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2022 Workshop on Advances in Computational Intelligence, its Concepts and applications, ACI 2022

作者： Sharma, Hitesh K. Choudhury, Tanupriya Ahlawat, Prashant Mohanty, Sachi N. Jain, Sarika Dehradun248007 India Dept. of Computer Science Chandigarh University Punjab 140413 India Department of Computer Science Singidunum University Serbia and and School of Computer Science & Engineering VIT-AP University Andhra Pradesh Amaravati India Dept. of Computer Application National Institute of Technology Haryana Kurukshetra136119 India

With the enhancement of the technology, expand of businesses and thoughts more and more people are applying for the loans. Both for their personal use and for business use. But due to the limited amount of assets the bank cannot grant loans to each and every person. Finding out those right people is a typical and time-consuming process. Banks desire to deliver the loan to an individual who can be recompensate the loan on time and can afford maximum profit to the bank. So there is a need of a system which could do this analysis and save the banks time and resources. This can be done using Machine Learning. The objective of this paper is to create a more accurate loan prediction model using machine learning to reduce the risk behind selecting of appropriate people for the loan. For this we'll mine the previous records of the people to whom the bank has granted loan. Using these records variables and bank loan rules we will train a machine learning model which will predict that a person is eligible for loan or not. We will use sklearn for our model and train_test_split for splitting the data set into train dataset and test dataset. Here we are going to use various models like Logistic Regression, Decision Tree(DT) and Random Forest(RF) so as to fetch more accurate results as the given problem is a supervised classification problem. © 2020 Copyright for this paper by its authors.

关键词： Forecasting

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Bioinspired Clustering in UWSNs Using Monarch Butterfly Optimization Compared with Ant Colony Optimization Algorithm

Bioinspired Clustering in UWSNs Using Monarch Butterfly Opti...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： G.Indira Ruchika Rani D Chiranjevi Saif O. Husain Rahul Yogi Nandhini T J Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering Aditya Engineering College India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-SIMATS Saveetha University Chennai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The Monarch Butterfly Optimization (MBO) algorithm for clustering in Underwater Wireless Sensor Networks (UWSNs) is investigated and compared to the performance of the Ant Colony Optimization (ACO) algorithm in this paper. As is evident from the outcomes depicted in the above graphs MBO consumes an average of 31% less energy than the ACO while lasting longer in the network. 7J or make a network get 790 round life cycle while ACO did 34. 5J and 730 rounds. The results indicate that MBO is more advantageous to cluster in the UWSNs making it a viable solution to boost up the network productivity and the overall energy utilization efficiency.

关键词： Wireless communication Productivity Wireless sensor networks Ant colony optimization Signal processing algorithms Clustering algorithms Signal processing Energy efficiency Telecommunication computing Optimization

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LSTM-based Pedestrian Trajectory Prediction Model under the General Direction Mechanism: DIR-LSTM 3

LSTM-based Pedestrian Trajectory Prediction Model under the ...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Luo, Junyuan Fu, Qiongfang Xiao, Jianhua Xiao, Hongbo Liao, Jiyong School of Computer Science and Engineering Huaihua University Hunan Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Hunan Huaihua China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (纸本)9798400716775

Accurate prediction of future traffic flow trends is essential to solve urban transportation problems. However, traffic flow prediction faces great challenges due to the multimodal nature of pedestrian behavior and the complexity of the traffic environment. Although a large number of studies have been conducted to investigate these issues in depth, there are still some limitations. In order to address these challenges more effectively, we propose a pedestrian trajectory prediction model based on long-short-term memory networks (LSTMs): the DIR-LSTM. The model introduces an innovative generalized direction mechanism and a self-attention mechanism, which captures pedestrian movement patterns more comprehensively and accurately by predicting overall directional movements first and then gradually subdividing them into individual directions of movement. The DIR-LSTM is designed to address the challenges posed by the diversity of pedestrian behaviors and the complexity of urban environments. To validate the state-of-the-art of the model, we conducted experiments using the publicly available ETH [10] and UCY [9] datasets. The experiments demonstrate that DIR-LSTM performs better in terms of accuracy compared to other models, providing a more reliable prediction tool for future urban traffic management. © 2023 ACM.

关键词： Long short-term memory

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Interference coordination techniques for D2D communications in a spectrum sharing environment

Interference coordination techniques for D2D communications ...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International Conference on

作者： Inzimam Ranganathaswamy M K Ashwini Kumar Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

device-to-tool (D2D) communications has become an essential a part of allowing understanding transfer, records sharing, and collaboration. As such, green usage of spectrum assets for D2D communications in a spectrum-sharing surroundings is important for making sure equity and advanced consumer revel in. This paper proposes diverse interference coordination techniques for D2D communications in a spectrum sharing environment. These strategies help in enhancing Qu’s by means of mitigating interference inside the goal region, while ensuring minimal disruption to the existing spectrum allocations. The proposed strategies include: Node Clustering and Grid Partitioning; Adaptive carrier Sensing and Dynamic Channel Allocation; Interference Cancellation and signal Combining; and more than one access techniques. The usage of simulation results, the efficacy of the proposed techniques become evaluated under various degrees of interference in the environment. Consequences display that these strategies drastically improve overall performance for a wide variety of applications, together with streaming films, voice communique, and information transfer.

关键词： Time-frequency analysis Simulation Interference Medical services Machine learning Device-to-device communication Sensors

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Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles

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Journal of Mobile Multimedia 2023年第2期19卷 477-496页

作者： Upadhye, Sachin Neelakandan, S. Thangaraj, K. Vijendra Babu, D. Arulkumar, N. Qureshi, Kashif Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India Department of CSE R.M.K Engineering College Chennai India Department of IT Sona College of Technology Salem India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore 632 014 India Department of Data Science and Statistics CHRIST (Deemed to be University) Bangalore India Department of CSE Sanskriti University Uttar Pradesh Mathura India

Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975. © 2022 River Publishers.

关键词： Deep learning speed estimation traffic flow monitoring unmanned aerial vehicles vehicle counting video surveillance

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MOOC Dropout Prediction Using Learning Process Model and LightGBM Algorithm 18th

MOOC Dropout Prediction Using Learning Process Model and Lig...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Nie, Hejing Wen, Yiping Cao, Buqing Liang, Bowen School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China The 14Th Five-Year Plan" Research Base for Education Science Education Informatization Research Base-Technology Application Direction Hunan China

ISBN: (纸本)9789819996360

With the development and widespread application of Massive Open Online Courses (MOOC) platforms, the issue of reducing learners’ dropout has become a challenge. Therefore, it is necessary to accurately predict whether a learner will complete the course or not. Existing researches mainly use machine learning methods to predict MOOC dropout, which still face challenges such as inadequate explanation and low prediction accuracy. In view of these problems, this paper proposes a MOOC dropout prediction method using learning process model and LightGBM algorithm. This method utilizes the learning process model to analyze learning behavior and generate feature vectors to provide a clear interpretation. Based on these feature vectors, the LightGBM algorithm is adopted to predict dropout. Compared to the related methods, the dropout prediction method proposed in this paper demonstrates improvements of 1.58%, 1.2%, 1.1%, and 0.864% in Recall, F1, Precision, and AUC respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Vision-Based Locomotion Control for Humanoid Robots: A Study on Vision-Guided Walking Strategies

Vision-Based Locomotion Control for Humanoid Robots: A Study...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Saroj Kumar Pandey Lalan Kumar Gaurav Kumar Ankit Kumar Kamred Udham Singh Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun

Recently, there has been a lot of research on using vision to direct humanoid robots as they walk. Focusing on the major methodologies, difficulties, and prospects, this review article presents an overview of the current state of study in the vision-guided walking of humanoid robots. In the first part of the study, we introduce vision-guided walking and go through the several methods that have been developed to execute it, such as visual serving, visual odometer, and visual simultaneous localization and mapping. The difficulties of vision-guided walking are then discussed; these include, but are not limited to, the necessity for accurate and strong computer vision algorithms, the robot's need to have a solid awareness of its own body and how it moves, and the need to maintain balance while walking. This paper provides a detailed review of the research on humanoid robots that use vision to direct their gait. Deep learning for vision-guided walking, tactile and proprioceptive sensing combined with vision, and reinforcement learning for adaptive walking are only a few of the issues covered in this overview. Future directions in vision-guided walking research are discussed in this paper. These include the development of more advanced and reliable computer vision algorithms, the incorporation of other modalities like tactile and auditory sensing, and the investigation of new applications for vision-guided walking in fields like healthcare and rehabilitation.

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Construction Equipment Performance with Cloud Data Analysis for Predictive Maintenance 6

Construction Equipment Performance with Cloud Data Analysis ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381689

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： Predictive maintenance

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FedBCD: Federated Ultrasound Video and Image Joint Learning for Breast Cancer Diagnosis

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2395-2407页

作者： Deng, Tianpeng Huang, Chunwang Cai, Ming Liu, Yu Liu, Min Lin, Jiatai Shi, Zhenwei Zhao, Bingchao Huang, Jingqi Liang, Changhong Han, Guoqiang Liu, Zaiyi Wang, Ying Han, Chu South China University of Technology School of Computer Science and Engineering Guangdong Guangzhou510006 China Department of Ultrasound Guangzhou510080 China Department of Radiology Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Sun Yat-sen University Cancer Center Collaborative Innovation Center for Cancer Medicine Department of Ultrasound State Key Laboratory of Oncology in South China Guangzhou China The First Affiliated Hospital of Guangzhou Medical University Department of Medical Ultrasonics Guangzhou China

Ultrasonography plays an essential role in breast cancer diagnosis. Current deep learning based studies train the models on either images or videos in a centralized learning manner, lacking consideration of joint benefits between two different modality models or the privacy issue of data centralization. In this study, we propose the first decentralized learning solution for joint learning with breast ultrasound video and image, called FedBCD. To enable the model to learn from images and videos simultaneously and seamlessly in client-level local training, we propose a Joint Ultrasound Video and Image Learning (JUVIL) model to bridge the dimension gap between video and image data by incorporating temporal and spatial adapters. The parameter-efficient design of JUVIL with trainable adapters and frozen backbone further reduces the computational cost and communication burden of federated learning, finally improving the overall efficiency. Moreover, considering conventional model-wise aggregation may lead to unstable federated training due to different modalities, data capacities in different clients, and different functionalities across layers. We further propose a Fisher information matrix (FIM) guided Layer-wise Aggregation method named FILA. By measuring layer-wise sensitivity with FIM, FILA assigns higher contributions to the clients with lower sensitivity, improving personalized performance during federated training. Extensive experiments on three image clients and one video client demonstrate the benefits of joint learning architecture, especially for the ones with small-scale data. FedBCD significantly outperforms nine federated learning methods on both video-based and image-based diagnoses, demonstrating the superiority and potential for clinical practice. Code is released at https://***/tianpeng-deng/FedBCD. © 1982-2012 IEEE.

关键词： Fisher information matrix

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Deep Hypergraph Neural Networks with Tight Framelets 39

Deep Hypergraph Neural Networks with Tight Framelets

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Ming Fang, Yujie Wang, Yi Feng, Han Gu, Yongchun Bai, Lu Lio, Pietro Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China Zhejiang Institute of Optoelectronics China School of Computer Science and Technology Zhejiang Normal University China Department of Mathematics City University of Hong Kong Hong Kong School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University China Department of Computer Science and Technology Cambridge University United Kingdom

ISBN: (纸本)157735897X

Hypergraphs provide a flexible framework for modeling high-order (complex) interactions among multiple entities, extending beyond traditional pairwise correlations in graph structures. However, deep hypergraph neural networks (HGNNs) often face the challenge of oversmoothing with increasing depth, similar to issues in graph neural networks (GNNs). While oversmoothing in GNNs has been extensively studied, its implications in relation to hypergraphs are less explored. This paper addresses this gap by first theoretically exploring the reasons behind oversmoothing in deep HGNNs. Our novel insights suggest that a spectral-based hypergraph convolution, equipped with both low-pass and high-pass filters, can potentially mitigate these effects. Motivated by these findings, we introduce FrameHGNN, a framework that utilizes framelet-based hypergraph convolutions integrating tight framelet transforms with both low-pass and high-pass components, as well as the commonly used strategies in designing deep GNN architecture: initial residual and identity mappings. The experiment results on diverse benchmark datasets demonstrate that FrameHGNN outperforms several state-of-the-art models, effectively reducing oversmoothing while improving predictive accuracy. Our contributions not only advance the theoretical understanding of deep hypergraph learning but also provide a practical spectral-based approach for HGNNs, emphasizing the design of multifrequency channels. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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