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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

Neutrosophic Sets and Systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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Neutrosophic Sets and Systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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An Adaptive Framework for Classification and Detection of Android Malware

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 59-73页

作者： Al Sharah, Ashraf Alrub, Yousef Abu Owida, Hamza Abu Elsoud, Esraa Abu Alshdaifat, Nawaf Khtatnaha, Hamzah Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Information Technology Applied Science Private University Amman Jordan

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. © 2024 by the authors of this article.

关键词： Android malware

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A fusion of CNN And SIFT For multicultural facial expression recognition

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Multimedia Tools and Applications 2025年 1-19页

作者： Qadir, Inam Iqbal, Muhammad Amjad Ashraf, Samman Akram, Sheeraz Department of Computer Science University of Central Punjab Lahore Pakistan Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan Lahore Pakistan

The most generic and understandable way of communication is by observing facial expressions;Facial Expression Recognition(FER) performance was affected by the differences in ethnicity, culture, and geography. This research proposes a feature grafting-based novel technique to recognize multicultural facial expressions. The Viola-Jones algorithm detects the facial region of an image that is most concentrated. Feature Grafting is used to combine high-level and low-level features. A multicultural facial expression database was created using still images of male and female subjects obtained from three datasets: TFEID, Radboud, and JAFFE. The model is designed and tested using this dataset and the proposed technique. The obtained accuracy using the proposed technique was 94.34% which is better than the state-of-the-art method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Grafting (chemical)

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RELIANCE: Reliable Ensemble Learning for information and News Credibility Evaluation 20

RELIANCE: Reliable Ensemble Learning for Information and New...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Ramezani, Majid Mohammad-Shahi, Hamed Daliry, Mahshid Rahmani, Soroor Asghari, Amirhossein Faculty of Computer Science and Information Technology Zanjan Iran

ISBN: (纸本)9798350383942

In the era of information proliferation, discerning the credibility of news content poses an ever-growing challenge. This paper introduces RELIANCE, a pioneering ensemble learning system designed for robust information and fake news credibility evaluation. Comprising five diverse base models, including Support Vector Machine (SVM), naïve Bayes, logistic regression, random forest, and Bidirectional Long Short Term Memory Networks (BiLSTMs), RELIANCE employs an innovative approach to integrate their strengths, harnessing the collective intelligence of the ensemble for enhanced accuracy. Experiments demonstrate the superiority of RELIANCE over individual models, indicating its efficacy in distinguishing between credible and non-credible information sources. RELIANCE, also surpasses baseline models in information and news credibility assessment, establishing itself as an effective solution for evaluating the reliability of information sources. © 2024 IEEE.

关键词： Learning systems

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Sustainable Water Futures: Enhancing Efficiency And Conservation In Madinah AL Munawara

Journal of Applied Science and Engineering

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Journal of Applied Science and Engineering 2024年第10期28卷 2027-2042页

作者： Syed, Toqeer Ali Naqash, Muhammad Tayyab Nawaz, Waqas Namoun, Abdallah Muhammad, Munir Azam Faculty of Computer and Information System Islamic University of Madinah Saudi Arabia Civil Engineering Department Faculty of Engineering Islamic University of Madinah Madinah42351 Saudi Arabia AI Center Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Khwaja Fareed University of Engineering & Information Technology Faculty of Computer and Information System Punjab Rahim Yar Khan Pakistan

Water resource management worldwide faces significant challenges, including high consumption rates, scarcity, and ageing infrastructure. This paper proposes a comprehensive approach to address these issues through five key solutions. Advanced Metering Infrastructure (AMI) first facilitates real-time data collection and consumer awareness campaigns. Secondly, wastewater recycling and reuse are promoted by upgrading wastewater treatment plants and fostering public-private partnerships. Thirdly, reducing network losses is achieved through leak detection systems and regular maintenance. Fourthly, policy enhancement and regulatory frameworks are strengthened by implementing regulations for new developments and incentives for water-saving technologies. Lastly, community engagement and education initiatives are undertaken to promote sustainable water practices. This multifaceted strategy aims to improve water conservation, efficiency, and resilience of the water supply system. This paper proposes solutions that can be applied to the Madinah AL Munawara region of Saudi Arabia, an area of utmost importance, demonstrating the applicability and effectiveness of these solutions in addressing regional water management challenges. © The Author(’s).

关键词： Wastewater treatment

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Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 149-170页

作者： Alsarhan, Ayoub Alnatsheh, Athari Aljaidi, Mohammad Al Makkawi, Tuqa Aljamal, Mahmoud Alsarhan, Tamam Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan King Abdullah II School of Information Technology The University of Jordan Amman Jordan

The rapid adoption of electric vehicles (EVs) has created a pressing need for efficient charging infrastructure. but challenges such as inconsistent demand and poor placement remain. An effective distribution of sufficient EV charging stations (CSs) is one of the major obstacles preventing the market penetration of EVs and the realization of a sustainable transportation system in urban areas. In this paper, a new machine learning technique is proposed in order to optimize the placement of EV charging stations (EVCSs) in metropolitan areas based on an energy consumption prediction model. A dataset from 148,136 charging transactions in Boulder, Colorado, is used with the proposed model. Key algorithms such as KNeighborsRegressor and RandomForestRegressor were incorporated to solve the placement problem. The analysis revealed significant demand fluctuations during peak commute hours, with the KNeighborsRegressor model demonstrating superior prediction accuracy. These insights can guide more effective infrastructure planning and resource allocation, ultimately enhancing the efficiency and user experience of EV charging networks and promoting sustainable urban transportation. © 2024 by the authors of this article.

关键词： Charging stations

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Generative adversarial networks based motion learning towards robotic calligraphy synthesis

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CAAI Transactions on Intelligence technology 2024年第2期9卷 452-466页

作者： Xiaoming Wang Yilong Yang Weiru Wang Yuanhua Zhou Yongfeng Yin Zhiguo Gong Department of Computer and Information Science University of MacaoMacaoChina School of Software Beihang UniversityBeijingChina Department of Computer Science and Technology Faculty of Information TechnologyBeijing University of TechnologyBeijingChina School of Foreign Languages Guangzhou Huashang CollegeGuangzhouChina

Robot calligraphy visually reflects the motion capability of robotic *** traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters,this article presents a generative adversarial network(GAN)-based motion learning method for robotic calligraphy synthesis(Gan2CS)that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy *** key technologies in the proposed approach include:(1)adopting the GAN to learn the motion parameters from the robot writing operation;(2)converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration;(3)reproducing high-precision calligraphy works by synthesising the writing motion data *** this study,the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot *** robot performs the writing with motion planning,and the writing motion parameters of calligraphy strokes are learnt with *** the motion data of basic strokes is synthesised based on the hierarchical process of‘stroke-radicalpart-character’.And the robot re-writes the synthesised characters whose similarity with the original calligraphy characters is *** calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.

关键词： calligraphy synthesis generative adversarial networks Motion learning robot writing

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MCBAN: A Small Object Detection Multi-Convolutional Block Attention Network

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computers, Materials & Continua 2024年第11期81卷 2243-2259页

作者： Hina Bhanbhro Yew Kwang Hooi Mohammad Nordin Bin Zakaria Worapan Kusakunniran Zaira Hassan Amur Computer and Information Science Department Universiti Teknologi PETRONASSeri Iskandar31750Malaysia Faculty of Information and Communication Technology Mahidol UniversityNakhon Pathom73170Thailand

Object detection has made a significant leap forward in recent ***,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susceptible to missed detection due to background ***,small object information is affected due to the downsampling *** learning-based detection methods have been utilized to address the challenge posed by small *** this work,we propose a novel method,the Multi-Convolutional Block Attention Network(MCBAN),to increase the detection accuracy of minute objects aiming to overcome the challenge of information loss during the downsampling *** multi-convolutional attention block(MCAB);channel attention and spatial attention module(SAM)that make up MCAB,have been crafted to accomplish small object detection with higher *** have carried out the experiments on the Karlsruhe Institute of technology and Toyota Technological Institute(KITTI)and Pattern Analysis,Statical Modeling and Computational Learning(PASCAL)Visual Object Classes(VOC)datasets and have followed a step-wise process to analyze the *** experiment results demonstrate that significant gains in performance are achieved,such as 97.75%for KITTI and 88.97%for PASCAL *** findings of this study assert quite unequivocally the fact that MCBAN is much more efficient in the small object detection domain as compared to other existing approaches.

关键词： Multi-convolutional channel attention spatial attention YOLO

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Orbit Weighting Scheme in the Context of Vector Space information Retrieval

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computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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