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A Low Complexity ML-Based Methods for Malware Classification

computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Development and analysis of whispering gallery mode model for refractive index sensing 6

Development and analysis of whispering gallery mode model fo...

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6th Photonics Meeting 2023, PM 2023

作者： Batumalay, M. Zain, H.A. Rahim, H.R.A. Harun, S.W. Department of Electrical Engineering University of Malaya Kuala Lumpur50603 Malaysia Faculty of Data Science and Information Technology INTI International University Nilai Negeri Sembilan71800 Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

A refractive index sensor based on a whispering gallery mode resonator is numerically investigated in this paper. Whispering gallery mode resonators confine light and can be used in sensing applications, and this work presents the model built using COMSOL software to analyze their sensing performance. This work has investigated the WGM resonators' response to refractive index changes numerically using 2D models in the refractive index ranges 1.000 and 1.333. The resonator showed a sensitivity of 167 nm/RIU in the refractive index range of 1.3331.4 and a sensitivity of 95 nm/RIU in the refractive index range of 1.000-1.010. The sensing resonator has potential applications for biological and chemical analytes. © Published under licence by IOP Publishing Ltd.

关键词： Refractive index

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Deep Learning with Wireless Sensor Network Platform for Multimedia Data Modeling

Deep Learning with Wireless Sensor Network Platform for Mult...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Mahdi Hussin, Marwa Saad Al-Hameed, Mazin Riyadh Al-Tahee, Mustafa Zearah, Sajad Ali Diame, Hussein Alaa Al-Tameemi, Ahmed Raed Al-Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-ayen University Scientific Research Center Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

Recent years have seen a meteoric rise in the volume of multimedia data generated by industries like e-commerce, finance, transportation, and social networks. Furthermore, the purpose of wireless multimedia sensor networks (WMSN) is to offer real-time multimedia applications using wireless sensors. Furthermore, Deep learning has an enormous impact on many applications involving multimedia data for tasks, including picture clustering, image classification, natural language processing, and speech recognition. Multimedia data accuracy, WMSN noise, and unattended situations are all problems for traditional methods. There are many moving parts in a successful organization, but network effectiveness is the backbone of an online business. To combat the problem of data noise in wireless sensor networks, the authors of this article propose using Deep Learning to aid in the development of Effective Multimedia Data Models (DLA-EMDM). In particular, no previous work has tried to model multimedia data cyclically using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). Based on the simulation findings, it is clear that the proposed DLA-EMDM approach improves the precision, efficiency ratio, cost-effectiveness, and silence of multimedia data processing in a WSN. By enhancing the aforementioned wireless network, we may furnish enhanced technical support and platform services to businesses and set the stage for enhancing business performance. © 2023 IEEE.

关键词： Wireless sensor networks

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Bioinspired Routing Protocols for UWSNs Using Particle Swarm Optimization

Bioinspired Routing Protocols for UWSNs Using Particle Swarm...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Adnan, Myasar Mundher Maheshkumar, N. Praveena, J. Patidar, Deepak Aarju Ambika The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Karpagam Academy of Higher Education Faculty of Engineering Department of Science and Humanities Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dapartment of Cse Chennai India

ISBN: (纸本)9798350390759

The aim of this research therefore is to propose and design bio-inspired routing protocols in UWSNs using PSO. High latency and low bandwidth, coupled with high error rates, make underwater routing a different ball game than routing in normal situations and traditional mediums. The proposed PSO-based protocols also consider factors like power utilization, coverage distance, and priority of data packets to make better routing choices thus helping to improve the efficiency and responsiveness of the UWSNs. The compare analysis of the proposed protocols with the earlier protocols, which are also developed based on AODV and DSR, proves high performance of the PSO based protocols in the energy consumption, data delivery ratio, end to end delay and the network lifetime within all the realistic underwater environments analyzed by the simulations. Such advancement would help to improve the communication system in UWSNs, presenting new approaches for efficient communication in the underwater environment. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Investigating the Strength and Hardness of AL7075 Composite Reinforced with Natural Fibers 2

Investigating the Strength and Hardness of AL7075 Composite ...

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2nd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2023

作者： Kumar, Dinesh Singla, Vineet Kundu, Ravinder Neha Kumar, Arun Sharma, Sunil Kumar University Institute of Engineering and Technology M.D.U Department of Mechanical Engineering Rohtak India Jamia Hamdard University Department of Computer Science and Engineering New Delhi India Shri Vishwakarma Skill University Skill Faculty of Engineering and Technology Department of Automotive Studies Palwal India Shri Vishwakarma Skill University Department of Green Technology Palwal India

ISBN: (纸本)9798350326666

Stir casting is the easiest way to melt and combine reinforcement in metal matrix composites made from low-melting metals like aluminium. The stir cast process was used to make Aluminium 7075 hybrid composites by reinforcing sugarcane and bamboo leaf ash at 2%, 4%, 6%, and 8% by weight and retaining AL7075-T651 concentration. Tensile testing was performed on standard-sized samples using Universal Testing Machine UNITEK-94100. Results were tabulated and compared to pure AL7075-T651. The tensile test indicates that the reinforcing percentage enhances strength up to a point, then decreases. Rockwell hardness was used to compare hybrid composite hardness to pure AL7075-T651 alloy. The results showed that adding SLA+BLA reinforcement enhances the hardness of the manufactured AL7075-T651 composite compared to the base matrix alloy up to a certain extent, thereafter it decreases. The results were compared to pure AL7075-T651 alloy using graphical methods, showing that reinforcing improved mechanical properties. © 2023 IEEE.

关键词： Hybrid composites

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Automated Artificial Intelligence Empowered White Blood Cells Classification Model

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computers, Materials & Continua 2023年第4期75卷 409-425页

作者： Mohammad Yamin Abdullah M.Basahel Mona Abusurrah Sulafah M Basahel Sachi Nandan Mohanty E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia E-commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)VisakhapatnamAndhra Pradesh530049India

White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.

关键词： White blood cells cell engineering computational intelligence image classification transfer learning

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Enhancing Mobile Robot Speed Control: PID Controller Optimization with Bio-Inspired Algorithms 4

Enhancing Mobile Robot Speed Control: PID Controller Optimiz...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Singh, Sangeeta Uma, H.R. Bhargav, H.K. Arya, Sujata Singh, Praveen Natrayan, L. JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201 301 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Karnataka Mandya571 448 India Basaveshwar Engineering College Department of Computer Science and Engineering Karnataka Bagalkot587 102 India Faculty of Law Viman Nagar Maharashtra Pune411 014 India Graphic Era Deemed to be University Department of Management Studies Uttarakhand Dehradun248002 India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

This research study presents an investigation into the optimization of PID controllers using bio-inspired algorithms for controlling the speed of mobile robots. The study begins by identifying a mathematical model that accurately represents the dynamics of the DC motor used in robots. Three different tuning methods, namely Ziegler-Nichols (ZN), Flower Pollination Algorithm (FPA), and Fish Swarm Optimization (FSO) are then utilized to design the PID controllers. The primary objective is to enhance the speed control performance of mobile robots. To evaluate the effectiveness of the optimization methods, the set speed is given to each of the three controllers, and their performance is compared. The results demonstrate that the optimization techniques outperform the conventional ZN method in achieving the desired speed control. Among the two bio-inspired algorithms, FPA yields exceptional outcomes in terms of time characteristics. The comparison of time characteristics serves as a benchmark for evaluating the performance of the controllers. It reveals that bio-inspired optimization methods, especially FPA, significantly improve the speed control of mobile robots. The time characteristics, such as rise time, settling time, overshoot, peak time, and peak value, illustrate the superior performance achieved through the optimization process. © 2024 IEEE.

关键词： DC motors

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A Convolutional Neural Network for Ultra-Wideband Radar-Based Hand Gesture Recognition

A Convolutional Neural Network for Ultra-Wideband Radar-Base...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

New developments in sensing technology have enabled the creation of improved assistive devices that enhance daily eldercare routines and offer personalized care to users. Wearable or ambient sensors can now detect a person's behavior, but the need for constant recharging due to their high energy consumption makes wearing such devices around the clock difficult. In this paper, a new sensing technique based on deep learning (DL) approaches for hand gesture recognition (HGR) using an ultra-wideband (UWB) radar sensor is introduced. The study examines the recognition performance of a convolutional neural network (CNN) for HGR and utilizes the UWB-gestures dataset to train and test the DL networks in various experiments with different scenarios. The experimental results demonstrate that the CNN achieved the highest F1-score of 97.19%. © 2023 IEEE.

关键词： Convolutional neural networks

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Using Machine Learning Tools to Analyze Large Real-Time Data Sets 1

Using Machine Learning Tools to Analyze Large Real-Time Data...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Hannah Jessie Rani, R. Yadav, Rakesh Kumar Garg, Avni Gayathri, C. Gaikwad, Vidya S. Vishnoi, Manish Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electronics and Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350359756

Using engine getting-to-know-the-gear to investigate big real-time statistics sets has become increasingly commonplace because of its efficiency, scalability, and advanced abilities. Devices getting to know equipment permit for speedy facts evaluation and sample reputation, allowing quicker and more accurate decision making. This gear may make actual-time data sets more beneficial via detecting traits, figuring out correlations, and developing models for predictive analysis. Moreover, machine mastering can personalize products and services to fulfill consumer desires. By utilizing the system and gaining knowledge of algorithms, groups can better understand user behavior and preferences, leading to extra valuable product and provider offerings. Ultimately, the use of system getting-to-know equipment to release real-time facts helps corporations to make greater informed selections and deliver higher consumer studies. © 2023 IEEE.

关键词： Decision making

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Predicting Dota 2 Game Period Winners Through Hero Analysis

Predicting Dota 2 Game Period Winners Through Hero Analysis

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International Electrical engineering Congress (iEECON)

作者： Chanwit Musika Kritthanit Malathong Chananate Arthayukti Jessada Konpang Boonchuay Jarernpon Phatsakul Thitimahatthanakusol Department of Computer Science Faculty of Science and Technology Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Telecommunication Engineering Faculty of Engineering and Technology Rajamangala University of Technology Isan Nakorn Ratchasima Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

The impact of hero selection on game outcomes is well-recognized, with input from respected streamers and commentators who emphasize the significance of hero choices. These insights, supported by substantial followers and views, underscore the importance of astute hero selection. However, hero selection is not straightforward due to factors like player proficiency and team strategy. While extensive game data allows us to gauge hero performance, practical challenges exist. To address these, a method for real-time outcome prediction based on hero selection is introduced. This approach includes an interface for hero selection, displays sections for hero information, and identifies both winning and losing heroes. The method also calculates and displays the financial advantage in-game, a crucial factor, and adapts to evolving patches, ensuring its reliability and accessibility to players.

关键词： Electrical engineering Navigation Heuristic algorithms Decision making Games Prediction algorithms Dynamic scheduling

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