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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Controller Placement with Respect to Controller Reachability 7

Controller Placement with Respect to Controller Reachability

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7th International Conference on System Reliability and Safety, ICSRS 2023

作者： Xu, Ran Wang, Fenghua Kooij, Robert E. Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft Netherlands Unit ICT Strategy and Policy Den Haag Netherlands

ISBN: (纸本)9798350306057

In this paper we investigate the controller placement problem on networks using controller reachability as the network performance metric. This metric is defined as the probability that each node can reach at least one controller, given that each link is operational with a fixed probability. By exploring placements for more than 100 real-world networks and by varying the number of controllers from two to five, we find that controller reachability varies greatly with different placements. Obviously, increasing the number of controllers increases the controller reachability. However, the extent of this increase depends on the strategy with which the controllers are placed. The findings indicate that efficient controller placement strategies should be developed to ensure good network performance. In this research, we propose four controller placement strategies. One strategy is based on topological network metrics: node degree and path length between controllers and nodes. The other three heuristic strategies are the greedy algorithm, the classic genetic algorithm and the heuristic genetic algorithm. By validating strategies on real-world networks, we find that all four strategies work well to solve the controller placement problem with respect to controller reachability. © 2023 IEEE.

关键词： Controllers

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Image proce bing with deep-learning and transfer learning for cutting tool degradation monitoring 1

Image proce bing with deep-learning and transfer learning fo...

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1st International Conference on Digital Sovereignty, ICDS 2024

作者： Colantonio, Lorenzo Equeter, Lucas Giovannelli, Hugo Dehombreux, Pierre Mahmoudi, Saïd Ducobu, François Machine Design and Production Engineering Lab University of Mom Place du pare 20 Mons7000 Belgium Computer Science Unit Place du parc 20 Mons7000 Belgium

Monitoring the degradation of cutting tools is of utmost importance in the manufacturing world. Tools with substantial wear fail to produce high-quality parts in terms of geometry, residual stre b, and surface finish. Furthermore, replacing tools in a non-optimal manner can lead to increased production costs and downtime. Therefore, monitoring the condition of the tool is e bential to avoid these additional costs and ensure good production quality. This article explores various cla bification models, specifically VGG19, EfficientNetV2, and Vision Transformers. These models cla bify the state of tools using their images. Using transfer learning, a comparison of the best-performing artificial intelligence-based image analysis models is conducted to identify those most suitable for monitoring cutting tools. A comparative analysis of their generalizability, performance and explainability is realized. The model with the best performance is VGG19 with an accuracy of 94%, followed by EfficientNetV2 and ViT with an accuracy of 87%. A full comparison of these results is carried out. © 2025 The Author(s).

关键词： Adversarial machine learning

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Air Pollutant Prediction Using Shallow Architecture Machine Learning Algorithms 8th

Air Pollutant Prediction Using Shallow Architecture Machine ...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Olaifa, Moses Malele, Vusumuzi Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria South Africa Unit of Data Science and Computing Vaal Campus North-West University Vanderbijlpark South Africa

ISBN: (纸本)9789819786947

Over the past few decades, mortality rates associated with air quality pollution have risen in numerous countries around the globe. This pollution stems from different factors such as meteorological conditions, human activities from urban industrialization, vehicular emission, and so on. Air pollution constituents such as NO2, CO, SO, and coarse and fine particulate matter for different locations have been analyzed to learn both correlation and inter-dependency for long and short terms. However, many of these studies overlook datasets collected using low-cost tools and equipment. Most of the time, datasets of this nature are marred with inconsistencies and gaps at irregular intervals. This may render the air pollutant data unsuitable. In order to test the suitability of machine learning algorithms on such dataset, this study conducts a time-series forecast analysis using the prophet algorithm. Two additional models including linear regressor and random forest regressor are trained and tested on the same dataset and a comparative analysis of the three models is performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Sales Prediction Using ARIMA, Facebook’s Prophet and XGBoost Model of Machine Learning 3rd

Sales Prediction Using ARIMA, Facebook’s Prophet and XGBoos...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

作者： Ratre, Sushila Jayaraj, Jyotsna Department of Computer Science and Engineering Bharati Vidyapeeth Deemed University Department of Engineering and Technology Navi Mumbai India Business Intelligence Unit Axis Bank Ltd. Bangalore India

ISBN: (纸本)9789811958670

In this paper, a comparative study is conducted on machine learning models used for predictive analytics of sales. Sales prediction is an important part of modern business intelligence and proves useful toward better stock administration and staff scheduling which in turn boosts deals and consumer loyalty. To prevent over-stocking and under-stocking, every retail store must forecast revenue and inventory requirements in advance. This enables the company to offer the best possible customer service while avoiding losses, guaranteeing the store’s long-term viability. The dataset selected is of the Rossmann chain of drug stores. Feature engineering helps understand the importance of various features and to clean the data by handling outliers and missing data. It is observed that the dataset is historical in nature and has seasonal trends. ARIMA Model, Facebook’s Prophet Model and XGBoost Model have been chosen for comparison. The Root Mean Square Error (RMSE) is employed to quantify the prediction accuracy because of the nature of our situation. The results show us that ARIMA Model fairs well compared to the others as it has the lowest value of 739.06. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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A New HourGlass Network for Detecting Text in Shaky and Non-shaky Video Frames 27th

A New HourGlass Network for Detecting Text in Shaky and Non-...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Halder, Arnab Palaiahnakote, Shivakumara Pal, Umapada Blumenstein, Michael Gornale, Shivanand S. Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom University of Technology Sydney Sydney Australia Department of Computer Science Rani Channamma University Belagavi India

ISBN: (纸本)9783031784972

Shaky and non-shaky videos are quite common in real-time applications such as surveillance and monitoring vehicles and human movements in protected areas. As a result, text detection in such videos is a formidable challenge due to motion blur, noise, shaky cameras, poor quality and poor visibility. In contrast to existing text detection methods, which focus on text detection in scene images or specific types of images, the present work focuses on text detection in both shaky and non-shaky video frames. Inspired by the impressive performance of the HourGlass network for adverse situations, we explore the HourGlass network for successful text detection in shaky, non-shaky video frames and natural scene images. To improve the performance of the HourGlass network, we employ the Real-Time Model (RTMHead) for predicting text precisely and the Cross Stage Partial Network (CSPNet), which is a neck architecture for robust feature fusion. In addition, the integration of the SiLu activation function with the HourGlass network improves the discriminative power ability. To test the efficacy of the proposed method, we conducted experiments on shaky and non-shaky video frames, as well as ICDAR 2015 video frames. Furthermore, to show the effectiveness of the proposed method, we used Total-Text scene images for experimentation. The results on different datasets and a comparative study with the state-of-the-art models show that the proposed model outperforms the existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Degradation Modelling and Prognostics of Rotating Equipment with Automated Machine Learning

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Procedia computer Science 2025年 253卷 1640-1648页

作者： Stefanos Kontos Alexandros Bousdekis Katerina Lepenioti Gregoris Mentzas Information Management Unit (IMU) Institute of Communication and Computer Systems (ICCS) School of Electrical and Computer Engineering National Technical University of Athens (NTUA) Athens Greece

Evaluating and predicting equipment degradation is essential to the reliability and safety of mechanical equipment systems. Condition monitoring based on sensors measuring vibration signals has emerged as the most well-established method for enabling detection and prediction of the health state of rotating equipment. One of the most well-established methods for degradation modelling based on vibration signals is Weibull distribution. Most recent approaches take advantage of Machine Learning (ML) and deep learning to generate prognostics insights. On the other hand, AutoML automates the configuration of ML pipelines to achieve a higher level of automation. Therefore, it has the credentials to enable predictive maintenance applications by optimizing the adopted predictive models; however, the exploitation of AutoML in rotating machine prognostics is an underexplored area. In this paper, we model the degradation procedure according to the Weibull distribution, we extract, combine, and compare various time-domain features, and we apply the H2O AutoML framework for providing predictions about the future health state of the rotating machinery. Our proposed approach is validated in a real-life scenario of cold rolling in the steel industry. Then, we implement and compare various pipelines with different combinations of features in terms of R-squared, MAE, and execution time.

关键词： AutoML predictive maintenance health state prediction rotating machinery

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An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

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computer Systems Science & engineering 2023年第7期46卷 701-717页

作者： Ahmed Ben Atitallah Mohamed Amin Ben Atitallah Yahia Said Mohammed Albekairi Anis Boudabous Turki MAlanazi Khaled Kaaniche Mohamed Atri Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Laboratory of Informatics Gaspard-MongeA3SIESIEE ParisCNRSGustave Eiffel UniversityFrance LETI ENISUniversity of SfaxSfaxTunisia Remote Sensing Unit College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) University of MonastirTunisia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from *** this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into *** system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been *** this study,an acceleration of the GCM algorithm(AGCM)is *** AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original *** assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech *** text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

关键词： Text recognition system GCM AGCM OCR color images graphical interface

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Decoding the future: exploring and comparing ABE standards for cloud, IoT, blockchain security applications

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Multimedia Tools and Applications 2025年第13期84卷 12299-12327页

作者： Singamaneni, Kranthi Kumar Yadav, Kusum Aledaily, Arwa N. Viriyasitavat, Wattana Dhiman, Gaurav Kaur, Amandeep Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology T.S Gandipet Hyderabad500075 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 173212 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Division of Research and Development Lovely Professional University Phagwara India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India MEU Research Unit Middle East University Amman Jordan

Data security poses a significant challenge in cloud computing, the Internet of Things (IoT), and blockchain applications. Attribute-Based Encryption (ABE) has emerged as a promising cryptographic solution for secure data sharing and access management in these contexts. This research article focuses on enhancing security in cloud, IoT, and blockchain applications through a comprehensive examination and comparison of various ABE standards. The study begins with an in-depth literature review on ABE requirements, offering insights into the evolving landscape, challenges, and trends. This review serves as the foundation for the research, which aims to compare ABE standards based on cryptographic techniques, encryption/decryption processes, and access control rules, considering criteria like flexibility, efficiency, scalability, and security. Statistical analyses complement the comparative study, generating empirical data on ABE standard performance. Key metrics, including key size, storage overhead, encryption/decryption time, and computational complexity, are considered to identify trends and draw meaningful conclusions. The research also explores use cases for different ABE models within blockchain, IoT, and cloud computing applications, pinpointing scenarios where specific ABE models excel. The study contributes to advancing access control and secure data-sharing technologies in specified domains, aiding researchers, practitioners, and developers in selecting the most appropriate encryption method. By systematically outlining advantages and disadvantages of various Cipher Text ABE standards, the research facilitates informed decision-making. Additionally, practical use cases ensure alignment between ABE models and the distinctive traits and requirements of each domain, expanding the toolset for enhancing data security in blockchain, IoT, and cloud computing applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springe

关键词： Internet of things

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An Innovative Way to Increase the Speed and Efficiency of Reverse Mapping of Elliptic Curve Points 6

An Innovative Way to Increase the Speed and Efficiency of Re...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ray, Ryan Saptarshi Das, Debendranath Sengupta, Aritro Kumar, Pankaj Sahani, Arun Kumar Govt. College of Engineering and Ceramic Technology Information Technology Kolkata India Indian Statistical Institute Cryptology and Security Research Unit Kolkata India Lingaya's Vidyapeeth Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350306613

Elliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver's end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver's end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form. © 2024 IEEE.

关键词： Elliptic Curve Cryptography Parallel Processing Points Mapping Reverse Mapping Thread Programming

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