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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Kumari, Sapna Raza, Muhammad Owais Kumari, Arsha Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Dept. of Computer Science Hyderabad Pakistan University of Sufism and Modern Sciences Dept. of Information and Computing Bhitshah Pakistan Mehran University of Engineering & Technology Dept. of Electronics Engineering Jamshoro Pakistan

ISBN: (纸本)9798350335316

In the last few decades, tremendous change is observed in rainfall patterns which are majorly influenced by two major factors 1) climate change and 2) CO2 emission. Erratic rainfall patterns caused catastrophic effects on agriculture and human life in developing countries like Pakistan, where major economic growth is largely dependent on agriculture. The main objective of this study is to evaluate a performance different Machine learning algorithms for forecasting rainfall patterns using dimensionality reduction techniques on climate change indicators. For this purpose rainfall data was collected for Pakistan. Principle component analysis (PCA), Pearson correlation, and Greedy search algorithms were used for feature selection and the evolution of models was performed using Root Mean Square error (RMSE), Root Absolute Error (RAE), and Coefficient of determination metrics. Results show that features obtained using the Pearson correlation produced the least error and Bayesian linear regression performed with the highest accuracy followed by Neural Network regression. © 2023 IEEE.

关键词： Climate change

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A Novel Design of Image Based Object Recognition Model Using Enhanced Neural Classification Logic

A Novel Design of Image Based Object Recognition Model Using...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Anvar Shathik J B Ravi Chandra M D Nandeesh T.C. Maniunath Swathi Pothala Nageswara Rao Lavuri Department of Computer Science & Engineering Anjuman Institute of Technology and Management Bhatkal Karnataka India Department of Electronics and Communication Engineering G.Pullaiah College of Engineering and Technology Kurnool Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bangalore Department of Computer Science Engg. IoT Cyber Security & Block Chain Technology Dean Research (R & D) Rajarajeswari College of Engineering Bangalore Karnataka India Department of Computer Science S V University Tirupati Department of Electronics and Communication Engineering CVR College of Engineering Telangana India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

The field of computer vision is a hard-hitting domain to detect and recognition objects. There are many strategies and algorithms to help in item detection in natural environments. The surveillance system and the image processing system monitor deflection of human and other object behaviors. Certainly the main things object detection is composed of are feature extraction and categorization. The method employed by this research is Enhanced Neural Network classifier (ENCL) based training of photos for object detection. Machine learning is used by computer vision. We are just proposing this research to create a new design and approach of a system that is capable of detecting objects and whenever an object is recognized as a specific label, the system will generate a pop up message. We use image process methods to build up the system. Thus, this experiment is conducted to know if the system can detect the object and determine the accuracy of the proposed system.

关键词： Training Support vector machines computer vision Accuracy Surveillance Computational modeling Object detection Artificial neural networks Feature extraction Object recognition

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Transmission System Expansion Planning Using Optimal Power Flow and Genetic Algorithm

Transmission System Expansion Planning Using Optimal Power F...

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2022 International Conference on Power, Energy and Innovations, ICPEI 2022

作者： Wiwechpaisankul, Weeratep Rattananatthawon, Ongorn Sirisumrannukul, Somporn Sabpayakom, Noppatee. Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand College of Industrial Technology of Power Electronics Engineering Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9781665460194

ISBN: (纸本)9781665460194

Transmission system expansion planning (TSEP) is the process of identifying time, location, and the number of transmission facilities will be needed in the future. TSEP is an optimization problem with the objective to minimize the investment cost to ensure supply-demand adequacy, subject to constraints on the power balance and the apparent power capability limit of transmission lines. This paper presents transmission system expansion planning based on an optimal power flow and a genetic algorithm (GA). The OPF problem was solved by the PowerWorld simulator, and the GA was coded in Python and developed on the built-in SimAuto function in the PowerWorld simulator. The simulation results from a case study of a modified Garver's 6-bus power system show that the developed methodology can determine the optimal sites and capacities of transmission lines to be added to the system. © 2022 IEEE.

关键词： Genetic algorithms

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Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data 2

Customer Churn Prediction in the Telecom Sector with Machine...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Aravinda, N.L. Reddy, R. Archana Bala Dhandayuthapani, V. Nayak, Jithendra P. R. Husseen, Ahmad Electronics and Communication Engineering Department Telangana Hyderabad India School of Computer Science and Engineering Sr University Telangana Warangal India University of Technology and Applied Sciences Shinas Branch Department of It College of Computing and Information Sciences Oman Srinivas Institute of Technology Department of Cse Valachil Mangalore India The Islamic University Najaf Iraq

ISBN: (纸本)9798350382952

A crucial aspect of maintaining a customer-oriented business in the telecommunications sector with machine learning (ML) is understanding the reasons and factors that lead to customer churn. However, the dataset is difficult by noise, misclassifications, duplicated data, and imbalanced customer information complicating the process of identifying ways to split the dataset based on events. The under-sampling method address the imbalance data by decreasing the data quantity of majority class, thereby achieving a balanced dataset. This method, utilizing Adaptive k-means clustering, directly determines the reduction based on that data quality in each class. The datasets are combined with class labels to generate a new balanced dataset used for classification in the SVM algorithm, which distinguishes between customer churn and non-churn in the prediction for the telecom sector. The SVM algorithm can perform linear and non-linear classification with a kernel function to control overfitting improve generalization and handle high-dimensional data. The obtained results demonstrate that the proposed method achieves better accuracy of 95.70%, precision of 56.01%, Recall of 55.05%, and F1-score of 96.05% on the telecom churn dataset. These results ensure superior detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Decision trees

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Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

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computer Systems Science & engineering 2023年第7期46卷 303-319页

作者： Imran Ali Zohaib Mushtaq Saad Arif Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Mechanical Engineering National Taiwan University of Science and TechnologyTaipei City106335TaiwanChina Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Mechanical Engineering HITEC University TaxilaTaxila Cantt.47080Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Hyperspectral imaging is gaining a significant role in agricultural remote sensing *** data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third *** classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral *** this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral *** dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each *** data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation *** this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in *** preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired *** the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral *** resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational *** support vector machines classifier was employed for multiclass classification of target crops using *** classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa *** proposed scheme achieved greater than 90%results for all the performance evaluation *** PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR *** proposed scheme

关键词： Hyperspectral imaging visible and near-infrared edge preserving feature dimensionality reduction crop classification

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Insurance Amount Prediction Based on Accidental Car Damage Level using AI

Insurance Amount Prediction Based on Accidental Car Damage L...

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International Conference on Communication and electronics Systems (ICCES)

作者： D. Manoj Kumar Chadipriala Nithin Kumar Reddy Gunisetty Venkata Sai Puligundla Sujan Chowdary Electronics and Computer Engineering SRM Institute of Science and Technology Chennai India

This document presents a machine learning-based approach for predicting the insurance amount for a car damage claim based on the level of damage caused by an accident. The proposed model uses a dataset of past car damage claims, including the level of damage and the corresponding insurance amount paid out. The dataset is subjected to the application of a number of machine learning algorithms, such as decision trees, random forests, and neural networks. The effectiveness of these algorithms is assessed using a variety of measures, including accuracy, precision, recall, and F1-score. The results show that the model achieves high accuracy and F1-score, indicating its effectiveness in predicting the insurance amount based on the level of damage. This proposed approach has the potential to improve the accuracy and efficiency of the insurance claims process, ultimately benefiting both insurance companies and their clients. Insurance companies use a variety of factors to determine the amount of compensation paid out to a customer for a car damage claim. One of the most important factors is the level of damage caused by the accident. However, accurately assessing the level of damage can be a difficult undertaking, as it requires a high level of expertise and experience. To address this challenge, machine learning techniques can be used to build models that can predict the insurance amount based on the level of damage. In order to do this, a model must be trained using past data to identify trends and connections between the severity of the harm and the associated insurance payouts. Overall, insurance amount prediction based on accidental car damage level using AI is a promising area of research and has the potential to benefit both insurance companies and their clients.

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Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9899-9903页

作者： Zhang, Yichi Zhang, Yuchen Zhu, Lipeng Xiao, Sa Tang, Wanbin Eldar, Yonina C. Zhang, Rui University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China Kash Institute of Electronics and Information Industry Kash844000 China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Chinese University of Hon g Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China

In this correspondence, we propose a movable antenna (MA)-aided multi-user hybrid beamforming scheme with a sub-connected structure, where multiple movable sub-arrays can independently change their positions within different local regions. To maximize the system sum rate, we jointly optimize the digital beamformer, analog beamformer, and positions of sub-arrays, under the constraints of unit modulus, finite movable regions, and power budget. Due to the non-concave/non-convex objective function/constraints, as well as the highly coupled variables, the formulated problem is challenging to solve. By employing fractional programming, we develop an alternating optimization framework to solve the problem via a combination of Lagrange multipliers, penalty method, and gradient descent. Numerical results reveal that the proposed MA-aided hybrid beamforming scheme significantly improves the sum rate compared to its fixed-position antenna (FPA) counterpart. Moreover, with sufficiently large movable regions, the proposed scheme with sub-connected MA arrays even outperforms the fully-connected FPA array. © 1967-2012 IEEE.

关键词： Budget control

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Optimizing Wireless Power Transfers for Telecommunication Networks: Performance Evaluation and Strategies

Optimizing Wireless Power Transfers for Telecommunication Ne...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhatt, Upendra Mohan Sharma, Somya Francis, G. Arun Kaushik, Neeraj Hemelatha, S. Sharma, Y.K. Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India School of Commerce and Management Iimt University Meerut India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Tmu Department of Ece India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

This study examines the typical performance of a wirelessly fueled transceiver in which an electricity source interacts with a target while being supplied by a specialized power transmitter (PB). As a transmission device, it is believed that the PB is competent of frequency prediction, cloud based spatial multiplexing, and spectral detection. When using a night before going to bed technique, the provider first collects information from either a PB with numerous receivers before sending data to the target. Theoretical formulas for the annualized pace for two distinct transmit antennas, particularly regarding postpone accepting and postponement incompatible, are obtained under the assumption of Nakagami-m path loss. Additionally, in some specific situations, such as the high send wattage domain and a huge assortment of antennas, closed-form methods for the ideal time spread that optimize the typical transmission are found. The effect of co-channel crosstalk is then researched. The total output can be increased at the PB by adding more broadcast transmitters, and since interruption can be used as an additional energy source, it may even be possible to use interruption to your advantage. This has been demonstrated by analytical and modeling findings. Additionally, the cruising pace is not significantly impacted by the energy conversion link's waning intensity level, particularly if there are a lot of PB receivers. Lastly, it is noted that the average flow is significantly influenced by the source. © 2023 IEEE.

关键词： Cellular data transmission Co-channel crosstalk Energy conversion link Frequency prediction Multiple access systems Optimization Performance evaluation Power beacons Power transmitter System model System performance Telecommunication networks Transmit antennas Wireless power transfer

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VocalEyes: Enhancing Environmental Perception for the Visually Impaired through Vision-Language Models and Distance-Aware Object Detection

arXiv

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arXiv 2025年

作者： Chavan, Kunal Balaji, Keertan Barigidad, Spoorti Chiluveru, Samba Raju School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Computer Science and Engineering S.R.M Institute of Technology Chennai India Electrical Electronics and Communication Engineering IIT Dharwad Dharwad India

With an increasing demand for assistive technologies that promote the independence and mobility of visually impaired people, this study suggests an innovative real-time system that gives audio descriptions of a user’s surroundings to improve situational awareness. The system acquires live video input and processes it with a quantized and fine-tuned Florence-2 big model, adjusted to 4-bit accuracy for efficient operation on low-power edge devices such as the NVIDIA Jetson Orin Nano. By transforming the video signal into frames with a 5-frame latency, the model provides rapid and contextually pertinent descriptions of objects, pedestrians, and barriers, together with their estimated distances. The system employs Parler TTS Mini, a lightweight and adaptable Text-to-Speech (TTS) solution, for efficient audio feedback. It accommodates 34 distinct speaker types and enables customization of speech tone, pace, and style to suit user requirements. This study examines the quantization and fine-tuning techniques utilized to modify the Florence-2 model for this application, illustrating how the integration of a compact model architecture with a versatile TTS component improves real-time performance and user experience. The proposed system is assessed based on its accuracy, efficiency, and usefulness, providing a viable option to aid vision-impaired users in navigating their surroundings securely and successfully. © 2025, CC BY.

关键词： Speech enhancement

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Physically Based Bidirectional Path Tacing Algorithm on the Tianhe-2 Supercomputer 25

Physically Based Bidirectional Path Tacing Algorithm on the ...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Yijia Zhang, Congpin Xu, Zhengwei Li, Ligang Chinese Academy of Sciences National Space Science Center Key Laboratory of Electronics and Information Technology for Space System Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer and Information Engineering Henan Normal University Henan Xinxiang China

ISBN: (纸本)9798350330014

Physically based ray tracing is a technique that involves creating a photo-realistic image from a 3D scene description by utilizing the laws of physics to simulate the behavior of light and matter. However, due to the random length of light samples and irregular memory access patterns, it can suffer from low scalability and severe load imbalance. As a result, the development of a framework for multi-node parallelism algorithm in physically based ray tracing is a significant challenge. To tackle these issues, this paper introduces a novel algorithm named distributed parallel physically based bidirectional path tacing algorithm (DP-BDPT). Through analyzing the physical essence of bidirectional path tracing algorithm and the light transport equation, we have devised a framework that carefully divides tasks between inter-compute nodes and intra-compute nodes. To support high scalability, we have developed a physically based architecture. Additionally, we have proposed a hybrid scheduling method to achieve load balancing. To further enhance performance, we have analyzed several key factors that affect rendering performance and implemented adaptive optimization techniques. The experiments conducted on the Tianhe-2 Supercomputer involving typical and challenging 3D scenes, namely the Cornell-box, Veach-bidir, and Teapot-full experiments, have illustrated that DP-BDPT demonstrate near-linear speedups as the number of CPU cores scales from 1200 to 9600, thereby demonstrating their efficiency when operating on a ten-thousand-core system. © 2023 IEEE.

关键词： Ray tracing

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