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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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An Experimental Analysis of Machine Learning Model in the Context of Sales Forecasting: -A Walmart Dataset as A Case Study 15

An Experimental Analysis of Machine Learning Model in the Co...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Athanikar, Abhishek Borude, Yash Chougule, Raviraj Diware, Om Department of Computer Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798331300579

Software programmers may get gradually precise at expecting consequences without being clearly coded using machine learning techniques. Machine learning is based on the idea that models and algorithms may collect input data then utilize statistical investigation to determine an output, while updating results as information become obtainable, as underlying principle. For example, models may be adapted and trained to meet management expectations so that correct measures are followed to reach a certain goal. Wall Mart, a one-stop shopping mall, has been used in this system to estimate sales of various products and to study impact that various variables have on sales of products. Predictive models may be built using different features of a Wall Mart dataset and methods used to construct them, and these findings can be used to make better business choices. This Predictive Algorithm is used in many fields over the market world for their profitability. Recently, Lot of companies are going to used this Machine Learning for better AI and new technologies in market. This report Includes combination of machine learning algorithm and Features engineering to predict Walmart sales. The research topic of this paper primarily solves the problems of Walmart sales forecasting that is provided by the Kaggle competition. © Grenze Scientific Society, 2024.

关键词： Input output programs

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Star-shaped chromium resonator solar thermal absorber design based on titanium carbide for industrial heating applications

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Optical and Quantum Electronics 2025年第1期57卷 1-16页

作者： Alsalman, Osamah Han, Bo Bo Patel, Shobhit K. Department of Electrical Engineering College of Engineering King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Information and Communication Technology Marwadi University Rajkot360003 India Department of Computer Engineering Marwadi University Rajkot360003 India

To promote the improvement of thermal energy observation in solar systems, the recent star-shaped design is performed with Chromium (Cr), Titanium carbide (TiC), tungsten (W), and an effective graphene for good output absorption. According to the output situation, the peak six wavelengths (µm) can be highlighted to show the extinct variation in parameter analysis such as λ1 = 0.25, λ2 = 0.32, λ3 = 0.47, λ4 = 0.9, λ5 = 1.2, and λ6 = 1.87 respectively. The design’s output can be studied under the area of ultraviolet (UV) regime, visible (V) regime, and near-infrared (NIR) span effectively. With the expression of bandwidth and wavelength ranges, the overall span for the overall range (2800 nm) is 91.44%, at a 1200 nm bandwidth observes 97.45%, and 96.02% in an 1800 nm bandwidth respectively. In this paper expression, several sections are demonstrated to identify the solar work effectively;the design development, Air Mass (AM) calculation with mathematical equations, parameter analysis by varying the numerical data, incident angle (0–80 degree) effect on the recent absorber, electric and magnetic field intensities (EFI and MFI), and a comparison table. This type of recent solar absorber can be achieved by using thermal solar energy sources in cooking, crop drying, water heating, and so on. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Solar absorbers

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Indic Language Empowerment in E-commerce: Integrating Generative Translations and Vectorized Searches 1

Indic Language Empowerment in E-commerce: Integrating Genera...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Bhosale, Parth Gopale, Kunal Taware, Karan Bewoor, Laxmi Vishwakarma Institute Of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

E-commerce has revolutionized the retail landscape, offering consumers unparalleled convenience and a vast array of choices from the comfort of their homes. Enabling e-commerce in native languages is crucial for creating an inclusive shopping experience, allowing businesses to effectively reach and engage diverse linguistic communities. India is renowned for its multilingual and cultural legacy and this work is an attempt to reach a wider consumer base and increase market potential in India by providing online shopping for cultural festivals. The proposed work is an attempt to develop native language E-commerce platform for various festivals celebrated in India by using Large Language Models (LLM) to generate contextually rich and relevant contents making the user experience more intuitive and personalized. Finally, we talk about making search features better by using modern technology. By using smart algorithms and models, we help users search for products in their own language, and show them results that are most relevant to what they're looking for. Through careful testing and analysis, we show that our solution works well in improving how people interact with e-commerce websites, reaching more customers, and making more sales. Our research is important because it helps bridge the language gap online, giving businesses new opportunities to grow and making online shopping more accessible for everyone. © 2025 IEEE.

关键词： Marketplaces

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Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第2期35卷 2337-2351页

作者： S.M.Udhaya Sankar Mary Subaja Christo P.S.Uma Priyadarsini Department of Information Technology Velammal Institute of TechnologyChennai601204India Department of Networking and Communication SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Energy conservation has become a signiﬁcant consideration in wireless sensor networks(WSN).In the sensor network,the sensor nodes have internal batteries,and as a result,they expire after a certain *** a result,expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efﬁcient way remains a ***,the clustering strategy employs to enhance or extend the life cycle of *** identify the supervisory head node(SH)or cluster head(CH)in every grouping considered the feasible strategy for power-saving route discovery in the clustering model,which diminishes the communication overhead in the ***,the critical issue was determining the best SH for ensuring timely communication *** secure and energy concise route revamp technology(SECRET)protocol involves selecting an energy-concise cluster head(ECH)and route revamping to optimize *** sensors transmit information over the ECH,which delivers the information to the base station via the determined optimal path using our strategy for effective data *** modeled our methods to accom-plish power-efﬁcient multi-hop ***,protected navigation helps to preserve energy when *** suggested solution improves energy savings,packet delivery ratio(PDR),route latency(RL),network lifetime(NL),and scalability.

关键词： Wireless sensor network wireless security wireless routing clustering ad hoc network

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems Science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Illicit Events Evaluation on the Dark Web Using NSGA-2 Algorithms Based on Energy Consumption

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Informatica (Slovenia) 2024年第18期48卷 77-96页

作者： Rawat, Romil Rajavat, Anand Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality. The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr). The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms. The result represents the "F-measure" metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using *** proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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Image Based Plant Disease Detection using CNN: An Experimental Research 15

Image Based Plant Disease Detection using CNN: An Experiment...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Narnaware, Aman Baral, Abhaykumar Gawade, Akash Valsange, Atharva Department of Computer Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798331300579

The first step in preventing losses in agricultural product output and quantity is the identification of plant diseases. The study of patterns that are visible to the human eye on plants is referred to as plant disease research. Plant disease identification and health monitoring are essential for sustainable agriculture. Manually keeping an eye on plant diseases is really challenging. It necessitates an enormous amount of processing time, a great deal of labor, and knowledge of plant diseases. Therefore, plant disease detection uses image processing. A number of processes are involved in disease detection, including feature extraction, classification, segmentation, pre-processing, and image acquisition. This study examined techniques for identifying plant illnesses using photos of their leaves. A few segmentation and feature extraction techniques for plant disease identification were also covered in this work. © Grenze Scientific Society, 2024.

关键词： CNN Architecture Convolution Neural Network Deep learning Plant disease detection

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Cnn models aided with a metaclassifier for lung Carcinoma classification using histopathological images

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Multimedia Tools and Applications 2024年 1-25页

作者： Gautam, Nandita Ghosh, Sohini Sarkar, Ram Department of Computer Science & amp Engineering Jadavpur University Kolkata India Department of Information Technology Netaji Subhash Engineering College Kolkata India

Lung cancer is a dangerous disease with differing treatment plans based on types and location of the cancerous cells. The overall 5-year survival rate for all stages of lung cancer is around 15%. People who smoke are at the highest risk of developing lung cancer. Early detection of lung cancer is crucial for starting early treatment and preventing the disease from spreading. Hence, it can improve people’s chances of survival. Imaging tests, such as a chest computed tomography (CT) scan, can detect lung cancer by providing a more detailed picture. However, the examination of chest CT scans is a challenging task and is prone to subject variability. For this, researchers have developed many computer-aided diagnostic (CAD) systems for the automatic detection of cancer using CT scan images. Misdiagnoses can occur in manual interpretation of images. An automated trained neural network on lung images from healthy and malignant lung cells helps lower the problem. Convolutional neural network (CNN)-based pretrained deep learning models have been used successfully to detect lung cancer. The accuracy of classification is significant to avoid false prediction. This research presents a metalearning based approach for identifying the common types of lung cancer tissues namely, Benign tissue, Squamous Cell Carcinoma, and Adenocarcinoma using LC25000 dataset. All the experiments have been conducted on a publicly available benchmark dataset for lung histopathological images. The features extracted from the penultimate layer (global average pooling) of the transfer learning-based CNN models, namely InceptionResNetV1, EfficientNetB7, and DenseNet121, have been fused together, and the dimensionality reduction has been applied to them before passing to the metaclassifier, which is the Support Vector Machine (SVM) classifier in our case. A quantitative analysis of the proposed algorithm has been conducted through classification accuracy and confusion matrix computation. When compared wit

关键词： Lung cancer

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