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Events and findings of the Purdue University Calumet Computer information technology & Graphics (CIT & G) department teaching partnership with the College of software engineering of Southeast University in Nanjing China 09

Events and findings of the Purdue University Calumet Compute...

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10th ACM Special Interest Group for information technology Education, SIGITE 2009

作者： Winer, Charles Wu, Hanqian Department of Computer Information Technology and Graphics Purdue University Calumet Hammond IN United States College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781605587653

In this poster session we are reporting on the results of two, three-week summer graduate teaching experiences that took place in Nanjing, China over a two-year period. A faculty exchange program was entered into between Southeast University of Nanjing China and Purdue University Calumet of Hammond, Indiana, USA. One of the goals of the exchange program was to expose Chinese students to the instructional methods employed by United States Universities. By understanding the cultural differences and utilizing various teaching methodologies employed by American teachers, the faculty and students involved in these three-week classroom intensive training courses were able to adapt and successfully complete the graduate level material that was presented.

关键词： Teaching

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DeepCancer: deep learning for brain tumor detection-based application system

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Neural Computing and Applications 2025年第7期37卷 5577-5596页

作者： AlShowarah, Suleyman A. Department of Software Engineering Faculty of Information Technology Mutah University Al-Karak Jordan

A brain tumor is the abnormal cells that growth in the brain, and it is considered as one of the most dangerous diseases that lead to the cause of death. Diagnosis at early is important for increasing the survival rate from the brain tumors. Specialists can identify the tumors manually, but it is very time and effort consuming, and are subject to human error, especially when dealing with large amounts of images. The automatic identification algorithms-based applications can facilitate the process. This study aimed to investigate the possibility of detecting brain cancer based on images using Deep Learning (DL) techniques and statistical operations. The features were extracted using two models of Convolutional Neural Network (CNN), (VGG-19 and AlexNet), then they were used to generate new datasets for statistical operations. CNN is used to extract features with distinct details from brain MRI images. The data were trained in three different training–testing data splitting ratios. Then, the features were classified based on the KNN, RF, and SVM to find the best accuracy of brain MRI image. At the end, the obtained classification accuracy was in favor of statistical operations especially for Large-Value, and Merge between features using KNN (99.1) and SVM (99.1). The features that extracted used in this study can provide high influence on the classification accuracy. The results across all three training–testing data splitting ratios were almost similar, and this approves that the brain cancer can be identified with high accuracy even if the training dataset sizes were minimal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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System for Alcohol Detection and Drowsiness Monitoring Based on Face and Drowsiness Recognition Model

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Journal of Institute of Control, Robotics and Systems 2025年第4期31卷 343-350页

作者： Jang, Soyoung Lee, Yeon Woo Jung, Yuna An, Eun Hyeon Lee, Jaeho Department of Information & Communication Technology Convergence Engineering Duksung University Korea Republic of Department of Software Duksung Universitys Korea Republic of

This study introduces an integrated real-time monitoring system to enhance driver safety. The system incorporates facial recognition, alcohol detection, and drowsiness monitoring to comprehensively analyze the driver’s condition and provide appropriate responses. For facial recognition, it utilizes Google’s MediaPipe AI framework and a Siamese network model. Alcohol detection is achieved through real-time measurement of the driver’s alcohol level using an MQ3 sensor. Drowsiness monitoring is accomplished through a combination of blink detection using dlib and OpenCV with head-rotation techniques to assess driver fatigue in real time. The proposed system offers customized information to drivers via Android and web interfaces. This research demonstrates the potential of systems that analyze driver safety and respond accordingly in real time. © ICROS 2025.

关键词： Face recognition

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NJmat 2.0:User Instructions of Data-Driven Machine Learning Interface for Materials Science

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Computers, Materials & Continua 2025年第4期83卷 1-11页

作者： Lei Zhang Hangyuan Deng Department of Internet Engineering School of Software EngineeringNanjing University of Information Science&TechnologyNanjing210044China Department of Materials Physics School of Chemistry and Materials ScienceNanjing University of Information Science&TechnologyNanjing210044China

NJmat is a user-friendly,data-driven machine learning interface designed for materials design and *** platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials *** platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular *** automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural ***,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding *** automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials *** latest version(available at https://***/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction *** utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials *** NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training *** providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.

关键词： Data-driven machine learning natural language processing machine learning potential large language model

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YOLOCSP-PEST for Crops Pest Localization and Classification

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Computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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YGC-SLAM:A visual SLAM based on improved YOLOv5 and geometric constraints for dynamic indoor environments

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虚拟现实与智能硬件(中英文) 2025年第1期7卷 62-82页

作者： Juncheng ZHANG Fuyang KE Qinqin TANG Wenming YU Ming ZHANG Department of Automation University of Information Science&TechnologyNanjing 210044China Department of Software University of Information Science&TechnologyNanjing 210044China Wuxi Research Institute of Nanjing University of Information Engineering Wuxi 214101China Department of Rail Transport University of WuxiWuxi 214101China

Background As visual simultaneous localization and mapping(SLAM)is primarily based on the assumption of a static scene,the presence of dynamic objects in the frame causes problems such as a deterioration of system robustness and inaccurate position *** this study,we propose a YGC-SLAM for indoor dynamic environments based on the ORB-SLAM2 framework combined with semantic and geometric constraints to improve the positioning accuracy and robustness of the *** First,the recognition accuracy of YOLOv5 was improved by introducing the convolution block attention model and the improved EIOU loss function,whereby the prediction frame converges quickly for better *** improved YOLOv5 was then added to the tracking thread for dynamic target detection to eliminate dynamic ***,multi-view geometric constraints were used for re-judging to further eliminate dynamic points while enabling more useful feature points to be retained and preventing the semantic approach from over-eliminating feature points,causing a failure of map *** K-means clustering algorithm was used to accelerate this process and quickly calculate and determine the motion state of each cluster of pixel ***,a strategy for drawing keyframes with de-redundancy was implemented to construct a clear 3D dense static point-cloud *** Through testing on TUM dataset and a real environment,the experimental results show that our algorithm reduces the absolute trajectory error by 98.22%and the relative trajectory error by 97.98%compared with the original ORBSLAM2,which is more accurate and has better real-time performance than similar algorithms,such as DynaSLAM and *** The YGC-SLAM proposed in this study can effectively eliminate the adverse effects of dynamic objects,and the system can better complete positioning and map building tasks in complex environments.

关键词： Visual SLAM Dynamic SLAM Target detection Geometric constraints

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Reliable Task Offloading for 6G-Based IoT Applications

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Computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks

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IEEE Access 2025年 13卷 70849-70871页

作者： Akram, Muhammad Ullah Bazai, Sibghat Imran Ghafoor, Muhammad Akram, Saira Ilyas, Qazi Mudassar Mehmood, Abid Iqbal, Sajid Asim Rafique, Muhammad Department of Software Engineering Quetta87300 Pakistan Department of Computer Engineering Quetta87300 Pakistan Pakistan Television Corporation Department of Engineering Lahore44000 Pakistan King Faisal University College of Computer Sciences and Information Technology Department of Information Systems Al Hofuf31982 Saudi Arabia King Faisal University College of Business Administration Department of Management Information Systems Al Hofuf31982 Saudi Arabia

Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifespan. To address these limitations and enhance the energy efficiency of WSNs, it is often necessary to divide sensors into clusters and establish routing to conserve energy. Machine learning algorithms can potentially automate these processes, minimizing energy consumption and extending network lifetime. This research investigates the application of machine learning algorithms, specifically Q-learning and K-means clustering, to propose the Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR) method for WSNs. This method facilitates cluster formation and routing path selection. The proposed method is compared with the well-established LEACH algorithm and two multi-hop variants, DMHT LEACH and EDMHT LEACH to validate its effectiveness. Our experimental results demonstrate the effectiveness of EEMLCR compared to LEACH and its multi-hop variants (DMHT LEACH and EDMHT LEACH). After 600 rounds in networks comprising 400 nodes, EEMLCR showed significant improvements in key performance metrics. These include increased alive nodes, reduced average energy consumption, higher remaining energy levels, and improved packet reception. Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency. By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes. The primary objective of this research is to contribute to the development of energy-efficient WSNs by leveraging machine learning algorithms for dat

关键词： Sensor nodes

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