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ABL-SMOTE: A Novel Resampling Method by Handling Noisy and Borderline Challenge for Imbalanced Dataset for Software Defect Prediction

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IEEE Access 2025年 13卷 78781-78794页

作者： Bashir, Kamal Abdelwahab Ghorashi, Sara Ahmed, Ali Khader, Abdolraheem Karary University College of Computer Science and Information Technology Department of Information Technology Omdurman12304 Sudan Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems. © 2013 IEEE.

关键词： Software testing

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PERTURBATION OF DISCRIMINANT FOR ONE-DIMENSIONAL DISCRETE SCHRÖDINGER OPERATOR WITH COMPLEX-VALUED SPARSE PERIODIC POTENTIAL

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Tohoku Mathematical Journal 2025年第1期77卷 77-91页

作者： Kaminaga, Masahiro Department of Information Technology Faculty of Engineering Tohoku Gakuin University 1-13-1 Chuo Miyagi Tagajo 985-0873 Japan

We consider the one-dimensional discrete Schrödinger operator with complex-valued sparse periodic potential. The spectrum for a complex-valued periodic potential is a complicated compact set in the complex plane represented by real intersections of algebraic curves determined by a discriminant. We represent the discriminant by Chebyshev polynomials and use perturbations of the discriminant to study the spectrum. © 2025 Tohoku University, Mathematical Institute. All rights reserved.

关键词： Complex-valued potential, periodic potential

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A Cross Attention Transformer-Mixed Feedback Video Recommendation Algorithm Based on DIEN

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Computers, Materials & Continua 2025年第1期82卷 977-996页

作者： Jianwei Zhang Zhishang Zhao Zengyu Cai Yuan Feng Liang Zhu Yahui Sun College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450000China Faculty of Information Engineering Xuchang Vocational Technical CollegeXuchang461000China College of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450000China College of Electronics and Information Zhengzhou University of Light IndustryZhengzhou450000China

The rapid development of short video platforms poses new challenges for traditional recommendation *** systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term ***,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior ***,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their *** paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)*** study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention ***,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user *** results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance *** advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.

关键词： Video recommendation user interest cross-attention transformer

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Machine Learning for Smart Soil Monitoring

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Computers, Materials & Continua 2025年第5期83卷 3007-3023页

作者： Khaoula Ben Abdellafou Kamel Zidi Ahamed Aljuhani Okba Taouali Mohamed Faouzi Harkat Faculty of Computers and Information Technology University of TabukTabuk71491SaudiArabia Applied College University of TabukTabuk71491SaudiArabia Department of Electronics Faculty of Engineering AnnabaBadji Mokhtar BP.12Annaba23000Algeria

Environmental protection requires identifying,investigating,and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural *** process of environmental protection is essential for maintaining human *** this context,it is critical to monitor and safeguard the personal environment,which includes maintaining a healthy diet and ensuring plant *** in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our *** ensure soil quality,it is imperative to monitor and assess the levels of various soil ***,an Optimized Reduced Kernel Partial Least Squares(ORKPLS)method is proposed to monitor and control soil *** approach is designed to detect increases or deviations in soil parameter quantities.A Tabu search approach was used to select the appropriate kernel ***,soil analyses were conducted to evaluate the performance of the developed *** simulation results were analyzed and *** this study,deficiencies or exceedances in soil parameter quantities can be *** proposed method involves determining whether each soil parameter falls within a normal *** allows for the assessment of soil parameter conditions based on the principle of fault detection.

关键词： Systems security soil analyses kernel partial least squares(KPLS) optimized reduced kernel partial least squares(ORKPLS) tabu search process monitoring machine learning fault detection(FD)

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Research on Improved TDOA Algorithm for Shallow Water Underwater Positioning 16

Research on Improved TDOA Algorithm for Shallow Water Underw...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Shen, Biyue Rong, Shaowei Ji, Lingxian Wu, Kaiyuan Li, Jiaju Deng, Xiangyu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Faculty of Artificial Intelligence Industry Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9781510689251

Accurate positioning in shallow water regions is crucial for various underwater applications. TDOA algorithms estimate the signal source location by measuring the time differences of signal arrival at multiple receivers. However, these methods often suffer from Non-Line-of-Sight (NLOS) errors and noise, leading to reduced positioning accuracy in shallow water environments. This research proposes an improved TDOA estimation algorithm that utilizes cross-correlation techniques to mitigate NLOS errors and combines the strengths of the Chan and Taylor algorithms to enhance positioning precision. The proposed algorithm addresses challenges such as time ambiguity and alpha-stable noise, offering advantages in TDOA modeling for underwater positioning. Implementation details are provided, including the design of matching basis functions, time delay matching procedures, algorithm flow, and computational steps. Simulation tests validate the effectiveness of the algorithm, demonstrating significant improvements in positioning accuracy. The computational complexity is also analyzed to assess the algorithm's practicality. Finally, the research findings are summarized, and directions for future work are discussed. © 2025 SPIE.

关键词： Time difference of arrival

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for Computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Evaluation of commercial space accessibility in tourist town: A case research of Maotai Town in China

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Frontiers of Architectural Research 2025年第4期14卷 965-982页

作者： Xu, Lai Liu, Yuwei Shen, Jingmi Tan, Jie Hutter, Akos Marcel Breuer Doctoral School Faculty of Engineering and Information Technology University of Pecs Pecs Hungary Department of Production Design Faculty of Art and Design Hankou University Wuhan430212 China Department of Arts and Design Faculty of Fine Arts Chiang Mai University Chiang Mai Thailand Department of Architecture and Urban Planning Institute of Architecture Faculty of Engineering and Information Technology University of Pecs Pecs Hungary

Accessibility is a crucial indicator for evaluating the spatial rationalization of commercial facilities in an area, impacting both commercial development and service efficiency. The objective of this research is to analyze the commercial space layout of Maotai Town in Guizhou Province, China, and to design an evaluation system for commercial space accessibility to enhance the efficiency of commercial space use. Firstly, POI (Point of Interest) Data Visualization Analysis will be employed to identify major commercial hotspots and areas not covered by their service reach. Secondly, the spatial accessibility of commercial spaces will be evaluated using Space Syntax and the Cost-Weighted Model in the topological grid dimension and geometric grid dimension. Finally, we will establish a Minimum Cumulative Resistance Model (MCR Model) based on the analysis results obtained to identify the resistance paths that tourists navigate within the research area. This method will provide a precise framework for evaluating the accessibility of commercial spaces throughout the area. © 2025 The Author(s)

关键词： Accessibility Commercial space Space syntax Cost-weighted distance model MCR model

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Cross-Scenario Device-Free Wireless Sensing with A Free-Energy View

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IEEE Internet of Things Journal 2025年第11期12卷 16610-16621页

作者： Chen, Bo Wang, Jie Liu, Xiaokai Gao, Qinghua Pan, Miao Fang, Yuguang Dalian Maritime University School of Information Science and Technology Dalian116026 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116023 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Device-free wireless sensing (DFWS) has gained significant attention due to its high accuracy and privacy-preserving capabilities. DFWS systems work by analyzing the influence pattern of targets on the surrounding wireless signals. However, changes in the sensing scenario can alter signal propagation patterns, causing deep learning models to lose effectiveness in cross-scenario applications. To address this problem, we analyze the information content of different samples from a free-energy view, and provide a new idea to guide the alignment of target scenario samples with source scenario samples based on free-energy. We find that free-energy can measure the degree of scenario knowledge contribution of the samples. Based on this observation, we first perform unsupervised coarse alignment by minimizing the free-energy deviation between scenarios. Next, we iteratively select a few number of high free-energy samples near the decision boundary to fine-Tune the network, achieving scenario fine alignment with a small labeling effort. Extensive experiments on two public datasets and one self-collected dataset show that our proposed method achieves high accuracy in cross-scenario human activity and gesture recognition tasks. © 2014 IEEE.

关键词： Labeling Sensors Wireless sensor networks Wireless communication Feature extraction Costs Internet of Things Gesture recognition Metalearning Training

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IoT-integrated smart toilet design for enhancing safety, precaution, and energy efficiency

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Journal of Building Pathology and Rehabilitation 2025年第2期10卷 1-14页

作者： Habib, Muhammad Irfan Muke, Patient Zihisire Machado, José Mendes Department of Electrical Engineering Technology National Skills University Islamabad Islamabad Pakistan Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wrocław Poland Department of Mechanical Engineering Minho University Braga Portugal

This research paper aims at enhanced sanitation and energy efficiency in public toilets and the safety of users. Conventional toilet facilities lack the necessary infrastructure to ensure cleanliness and operational efficiency. This seriously hampers personal hygiene, public health, and the effective use of our most precious resources (water, energy, etc.). This research paper discusses an effective design of smart toilets empowered by IoT technologies, which can modernize old toilets in public places. Smart toilets use sensors like humidity, temperature, oxygen, infrared, leak detection, lux, and pressure sensors for real-time monitoring. All connected sensors work perfectly in tandem to save on important resources and keep the user safe by automatically performing the required functions in the system. Such Operations are controlling the water flow and monitoring the environment, as well as alerting upon detection of a leak. Proteus was used to simulate the circuit, and a smart toilet system design using Arduino Mega as well as ESP8266 modules can help lessen injuries and save energy. The results of this study highlight that how IoT-enabled smart toilets can modify public toilets for sustainability as well as responsiveness. Smart toilets make urban spaces safer and greener in line with modern urban infrastructure requirements and the mission of energy environmental responsibility. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Arduino Mega Energy management ESP8266 WIFI module IoT technology Smart toilet

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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Computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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