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2025 Communication Strategies in Digital Society Seminar, ComSDS 2025

作者： Saber, Mohamed Gusarova, Natalia Bogatyrev, Vladimir Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

ISBN: (纸本)9798331536121

the growth of IoT devices presents significant security challenges due to their diverse and dynamic environments. To address this, we developed HAMI, a novel intrusion detection system. Our method leverages the GIFS algorithm, which combines feature rankings from ensemble models with genetic algorithms to uncover subtle inter-feature correlations, enhancing detection accuracy. Additionally, we introduce GridSearchSMOTE, a technique that optimizes data balancing by evaluating various SMOTE variants through a meticulous grid search, ensuring optimal performance for each dataset. Empirical evaluations of HAMI show superior accuracy, achieving a 99% detection rate and reduced false positives, outperforming existing systems. These results underscore HAMI's potential to significantly improve IoT network security by exploring its scalability and real-time detection capabilities. © 2025 IEEE.

关键词： Network intrusion

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A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Babu, Vunnava Dinesh Ramkumar, M. Ayyadurai, M. Vivek, K. Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones. © 2024 IEEE.

关键词： Fruits

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Internet of Things (IoT): survey of most important security risks

Internet of Things (IoT): survey of most important security ...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Alaskri Nuredin Ali Salem Ahmed Hala Shaari Software Engineering Dep. Collage of Computer Technology Tripoli Libya Computer Engineering Department Faculty of Engineering Tripoli Libya Web Technologies Department Faculty of Information Tripoli Libya

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field.

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Activity Diagram Synthesis Using Labelled Graphs and the Genetic Algorithm

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Journal of computer Science & Technology 2021年第6期36卷 1388-1406页

作者： Chun-Hui Wang Zhi Jin Wei Zhang Didar Zowghi Hai-Yan Zhao Wen-Pin Jiao Key Laboratory of High Confidence Software Technology(Ministry of Education) Peking University Beijing 100871China Institute of Software School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China School of Computer Science Inner Mongolia Normal UniversityHohhot 010022China Faculty of Engineering and Information Technology University of TechnologySydney 2007Australia

Many applications need to meet diverse requirements of a large-scale distributed user *** challenges the current requirements engineering ***-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of the large-scale user ***,there are still many *** others,a key issue is how to merge these requirements to produce the synthesized requirements description when a set of requirements descriptions from different participants are *** techniques are needed for supporting the requirements *** are widely used in industry to represent *** paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts the genetic algorithm to repeatedly modify a population of individual solutions toward an optimal *** a result,it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input *** approach is featured by:1)the labelled graph proposed as the representation of the candidate solutions during the iterative evolution;2)the generalized entropy proposed and defined as the measurement of the solutions;3)the genetic algorithm designed for sorting out the high-quality *** cases of different scales are used to evaluate the effectiveness of the *** experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability.

关键词： crowd-based requirements engineering requirements synthesis activity diagram genetic algorithm

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The Impact Of Social Media On The Academic Development Of School Students 10

The Impact Of Social Media On The Academic Development Of Sc...

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10th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2022

作者： Perera, U.D.Harshi Lahiruka Harshanath, S.M.B. Sri Lanka Institute of Information Technology Faculty of Graduate Studies Research Sri Lanka Sliit Department of Computer Science and Software Engineering Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

While social media has reached the peak of its popularity, the addiction to social media become a major issue. With the present pandemic the authorities are forced to close the schools and to shift to virtual classrooms. The students must therefore use a connected devise to participate to the classes which has contributed to the increased addiction to social media among the youth. Inappropriate or excessive usage social media may affect the studies as well as mental and physical health of the user. Spread of fake news through social media has also been a big problem. Mainly, due to its convenience of usage, social media, has become a daily necessity, especially among the youth. So, the primary goal of this study project is to determine the impact of social media on school students' academic performance and to suggest a separate social media platform which will meet their needs. © 2022 IEEE.

关键词： Students

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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Examinations Timetabling System Based on A Genetic Algorithm

Examinations Timetabling System Based on A Genetic Algorithm

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Mohammed, Mousa F Abobaker, Rema A. Aljalil, Asma M. Maatuk, Abdelsalam M Omar Al-Mukhtar University Faculty of Science Department of Computer Science Al-Bayda Libya University of Benghazi Department of Software Engineering Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Examination timetabling is a complicated issue that is part of the larger field of scheduling. Timetabling comes in a variety of forms based on the field, for which it is used. In this paper, we explore the problem of exam timetabling in a university. This problem had been defined as difficult to solve since it is generally done by hand over several days or weeks of iterative correction based on feedback from staff and students. We designed a computer-based application to generate an automated exam scheduler that was implemented on the data collected from the faculty of Science at Omar Al-Mukhtar University. We study the problem of examination scheduling for the faculty intensely and propose a genetic algorithm based on a strategy for generating examination schedules that prioritize students' achievement while meeting the hard constraints required for feasibility. Finally, an application entitled 'Examination Scheduler' has been developed and evaluated to confirm that it generates satisfactory results. © 2022 IEEE.

关键词： Genetic algorithms

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Enhanced Multi-Head Self-Attention Transformer-Based Method for Prediction of Drug-Drug Interactions

Enhanced Multi-Head Self-Attention Transformer-Based Method ...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Semih Cal Ramazan Ozgur Dogan Hulya Dogan Department of Engineering Computer Science & Physics Otterbein University Westerville OH USA Department of Artificial Intelligence Engineering Faculty of Computer and Information Sciences Trabzon University Trabzon Türkiye Department of Software Engineering Faculty of Engineering Karadeniz Technical University Trabzon Türkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The usage of various drugs has increased significantly in recent years, leading to a higher possibility of drug-drug interactions (DDIs). The concurrent usage of multiple drugs can result in potentially hazardous interactions, making it crucial to foresee DDIs to prevent adverse effects and enhance patient safety. Traditional DDI prediction methods often require extensive examinations, which can be time consuming and resource intensive. As a result, automatic DDI prediction methods have gained attention in the literature, offering clinicians support for making more accurate decisions and designing effective treatment plans. Despite progress in DDI prediction studies, substantial challenges remain in the field. Our study addresses these challenges by proposing a deep learning-based model leveraging drug features. Specifically, this study introduces an enhanced multi-head self-attention transformer-based method, which incorporates pharmacological features to achieve improved performance. The proposed method consists of two primary stages: feature extraction and model design. To evaluate the efficacy of the proposed method, performance evaluation procedures -Accuracy (ACC), Precision (PRE), Recall (REC), and F -Score-are utilized. Comparative experiments are conducted with several state-of-the-art methods on a data set specifically created for this study. Out of all, the proposed method achieves mean values of ACC, PRE, REC, and F-Score as 87.49%, 87.19%, 82.76%, and 84.56 %, respectively, surpassing the performance of other methods. The results unequivocally demonstrate the effectiveness and superiority of the proposed method in predicting DDIs.

关键词： Drugs Learning systems Accuracy Predictive models Feature extraction Transformers Robustness Safety Matrix decomposition Security

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Mobile Devices Interface Adaptivity Using Ontologies

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computers, Materials & Continua 2022年第6期71卷 4767-4784页

作者： Muhammad Waseem Iqbal Muhammad Raza Naqvi Muhammad Adnan Khan Faheem Khan T.Whangbo Department of Software Engineering The Superior UniversityLahore53700Pakistan INP-ENIT University of ToulouseTarbes65000France Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of *** context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the *** this purpose,the ontological modeling has been made for specific context and *** type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied *** contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and *** competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context *** consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined *** semantic context model is focused to bring in the usage of adaptive *** exploration has finished up with a versatile,scalable and semantically verified context learning *** model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.

关键词： User context adaptive interfaces human computer interaction

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An Intelligent Transportation Systems-Based Machine Learning-Enhanced Traffic Prediction Model using Time Series Analysis and Regression Techniques*

An Intelligent Transportation Systems-Based Machine Learning...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Klea Elmazi Donald Elmazi Eugen Musta Fatjon Mehmeti Florenc Hidri Department of Computer Engineering Faculty of Engineering University of Rijeka University Luarasi Tirana Albania Department of Computer Engineering Faculty of Computer Science and IT Artificial Intelligence Laboratory University Metropolitan Tirana Tirana Albania Department of Finance Faculty of Economy Canadian Institute of Technology Tirana Albania Department of Computer Engineering University Metropolitan Tirana Tirana Albania Department of Software Engineering Faculty of Engineering Canadian Institute of Technology Tirana Albania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Traffic congestion represents a daunting challenge for all facets of urban development, as well as represents a universal problem in all urban areas, to various extents. In recent years, many cities have adopted the use of Intelligent Transport Systems (ITS) to manage traffic congestion. These systems are indeed useful, but they are mainly geared to predict real-time traffic congestion, yielding to a certain short-sightedness in our prediction model. Through the use of Time Series Analysis, and Regression models for traffic congestion prediction, we posit that we can address the issue. The data that these models were trained on derives from two datasets, namely a dataset from Kaggle and another from the road traffic footage of Tirana. The data then points to Gated Recurrent Units (GRU) being a more accurate time series category, and Support Vector Regression (SVR) to be a better performer than linear regression.

关键词： Support vector machines Analytical models Technological innovation Time series analysis Urban areas Transportation Predictive models

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