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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Umar, Zeeshan Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Nosheen, Summaira Qureshi, Ali Moiz Ahmad Siddiqui, Adeel Mubarak, Zaima Chuhan, Saad Hussain Mustafa, Muzzamil Yaqub, Muhammad Atif Bilal, Afshan Abbas, Tanveer Bahria University Lahore Department of Computer Science Lahore54000 Pakistan University of Central Punjab Faculty of Information Technology Lahore54000 Pakistan National College of Business Administration And Economics Department of Computer Science Lahore54660 Pakistan

ISBN: (纸本)9798350394474

Malware is created with the express purpose of causing damage to a computer system. Malware may be examined using either static or dynamic analysis techniques. These techniques allow for the precise extraction of unique patterns, which may then be used to identify malware in files. In this research, we propose a behaviorally based approach to identifying malware. Since new malware families and variations are constantly being found on the internet and dark web, they constitute a particularly severe danger. To undo the damage done by malware might be difficult because of the encryption methods it employs. Malware proliferation tracks with AI's rapid development. Because machine learning and deep learning may identify previously unknown risks, its potential use in identifying malware is receiving a lot of attention. This approach yields prediction algorithms that can analyze the actions of malware to unearth new variations and families. There are a total of 3540 rows describing features of corrupted files and 6999 describing properties of uncorrupted files in the dataset. To achieve this, I used the 98.19% accurate Random Forest machine learning method and the 96.77% accurate KKN machine learning algorithm. © 2024 IEEE.

关键词： Deep learning

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Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

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Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

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Metagenomic Face: Applying Machine Learning to Facial Microbiome Data for Pattern Recognition and Disease Classification 4

Metagenomic Face: Applying Machine Learning to Facial Microb...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Guru Prasad, M.S. Devadas, Raghavendra M Hiremani, Vani Jahan, Afsar Praveen Gujjar, J. Dahiya, Aryan Department of Computer Science and Engg Dehradun India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India University Pune India Department of Computer Applications Dehradun India Faculty of Management Studies Bengaluru India

ISBN: (纸本)9798350349900

This innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. This framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. The investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. These findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector Machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. The Support Vector Machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. These additional insights contribute to a comprehensive understanding of the Metagenomic Face framework's efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Impact of Distributed Generation Integration on Distribution system Reliability

Impact of Distributed Generation Integration on Distribution...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Vukušić, Mario Šljivac, Damir Marić, Predrag Žnidarec, Matej J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9789531842976

Ensuring the reliability of electrical power systems is essential for meeting the growing energy demands of modern society. In this paper, we investigate the technical implications of distributed generation deployment, focusing on its influence on system reliability indices such as frequency and duration of distribution grid components failures and resulting expected energy not delivered to the consumers. In order to assess this impact a reliability analysis is performed for case study distribution network in DIgSILENT PowerFactory software for integration scenarios of variable photovoltaic generation and constant biogas power plants generation focusing on possibility of islanded operation of distributed generation on reliability indices. © 2024 IEEE.

关键词： Power distribution reliability

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Smart Home Automation with IoT Sensor systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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A Review on Unconstrained Real-Time Rotation-Invariant Face Detection 3

A Review on Unconstrained Real-Time Rotation-Invariant Face ...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Agrwal, Shubh Lakshmi Sharma, Sudheer Kumar Kant, Vibhor Manipal University Jaipur Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India Lnm Institute of Information Technology Department of Computer Science & Engineering Jaipur India Banaras Hindu University Department of Computer Science & Engineering Rgsc Varanasi India

ISBN: (纸本)9781665453578

With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research. © 2023 IEEE.

关键词： Face recognition

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer systems science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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AppTest: Assessing the Usability and Performance Efficiency of BOSESKO for Digital Participation 26

AppTest: Assessing the Usability and Performance Efficiency ...

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26th International Conference on Advanced Communications technology, ICACT 2024

作者： Llovido, Jennifer L. Brogada, Michael Angelo D. MacEda, Lany L. Abisado, Mideth B. Bicol University Computer Science and Information Technology Department Legazpi City Philippines National University Manila Philippines

ISBN: (纸本)9791188428120

Software systems need a proper evaluation to avoid errors and problems. This research aims to showcase an evaluation method to test an integrated mobile and web application for gathering valuable feedback from Filipinos nationwide. This application, named 'BOSESKO: Building on Opinions and Sentiments for Sustainability and Knowledge Opportunities' - a multilingual, inclusive, deliberative, synoptic, digital participatory toolkit, is intended to act as a central hub to solicit insights on both existing and upcoming policies in the Philippines. AppTest pertains to a comprehensive evaluation of the BosesKo software system. This research hopes to identify the strengths and weaknesses of BOSESKO and improve it, which would serve as a tool to potentially enhance civic engagement in the country by actively involving the general public in policy-making. This study employed a mixed-method approach that seamlessly integrates manual and automated testing techniques. The assessment of BOSESKO is aligned with the ISO 25010 standard, focusing on evaluating usability and performance efficiency. To delve into the usability of BOSESKO, a usability survey was administered to gauge user perspectives on (a) learnability, (b) appropriateness, (c) recognizability, (d) operability, (e) error protection, (f) user interface aesthetics, and (g) accessibility. Additionally, in-depth performance evaluations were conducted using various automated tools, including WebPageTest, PageSpeed Insight, Google Lighthouse, Yellow Lab, and Pingdom. This combination of manual and computerized methodologies provided a robust and thorough analysis of BOSESKO and, in turn, offered valuable insights into the functionality and efficiency of the developed system. As of the time of this writing, about 10,500 users across the Philippines have already engaged with BOSESKO. Initial assessment results of BOSESKO revealed an acceptable user rating with an average weighted mean of 4.36 in terms of usability. Meanwhile,

关键词： User interfaces

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Sentiment Analysis of Mental Illness using Machine Learning Techniques 2

Sentiment Analysis of Mental Illness using Machine Learning ...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Anpan, Sakshi Gourshettiwar, Palash Daigavhane, Mansvi Kishorrao Gadkari, Ayush Pravin Faculty of Engineering and Technology Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India Faculty of Engineering and Technology Wardha442001 India

ISBN: (纸本)9798331528713

Mental health dynamics have also been affected with significant changes by the digital age with the changes in the way people interact, work, or get information. What has emerged over the last decade, while information technologies such as social media, mobile applications, and telemedicine in the broad sense contain the potential for increasing awareness, availability, and support for individuals with mental health problems, new risks also appear. Screens are addictive, and increased use of social media can result in cyberbullying and social comparison, spreading of fake news which causes anxiety, depression, anxiety and other mental illnesses. On the other hand, as found in the digital contexts, there are support networks, opportunities to plan early intervention, along with mental health literacy. The current study discusses the twofold nature of the effects of digital technology on mental health and how both positive and negative outcomes should be used as the guide for the use of technology in the improvement of mental health. It raises awareness of digital literacy that is necessary to teach people healthy virtual behaviors and discussion of mental-health issues to contribute to the creation of a positive online culture. Given that the process advances daily, it also becomes necessary to assess the influence of digital environment on mental health and work on shielding from potential dangers while enhancing advantages © 2024 IEEE.

关键词： Electronic health record

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Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

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computer systems science & Engineering 2023年第9期46卷 2755-2772页

作者： Mesfer Al Duhayyim Badriyya B.Al-onazi Mohamed K.Nour Ayman Yafoz Amal S.Mehanna Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Natural Language Processing(NLP)for the Arabic language has gained much significance in recent *** most commonly-utilized NLP task is the‘Text Classification’*** main intention is to apply the Machine Learning(ML)approaches for automatically classifying the textual files into one or more pre-defined *** ML approaches,the first and foremost crucial step is identifying an appropriate large dataset to test and train the *** of the trending ML techniques,i.e.,Deep Learning(DL)technique needs huge volumes of different types of datasets for training to yield the best *** current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machinebased Arabic Corpus Classification(DODHBM-ACC)model in this *** presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding *** Arabic text classification,the DHBM technique is *** technique is a hybrid version of the Deep Boltzmann Machine(DBM)and Deep Belief Network(DBN).It has the advantage of learning the decisive intention of the classification *** adjust the hyperparameters of the DHBM technique,the Dice Optimization Algorithm(DOA)is exploited in this *** experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC *** outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.

关键词： Arabic corpus text classification machine learning deep learning dice optimization

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