Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world *** are more vulnerable to security *** in nodes,band-width limits,and centralized c...
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Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world *** are more vulnerable to security *** in nodes,band-width limits,and centralized control and management are some of the ***(Intrusion Detection System)are the aid for detection,deter-mination,and identification of illegal system activity such as use,copying,mod-ification,and destruction of *** address the identified issues,academics have begun to concentrate on building IDS-based machine learning *** learning is a type of machine learning that can produce exceptional *** study proposes that WOA-DNN be used to detect and classify incursions in MANET(Whale Optimized Deep Neural Network Model)WOA(Whale Opti-mization Algorithm)and DNN(Deep Neural Network)are used to optimize the preprocessed data to construct a system for classifying and predicting unantici-pated cyber-attacks that are both effective and effi*** a result,secure data transport to other nodes is provided,preventing intruder *** invaders are found using the(Machine Learning)ML-IDS and WOA-DNN *** data is reduced in dimensionality using Principal Component Analysis(PCA),which improves the accuracy of the outputs.A classifier is used in forward propagation to predict whether a result is normal or *** compare the traditional and proposed models’effectiveness,the accuracy of classification,detection of the attack rate,precision rate,and F-Measure,Recall are *** proposed WOA-DNN model has higher assessment metrics and a 99.1%accuracy ***-DNN also has a greater assault detection rate than others,resulting in fewer false *** classification accuracy of the proposed WOA-DNN model is 99.1%.
作者：
Chaduvula, KavithaKranthi kumar, K.Markapudi, Babu RaoRathna Jyothi, Ch.Assistant Professor
Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head
Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head
Department of Computer Science and Engineering Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Associate Professor
Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Andhraparadesh Vijayawada India
In the Internet of Things (IoT) the physical objects network is described as "things" linked to sensors, software and other technologies to connect and share information through the Internet with other devic...
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In the Internet of Things (IoT) the physical objects network is described as "things" linked to sensors, software and other technologies to connect and share information through the Internet with other devices and systems. The floods lead to large losses of life and property in several countries. However, the absence of sufficient technology results in further death and property losses as a result of flooding in developed countries. This is because of a lack of flood alert systems. The objective of this paper is to monitor the flood situation and provide a text message warning in the event of a threat. The main aim of this paper is to detect rising river water levels at a safe distance away from the railways and to allow the respective authorities to take appropriate measures by means of SMS. A sensor is a system that senses and reacts to certain physical data. The 8051 microcontrollers were produced by Intel in 1981. The microcontroller is 8-bit. It has 40 DIP pins, 4 kb of ROM stores and 128 bytes of RAM and two timers and 16 bits. It is equipped with 2 timers. There are four parallel 8-bit ports that are programmable and adjustable as necessary. The hardware device that uses GSM mobile technology for the provision of an information link with a remote network is a GSM modem or a GSM module. A float switch is a level sensor type, a fluid control system that is used inside a tank. A condenser is a tool that stores electrical energy in an electric field. It's a passive electronic feature with two terminals. A LED is a semiconductor light source that transmits light through the flowing current. Liquid Crystal Display stands for LCD. It is a thin, flat display device used in a variety of electronics. A digital electronic level shifter is a system used in the transformation of signs from one logic or voltage domain to another, also known as logic level shifter or voltage level Translation. A diode is an electronic component with a two-terminal that mainly conducts curre
作者：
Sriram, K.P.Anbalagan, E.Sasikumar, S.Kumar, M. Guru VimalParamesh, J.Sujatha, P. KolaAssistant Professor
Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor
Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor
Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor
Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor
Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology
MIT Campus Anna University Chennai Tamil Nadu India – 600044
This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farmin...
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作者：
Anbalagan, E.Sasikumar, S.Kumar, M. Guru VimalParamesh, J.Sriram, K.P.Professor
Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor
Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor
Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor
Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor
Information Technology St.Joseph's Institute of Technology Chennai India
By introducing this collaborative filtering algorithm, which is dependent on machine learning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personal...
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Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes mo...
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The clustering of datasets is a widely used technique in unsupervised machine learning. The cluster quality evaluation is a tricky problem because external validation is usually not possible for clustering. This happe...
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Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based ...
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Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene
Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics...
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Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models ca...
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Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.
作者：
A KannagiPavan ChaudharyMuthupandi GAssociate Professor
Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Assistant Professor
Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Associate Professor
Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India
This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Sim...
This paper introduces a novel methodological approach to evaluating security measures within simulated real-world contexts. By leveraging three distinct algorithms Vulnerability Scoring Algorithm (VSA), the Attack Simulation Algorithm (ASA), and the Impact Assessment Algorithm (IAA) this provides an exhaustive analysis of potential vulnerabilities, simulate cyberattacks based on these vulnerabilities, and gauge the consequences of successful breaches. The Proposed method's foundational algorithm, VSA, quantifies vulnerabilities' severity, whereas ASA simulates realistic attacks and calculates the likelihood of their success. Lastly, the IAA assesses the potential impact of successful attacks. Collectively, these algorithms offer a rigorous and holistic evaluation of security measures. The findings visualized and underscored the superior performance of our proposed method compared to traditional approaches. This research contributes a valuable tool set for organizations seeking to fortify their security posture in an ever-evolving threat landscape.
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