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Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

computers, Materials & Continua 2023年第2期74卷 3133-3149页

作者： Mesfer Al Duhayyim Mohammed Maray Ayman Qahmash Fatma S.Alrayes Nuha Alshuqayran Jaber S.Alzahrani Mohammed Alghamdi Abdullah Mohamed Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information and Technology Systems College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data *** healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care *** general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty *** the same time,it is also significant to maintain the delicate contents of health care images during reconstruction ***,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive *** current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT *** aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them *** do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical ***,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO *** the receiver side,decryption process is initially carried out and shadow image reconstruction process is *** experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other *** comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.

关键词： Medical image security image encryption shadow images homomorphic encryption optimal key generation

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Short Text Mining for Classifying Educational Objectives and Outcomes

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computer systems Science & Engineering 2022年第4期41卷 35-50页

作者： Yousef Asiri College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational ***,map-ping program educational objectives(PEOs)to student outcomes(SOs)is a challenging and time-consuming task,especially for a new program which is applying for ABET-EAC(American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission)*** addition,ABET needs to automatically ensure that the mapping(classiﬁcation)is reasonable and *** classiﬁcation also plays a vital role in the assessment of students’*** the PEOs are expressed as short text,they do not contain enough semantic meaning and information,and consequently they suffer from high sparseness,multidimensionality and the curse of *** this work,a novel associative short text classiﬁcation tech-nique is proposed to map PEOs to *** datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by *** datasets are processed and transformed into a representational form appropriate for association rule *** extracted rules are utilized as delegate classiﬁers to map PEOs to *** proposed asso-ciative classiﬁcation of the mapping of PEOs to SOs has shown promising results,which can simplify the classiﬁcation of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.

关键词： ABET accreditation association rule mining educational data mining engineering education program educational objectives student outcomes associative classiﬁcation

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Dynamic Resource Utilization Prediction Model for Cloud Datacenter

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IAENG International Journal of Applied Mathematics 2025年第2期55卷 307-331页

作者： Bliedy, Doaa Khafagy, Mohamed H. Badry, Rasha M. Department of Information Systems Faculty of Computers and Artificial Intelligence Fayoum University Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Fayoum University Egypt

Much research has been done on predicting resource utilization in the cloud to avoid over- and under-provisioning resources. Most existing systems focus on estimating the utilization of one or two resources at most, including memory, CPU, storage, network, or servers dedicated to cloud applications;they do not consider the correlation between resources. A maximum of one or two machine learning algorithms is employed for prediction purposes. Additionally, traditional prediction methods in the cloud provide a one-dimensional output. Most current solutions predict resources, such as memory and CPU utilization, as a single output. Unfortunately, a one-dimensional output in resource provision and usage cannot capture the relationship between the application requirements of different resources, such as CPU, memory, CPU cores, disk, and network;this results in incomplete and inaccurate information and prediction results. Efficient resource allocation and management require predicting several parameters using multivariate state variables. This study presents a multi-resource utilization prediction model that uses several machine learning approaches, such as support vector regression, random forests, MLP regression, neural networks (NN) using Adam and SGD optimizers, and decision tree regression. The prediction model is based on univariate and multivariate time series. Google cluster trace data is used to evaluate the work. Four experiments are executed on the dataset, seeking to predict the resources for different time series interval periods. The various algorithms’ mean absolute errors (MAE), root mean square error, mean absolute percentage error and R-squared are compared to determine which technique achieves the lowest error rate. The experimental results have shown that the prediction model yields better accuracy than previous research. © (2024), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Droid-IoT:Detect Android IoT Malicious Applications Using ML and Blockchain

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computers, Materials & Continua 2022年第1期70卷 739-766页

作者： Hani Mohammed Alshahrani College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

One of the most rapidly growing areas in the last few years is the Internet of Things(IoT),which has been used in widespread fields such as healthcare,smart homes,and *** is one of the most popular operating systems(OS)used by IoT devices for communication and data *** OS captured more than 70 percent of the market share in *** of the popularity of the Android OS,it has been targeted by cybercriminals who have introduced a number of issues,such as stealing private *** reported by one of the recent studies Androidmalware are developed almost every 10 ***,due to this huge exploitation an accurate and secure detection system is needed to secure the communication and data exchange in Android IoT *** paper introduces Droid-IoT,a collaborative framework to detect Android IoT malicious applications by using the blockchain ***-IoT consists of four main engines:(i)collaborative reporting engine,(ii)static analysis engine,(iii)detection engine,and(iv)blockchain *** engine contributes to the detection and minimization of the risk of malicious applications and the reporting of any malicious *** features are extracted automatically fromthe inspected applications to be classified by the machine learning model and store the results into the *** performance of Droid-IoT was evaluated by analyzing more than 6000 Android applications and comparing the detection rate of Droid-IoT with the state-of-the-art ***-IoT achieved a detection rate of 97.74%with a low false positive rate by using an extreme gradient boosting(XGBoost)classifier.

关键词： Android blockchain analysis malware

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Predicting COVID-19 Cases in India Using ARIMA, Prophet, LSTM and Data Analysis Using Power BI 2nd

Predicting COVID-19 Cases in India Using ARIMA, Prophet, LST...

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2nd International Conference on Data Science and Network Engineering, ICDSNE 2024

作者： Das, Abhrajit Seidenberg School of Computer Science and Information Systems Pace University New York United States

ISBN: (纸本)9789819783359

India confronted a serious challenge in the initial throes of the COVID-19 pandemic. This study addressed this by leveraging machine learning to analyse confirmed cases and support better decision-making. We compared the efficacy of Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet and Long Short-Term Memory (LSTM) models on a Kaggle dataset, visualizing the results with Power BI to forecast future trends. The LSTM model, evaluated using Mean Absolute Percentage Error (MAPE), demonstrated superior accuracy in predicting case numbers. This research underlines the importance of data analysis during public health emergencies. Machine learning offers valuable insights for policymakers, empowering them to control outbreaks and allocate resources effectively. Ultimately, this paves the way for strengthened public health responses and preparedness for future pandemics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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A Fast Frequency-Space Transform-Invariant Template Matching using a "Pseudo-Normalizable"Fourier Transform (PNFT) 6

A Fast Frequency-Space Transform-Invariant Template Matching...

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6th IEEE International Conference on Image Processing, Applications and systems, IPAS 2025

作者： Zakaria, Nordin Universiti Teknologi Petronas Center for Cyber Physical Systems Computer and Information Science Department Malaysia

ISBN: (纸本)9798331506520

Given a template image T, the task in template matching is to search for T in a larger image region S. While simple in concept, much effort in recent years has been channeled on improving its robustness to complex transformation, with attention being largely on the similarity measures. Much lesser attention has been on the efficiency of the search computation itself. This paper attempts to fill the gap by proposing Pseudo-Normalizable Fourier Transform (PNFT), a novel complex transform that enables efficient transform-invariant, vectorized template matching. The concept of normalization is overloaded to denote the transformation of a function over an arbitrarily located patch to that about the origin, enabling the efficient use of image integral to perform template matching. The paper discusses on the 'normalizability' - whether the function can be normalized - and further introduces pseudo-normalization as an approximation. The approach proposed requires no learning and runs fast on lower-end CPU-only platform. Further, it is conceptually simple and can be implemented in approximately 200 lines of Python/Numpy code. The efficacy of the approach is empirically analyzed using a complex example image. © 2025 IEEE.

关键词： Template matching

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Mobile Application to Help Abandoned Pets 3

Mobile Application to Help Abandoned Pets

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3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Rahajeng, Salicca Dewi Nurfitriyani, Siti Julianingsih Akbar, Zakin Izzani Leonardo, Calvin Warnars, Harco Leslie Hendric Spits Warnars, Laurens Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350354348

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans. © 2024 IEEE.

关键词： Mobile applications

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Enhanced Road Safety Through Intelligent Lane Detection and Driver Drowsiness Monitoring 15

Enhanced Road Safety Through Intelligent Lane Detection and ...

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

作者： Arunpandian, J. Gunasekaran, V. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350370249

The innovative system outlined in the provided research demonstrates a significant stride towards addressing the growing concern of road safety. Lane departure and driver drowsiness are two major causes of road accidents. In this paper, we present an observation of road lane and drowsiness detection system that uses a camera to capture real-time video footage of the road ahead and applies image processing techniques to detect and track road lanes. The system's road lane detection module employs the canny edge detection algorithm and Hough transform to detect lane lines accurately. The drowsiness detection module of the system analyzes the driver's facial features and tracks the driver's eye movements, blink frequency to identify signs of drowsiness. If the driver found to be sleepy, it issues an audio alert to the driver. The system's capability to detect and track road lanes with precision, along with its ability to identify signs of driver drowsiness, underscores its potential as a promising solution for enhancing road safety. © 2024 IEEE.

关键词： Canny edge algorithm drowsiness lane detection road safety vehicles

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