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AI-Driven Software Reuse: Optimization and Comparative Performance Analysis

AI-Driven Software Reuse: Optimization and Comparative Perfo...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Atul Thakare Sharon Christa Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India School of Computer Science and Engineering JAIN Deemed to be University Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Software reuse improves productivity and ensures that there is less time delivered to the market by enabling the proper usage of reusable code from the software repositories. The study provides the collaboration between the AI techniques, which includes Convolution Neural Networks (CNN), Random Forest (RF), and Support Vector Machines (SVM), to predict software components' reusability. The comparative analysis shows the effectiveness of different AI- driven algorithms, where CNN has shown the highest accuracy of 92%, thus reducing the human effort in finding the reusable modules. The Case studies in e-commerce and health care show the benefit of using AI-driven reuse of software; this means that the development time is reduced by 25%, and the maintenance cost is decreased by 20%. The paper highlights how different AI algorithm integration with software engineering has some limitations. The findings show that AI has greatly revolutionized software reuse. Thus bringing innovation and efficiency in software engineering.

关键词： Support vector machines Computational modeling Medical services Software Data models Convolutional neural networks Artificial intelligence Software reusability Random forests Software engineering

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A Comprehensive Review of Leap Motion Controller-Based Hand Gesture Datasets

A Comprehensive Review of Leap Motion Controller-Based Hand ...

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Next Generation Electronics (NEleX), International Conference on

作者： Bharatesh Chakravarthi Prabhu Prasad B M Raju Imandi Pavan Kumar B N School of Computing and Augmented Intelligence Arizona State University Tempe Arizona USA Dept. of Computer Science and Engineering Indian Institute of Information Technology Dharwad India Dept. of Computer Science and Engineering Indian Institute of Information Technology Sri City Chittoor India

This paper comprehensively reviews hand gesture datasets based on Ultraleap's leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology.

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Auto Text Correction Using NLP Techniques

Auto Text Correction Using NLP Techniques

引用

Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Nalla Srivarsha Gummadi Nithin Shanmugasundaram Hariharan Bibhuti Bhusan Dash Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Auto text correction is a fundamental tool in modern Natural Language Processing (NLP) that upgrades user involvement by decreasing typographical and linguistic errors in composed content. This extends leverages progressed NLP procedures to create a strong and proficient auto-correction framework that precisely predicts and corrects content input errors in real-time. The framework utilizes a combination of deep learning models, such as sequence-to-sequence models and transformers, in conjunction with conventional strategies like n-grams and edit distance algorithms. By analyzing the context and structure of input sentences, the model is able to offer relevant rectifications, making it versatile to different dialects and user composing styles. Moreover, we investigate diverse assessment measurements and compare different calculations to optimize the precision and productivity of the correction system. The arrangement has potential applications in word processors, mobile keyboards, and other content input platforms where high accuracy and low latency are crucial.

关键词： Productivity Program processors Keyboards Linguistics Transformers Prediction algorithms Natural language processing Real-time systems Low latency communication Mobile computing

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Automatic Digit and Alphabet Recognition Based Online Toll Collection System

Automatic Digit and Alphabet Recognition Based Online Toll C...

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2021 International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2021

作者： Hossen, Md. Sahadat Akter, Mahmuda Saifuddin, Khaled Mohammed Premier University Dept. of Computer Science and Engineering Chittagong4000 Bangladesh Khulna University of Engineering and Technology Dept. of Electronics and Communication Engineering Khulna9203 Bangladesh

ISBN: (纸本)9781665414609

Radio frequency identification (RFID) based automatic highway toll collection system has become very popular among developed countries. However, RFID is not feasible for developing countries to embed with each vehicle since it is very costly. Moreover, as metal objects can influence Radio Frequency (RF) field, intently the nearness of any metal object while reading RFID information may result in unwanted noise. These problems are still significant concerns. We propose a vehicle number plate recognition-based automated toll collection system to address these issues, which is more efficient, reliable, and cost-effective for widely use. In this research, the Normalized Cross-Correlation (NCC) based template matching algorithm is exploited for character recognition from each vehicle's number plate. After recognizing characters from a number plate successfully, a fixed amount of toll fee is charged automatically according to the vehicle's category. © 2021 IEEE.

关键词： Template matching

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Design of Smart Dashboard based on IoT Fog Computing for Smart Cities 5

Design of Smart Dashboard based on IoT Fog Computing for Sma...

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5th International Conference on Trends in Electronics and informatics, ICOEI 2021

作者： Patel, Minal Mehta, Akash Chauhan, N C Charusat Computer Engineering Department Anand India Information Technology Dept. Ahemadabad India A D Patel Institute of Technology Information Technology Dept. Anand India

ISBN: (纸本)9781665415712

In smart city concept, the various aspects of citizens' life can be facilitated with the design of smart transport, smart parks, smart homes, smart healthcare, etc. The various components of smart city architecture are designed as a large network and the data management can be provided with all types of privacy to citizens. In this paper, dashboard is designed which combines open-source tools and web technologies including servers, sensors and various applications. These data collections are considered as a mechanism which can display summary of a city to visualize in a single view, and it can assist citizen to track and analyze the current state in city. In this paper, we have presented smart city framework using IoT based architecture. The existing dashboard solutions are also provided to analyze the various aspects of dashboard in detail. The dashboard discussed in this paper is able to provide following information: i) basic environment information services ii) advanced services such as healthcare, public transport parking system services. The working of smart dashboard can be implemented by introducing the architecture system design of smart dashboard. The proposed smart dashboard is designed with its detailed functionalities in this paper. © 2021 IEEE.

关键词： Fog computing

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Image inpainting based on directional Gaussian graph model using multi-head reference 13

Image inpainting based on directional Gaussian graph model u...

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13th International Conference on Signal Processing Systems, ICSPS 2021

作者： Zhang, Zhonghao Ma, Lihong Dept. of Electronic & Computer Engineering The Hong Kong Univ. of Science & Technology Clear Bay Hong Kong School of Electronic & Information Engineering South China Univ. of Technology 386 Wushan Rd. Guangzhou China

ISBN: (数字)9781510653184

ISBN: (纸本)9781510653177

This paper aims to repair missing regions which are corrupted along arbitrary directions. It presents a mixed image inpainting method based on Markov random field. By using Belief Propagation scheme in low gray-levels and alternately suggesting a directional Gaussian Graphical model (DGGM) for multiple references in a high-level range, it gains a balance between the model accuracy and the computation complexity in realization. On the basis of an existing method in [1], it improves the method in high level inpainting for the task under small train sets and large corrupted regions, by introducing these multi-head reference clues. Experimental results are given, the inpainting quality of different kinds of images with different sizes and contents under different parameter settings are compared in metrics of the peak signal noise ratio and the structural similarity index. The significance of parameter settings and the efficient computational cost could demonstrate the feasibility of this method. © COPYRIGHT SPIE.

关键词： Belief propagation

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Smart Roads: U-Turn Accident Prevention System

Smart Roads: U-Turn Accident Prevention System

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Neena Johnson J Ancy Jenifer Aanya Khan Akhila Jyothi Dept. of Computer Science and Engineering Karunya Institute Of Technology and Sciences Coimbatore India Karunya Institute Of Technology and Sciences Coimbatore India

When discussing statistics of everyday road accidents in India, a very large contributing factor is accidents in hilly areas due to hairpin bends. These blind turns are highly dangerous and may more often than not result in fatal accidents. The low visibility results in the driver being extremely vulnerable to head-on collisions. Coming up with a preventive measure to this perilous situation is the prime focus of this paper. The proposed system in this paper is to, warn drivers of other vehicles at hairpin curves to avoid accidents involving human lives. This system is designed to allow residents and tourists to drive safely by warning them of blind corners and u-turns and reducing the possibility of accidents. This paper intends to reduce the chances of head-on collisions by implementing an accident prevention system. A prevention system, although new, should be comprehensible for a new user, to solve this issue, an existing system has been integrated with the proposed idea.

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Soil Nutrient Analysis in Agricultural Crops

Soil Nutrient Analysis in Agricultural Crops

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Aditya Pai H Mahesh TR Saravana Kumar R Chandramma R Bhagavant K. Deshpande Omswaroop T M School of Computer Science and Engineering Jain (Deemed-to be University) Bengaluru India Dept. of CSE SEA College of Engineering and Technology Bengaluru

Agriculture is an important area of research and is also considered to be a very critical area. As per the data collected from the farmer and citizens, it is found that nowadays the citizens are playing an important role in assisting farmers to provide solutions to the current system by doing research and innovation in the farming sector. The government too is providing benefits by funding the agricultural research projects which the citizens are working on. This has directly created an advantage for the farmers. Now in this research area, a lot of work has been carried out using the new hardware and software technology. This has made the farmers very easy to yield better crops. Adding to this there are other research that are being carried out on finding the quality of the soil and predicting the disease of the plant. The majority of the work carried out in the agricultural industry is by the IT industry. In the review paper, various soil nutrients are taken into account such as pH value, nitrogen content, phosphorous content, organic carbon, potassium content, and electric conductivity (salinity in the soil). The paper also focuses more on the different ML algorithms that is discussed in the last decade. On the technological front, we study more on how the farmers can be assisted in improving the crop by doing predictions using the ML technique. A novel approach has been found for performing farming in a smarter way. Also, the government provides grants to researchers to carry out innovative projects in the area of agriculture.

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Master GAN: Multiple Attention is all you Need: A Multiple Attention Guided Super Resolution Network for Dems

Master GAN: Multiple Attention is all you Need: A Multiple A...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Azhan Mohammed Mohammad Kashif Md Haider Zama Mohammed Abbas Ansari Saquib Ali GalaxEye Space Bengaluru India Dept. of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi India

The task of transforming low-resolution remote sensing images to high-resolution has consistently presented a formidable challenge in the field. The use of Generative Adversarial Networks (GANs) has led to tremendous development in the field. In this study, a novel super resolution architecture Multiple Attention Swin Transformer Enhanced Residual GAN (MASTER GAN) has been introduced, that uses multiple attention techniques in a neural network trained in an adversarial training environment. The introduced MASTER GAN acheives state-of-the-art results in super resolution tasks, when compared to existing mechanism. The paper also introduces an open source database of low resolution and counter high resolution imagery, generated using Kernel GAN. The training code has been provided at: https://***/sheikhazhanmohammed/***

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Automated Healthcare Medical Imaging through EOA Optimized Hyperparameter in CNN

Automated Healthcare Medical Imaging through EOA Optimized H...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Agrawal, Umang Kumar Panda, Nibedan Das, Debashreet Dalai, Asish Kumar Ramana, B.V. Mishra, Ayush School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Dept. of Computer Science and Engineering Centre for UG and PG Studies BPUT Odisha Rourkela India School of Computer Science and Engineering VIT-AP University AP Amaravati India Department of IT Aditya Institute of Technology and Management AP Tekkali532201 India

Medical imaging (MI) is the prefatory field of healthcare that plays a vital role in the patient's clinical and medical intervention. Recent developments in MI help healthcare professionals such as doctors and radiologists diagnose patients with improved quality of MI methods such as X-ray, CT and MRI scans. The upsurge of the Convolutional Neural Network (CNN) offers the latent for timely detection and classification of the disease with deeper insights. It adapts the spatial features and learns the input data hierarchically, which makes well-suited for this task. To improve the robustness of the CNN, the parameters are optimized through a nature-inspired dynamic mass balance-based metaheuristic approach named the Equilibrium Optimizer Algorithm (EOA). As a real-life implication, the suggested EOA-CNN approach has been verified by utilizing two benchmark datasets. The competence of EOA-CNN is assessed by present-day methods in terms of parameters namely accuracy, error rate, min, avg, std, accuracy, sensitivity and specificity. By streamlining the workflow, the said approach is a valuable asset conforming to the reliability to resolve healthcare imaging complications with advanced diagnostics support. This framework is being used to optimize the constraints meanwhile bridging the gaps in healthcare convenience. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Functional neuroimaging

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