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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Chinnaraj Govindasamy T.R Nisha Dayana Siddhika Dilip Adkar D. Shyam Prakash A. Shilpa Gupta Sanjeev Kumar Trivedi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science Vels Institute of Science Technology and Advance Studies Chennai School of Computer Science Engineering and Applications D Y Patil International University Akurdi Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad Department of Electronics Faculty of Engineering and Technology Khwaja Moinuddin chishti Language University Lucknow

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts.

关键词： Performance evaluation Cloud computing Power demand Electricity Stability criteria Machine learning Load management

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Machine Learning based Mobile Applications for Autonomous Fertilizer Suggestion 4

Machine Learning based Mobile Applications for Autonomous Fe...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Raviraja, S. Raghavender, K.V. Sunagar, Prashant Ragavapriya, R.K. Kumar, M Jogendra Bharath, V.G. University of Garden City Faculty of Computer Studies Khartoum Sudan Karnataka Mysuru India Department of Computer Science and Engineering Telangana Hyderabad India M S Ramaiah Institute of Technology Department of Civil Engineering Karnataka Bengaluru India Sri Ramakrishna Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Brindavan College of Engineering Department of Mechanical Engineering Karnataka Bengaluru560063 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

To ensure food security, advanced agriculture and the Green Movement haveincreased overall output, but with a yield gap. However, the use of agrochemicals such as fertilizers and pesticides, as well as modern cultivars, irrigation, and other inputs, continues to suffer agriculture in some locations. The agriculture-based on traditional agrochemicals may result in soil and environmental degradation, acidification, and a reduction in soil fertility. Only by utilizing appropriate fertilizers and limiting the number of soil resources that are exploited, can help to maintain soil quality and crop yield. As a result, fertilizer recommendations in agriculture are crucial to meeting productivity, and financial needs, and minimizing nutrient losses to the environment. In this research, the smartphone app will be used to construct an autonomous fertilizer suggestion system for farmers. The dataset was gathered from the website Kaggle. The encoding technique is done to make the data entries numeric. Three Machine Learning (ML) models like K-Nearest Neighbour (KNN), Random Forest (RF), and Decision Tree (DT) are employed to predict fertilizer based on environmental and soil characteristics. The best model is identified using performance metrics and deployed on the mobile app. From the performance analysis, the RF gives high accuracy of 90%. © 2022 IEEE.

关键词： Fertilizers

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Exam Cheating Classification Using a Deep Learning Approach

Exam Cheating Classification Using a Deep Learning Approach

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Bishaw, Molla Ejigu Salau, Ayodeji Olalekan Vats, Satvik Kaushik, Keshav Alemu, Wubie Belay Tin, Ting Tin Debre Tabor University Department of Information Technology Debre Tabor Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Graphic Era Hill University Department of Cse Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248007 India Amity School of Engineering and Technology Amity University Mohali Punjab India Debark University Department of Information Technology Debark Ethiopia Inti International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia

ISBN: (纸本)9798350379716

Exam cheating has become a significant concern in educational institutions due to its potential negative impact on academic integrity. Researchers have attempted to overcome the issue. However, the previous research did not use multiple cheating activities in their datasets and the attributes of the studies used were not strong enough to represent exam cheating, particularly about paper positions. The study utilizes the dataset containing cheating and non-cheating images to develop a model using deep learning approaches. The dataset consists of images collected from network colleges, confirming the presence of cheating and non-cheating instances. Preprocessing techniques, including image resizing, noise removal, histogram equalization, augmentation and feature extraction are applied to the dataset, and both original and augmented versions of the datasets are used in experimental studies. Different input image sizes are employed for the deep CNN, AlexNet, GoogLeNet, and ResNet architectures and evaluated for their performance in classification in a paper based exam cheating. As the experimental results demonstrate from the models used in this study the customized CNN model achieves the highest accuracy of 93% and exhibits excellent performance in terms of loss function and accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Statistical Time Series Forecasting Models for Pandemic Prediction

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computer Systems Science & engineering 2023年第10期47卷 349-374页

作者： Ahmed ElShafee Walid El-Shafai Abeer D.Algarni Naglaa F.Soliman Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian University6th October CityGizaEgypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease *** this case,COVID-19 data is a time-series dataset that can be projected using different ***,this work aims to gauge the spread of the outbreak severity over ***,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus *** have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML *** of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive ***,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best ***,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

关键词： Forecasting COVID-19 predictive models medical viruses mathematical model market research diseases

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Nodal Transmission in 5G Wireless Networking

Nodal Transmission in 5G Wireless Networking

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Trapty Agrawal Pallabi Kakati Sunanda Das Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

The increase in data transmission network speed and reliability is inextricably linked to the fast growth of information technology. This calls for the ongoing improvement and upgrading of current standards. Next-generation communication networks are being developed and deployed with diligence by several organizations. This article provides a thorough examination of the software options available that are designed for analyzing and evaluating data network behaviors. It specifically emphasizes tools with substantial built-in functionality and flexibility that are ideally suited for mobile communication systems. It becomes clear that the NS3 network simulator is the best option. This article provides an overview as well as a unique NS3 solution that makes it possible to estimate the dependability of data transmission from a network node's first movement to its removal from a base station.

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Efficient Segmentation for wsn Connectivity Technologies

Efficient Segmentation for wsn Connectivity Technologies

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vivek V Savita Aruna Dore Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

In wireless sensors networks, the chargeable capacity of sensor nodes serves as the primary energy source. In WSNs, grouping has emerged as a crucial strategy to save energy use and lengthen network lifespan. Transport energy is inversely correlated to the amount of distance across transmitter and destination nodes, which supports clustering. In this paper, we provide a novel fuzzy logic model for the cluster head (CH) choice, a key step in clustering in WSNs. The recommended model includes five characteristics to assess each node's suitability for CH status. The closeness to the starting place, volume, topographical suitability, and remaining power are some of these criteria. This fuzzy logic methodology is used to propose the Fuzzy Reasoning-based Energy-Efficient Grouping for WSN, with emphasis on establishing the shortest possible distance across CHs (FL-EEC/D). As a further metric of clustered methods' energy use, we also evaluate how efficiently they distribute energy among the sensors inside the WSN using the Gini index. We compare our proposed FL-EEC/D approach with the Low Energy Adaptation Clustered Architecture (LEACH), a fuzzy logic-based clustering approach, and various techniques. For various network sizes and topologies, simulation findings show considerable improvements in energy efficiency, network longevity, and balanced energy consumption across sensor nodes. Our results show that initial node depletion and half node depletion have significantly improved on average.

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Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare

Reinforcement Learning for Predictive Modeling and Managemen...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Pavan Chaudhary Inzimam R Hannah Jessie Rani Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka

The difficulties caused by rare genetic abnormalities in pediatric healthcare highlight the need for advances in predictive modeling and treatment choices. An innovative method is discussed in the article “Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare.” Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critical are the three RL algorithms used in the suggested approach. To determine the most effective treatment for children with uncommon genetic illnesses, we used these algorithms to create a prediction model. To improve healthcare quality, we carefully evaluate the suggested approach alongside six alternative methods with overlapping but distinct names. We also take accuracy and precision into account while making our assessments. The suggested strategy, which we refer to as the “Novel Genetic Predictor (NGP),” excels in every metric we tested. The results show a considerable improvement in the detection of rare genetic disorders in children. NGP also demonstrates an enhanced ability to make precise predictions, which opens the path for treatment alternatives that are both patient-specific and more effective. This research not only shows the promise of RL in pediatric healthcare, but also emphasizes the need for tailored care for children with rare genetic disorders. The results show that NGP is better than other methods, and they provide doctors a tool they may use to better care for their patients.

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Exploiting hybrid features extracted from color spaces of dermoscopic images

Exploiting hybrid features extracted from color spaces of de...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Simona Moldovanu Mihaela Miron Anisia Culea-Florescu Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galați Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galați Romania

In the context of detecting dangerous illnesses of the patient affected by skin lesions, this study is focused on the problem of improving the classification of melanoma vs. normal nevus considering neural network and new proposed features based on high and low pixels intensity, and densities of the pattern detected with hit-or-miss (HoM) morphological transform. A large set of features from color spaces such as RGB (red, green, and blue), HSV (hue, saturation, and value) and L*a*b* (lightness variable L*, chromaticity indices a* and b*) were initially extracted and grouped in two parts: the first group includes first and second order features, and the second group contains hybrid features (a mix of first order, second order features and proposed features). The dimensionality of the features from these two groups is considerably reduced with principal component analysis (PCA) statistical method and fed three different types of neural network (NN): Radial Basis Function Neural Network (RBFNN), Cascade-Forward Neural Network (CFNN) and Pattern Recognition Neural Network (PRNN). From this corroboration of hybrid features, color space and NN, the L*a*b* color space with CFNN provided accuracy (ACC) of 0.897, F1-score of 0.91 and area under the ROC curve (AUC) of 0.896, respectively.

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Managing Cloud Networks: A Classification of SDN-Enabled Clouds

Managing Cloud Networks: A Classification of SDN-Enabled Clo...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Vaishali Singh Kehkeshan Jallal S Sunanda Das Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

The idea of separating the management plane from the data-forwarding gear was first introduced by Software-Defined Networking (SDN), which has brought about a revolutionary new age in networking. This innovation makes networks programmable, flexible, and dynamically reconfigurable. These characteristics have great potential for the field of computer science in the cloud, where dynamic adjustments and reconfiguration are essential owing to on-demand consumption patterns. The many simulation and empirical assessment techniques that have been created expressly for SDN-enabled cloud settings are also highlighted by us. Finally, study conducted perform a critical review of the state of the research, highlighting gaps and suggesting possibilities for further study.

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Utilizing Cutting-Edge Machine Vision Techniques for Swift Anomaly Detection in Suboptimal Surveillance Environments

Utilizing Cutting-Edge Machine Vision Techniques for Swift A...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Rakesh Kumar Yadav Veena C S Gadug Sudhamsu Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

In the realm of surveillance and anomaly detection, the proposed Adaptive Multimodal Anomaly Detection (AMAD) method stands as an innovative and potent approach. The objective of this method is to detect anomalies swiftly and accurately in suboptimal surveillance environments by amalgamating state-of-the-art machine vision techniques with multimodal data fusion. The core idea revolves around leveraging advanced deep learning architectures and statistical models, thereby enhancing the performance of anomaly detection systems. The AMAD method initiates with Algorithm 1: Multimodal Data Fusion, a process that involves integrating multimodal data streams, such as visual, thermal, and infrared information, into a unified feature space. This integration is accomplished using a weighted sum approach, allowing varying importance levels to be assigned to each modality based on weights $(\alpha, \beta, \gamma)$ . This fusion strategy proves pivotal in capturing a holistic representation of the environment. Convolutional Neural Network (CNN) for Feature Learning follows, where a CNN architecture is employed to learn robust and discriminative features from the fused data. This study showcases that AMAD, with its multimodal integration, deep learning-based feature extraction, and efficient scoring mechanism, triumphs over traditional anomaly detection techniques. It significantly contributes to enhanced anomaly detection capabilities, emphasizing its potential for bolstering public safety and security in dynamic and challenging technological landscapes.

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