, by using facts and managing records from the communique and routing layers, go-Layer Protocols for proper resource Allocation can reduce the complexity of the community, even offering greater scalability. This paper affords unique cross-Layer Protocols for resource Allocation in Cellular Networks. The proposed protocol permits integrated resource allocation in the cell network environment, considering communication and computation layers. © 2024 IEEE.

关键词： Network layers

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Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier

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computers, Materials & Continua 2021年第6期67卷 3045-3055页

作者： K.Lakshminarayanan N.Muthukumaran Y.Harold Robinson Vimal Shanmuganathan Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering Francis Xavier Engineering CollegeTirunelveli627003India School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Department of Information Technology National Engineering CollegeKovilpatti628503India Department of Mathematics and Computer Science Beirut Arab UniversityLebanon Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Hookworm is an illness caused by an internal sponger called a *** from deprived cleanliness in the developing nations,hookworm infection is a primary source of concern for both motherly and baby *** current framework for hookworm detection is composed of hybrid convolutional neural networks;explicitly an edge extraction framework alongside a hookworm classification framework is *** consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework,pooling layers are *** hookworms display different profiles,widths,and bend *** challenges make it difficult for customized hookworm *** the proposed method,a contourlet change was used with the development of the Hookworm *** this study,standard deviation,skewness,entropy,mean,and vitality were used for separating the highlights of the each *** estimations were found to be *** classifier was utilized to characterize the hookworm *** this paper,the exactness and the territory under bend examination in identifying the hookworm demonstrate its scientific relevance.

关键词： Hookworm bleakness edge include storing convolutional neural network contourlet transformation skewness entropy

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Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

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computers, Materials & Continua 2022年第9期72卷 6061-6077页

作者： Muhammad Akram Muhammad Waseem Iqbal Syed Ashraf Ali Muhammad Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science The Institute of Management Sciences(PAK-AIMS)Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

In cipher algorithms,the encryption and decryption are based on the same *** are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes *** paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and *** plaintext character is substituted by an alternative *** mode of substitution is transformed cyclically which depends on the current position of the modified *** keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st *** study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the *** utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

关键词： Encryption decryption cipher symmetric key cryptography single key attack

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