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Applying Keyword Extraction Techniques on Customer’s Feedback of a Software Product to Accelerate Agile Based Development

Applying Keyword Extraction Techniques on Customer’s Feedba...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Nidhi Bhatia Jitendra Singh Vandana Department of Computer Science and Engineering SRM Institute of Science & Technology Delhi NCR Campus Ghaziabad UttarPradesh India Department of Computer Application SCRIET Chaudhary Charan Singh University Meerut UttarPradesh India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Keywords or key-phrase extraction is an important task in almost all the scenarios where natural language processing is required. Present paper analyses various keyword extraction techniques for improving a softwareproduct, taking into consideration an agile based development approach. Feedback, reviews given by customers are always written in an unrestricted natural language. If we are able to extract keywords from this feedback, at predevelopment stage, quality of a software can be improved, and it would be saving efforts and time of developers. Keywords provide a quick review about sentiments, prioritize customer needs, enhance user experience, provide marketing insights and also analyse the trends. Change-request submitted in last stages of development process require a lot of reworks. Hence this work can be beneficial in agile based development process to develop an end product which incorporates suggestions given by users.

关键词： Reviews Transformers Market research Software User experience Natural language processing Informatics

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Three-Dimensional Spatial-Temporal Near-Field Passive Localization Based on an Exact Spatial Propagation Model

Three-Dimensional Spatial-Temporal Near-Field Passive Locali...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiaxiong Fang Juan Liu Hua Chen Wei Liu Ye Tian Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

Based on the exact source-sensor spatial geometry, a three-dimensional (3-D) spatial-temporal localization algorithm for multiple near-field (NF) sources is proposed without adopting the Fresnel approximation, which simplifies the spatial phase difference by Taylors polynomial. In addition, considering the propagation attenuation which varies from different sensors, the spatial and temporal information can be exploited to construct a third-order parallel factor (PARAFAC) data model and the array manifold matrices can be extracted by trilinear decomposition; then, estimation of the unambiguous range and angle parameters of the NF sources is achieved from the spatial amplitude-phase factors by the least squares method. The obtained 3-D parameters associated with each source require no additional pairing process, as also demonstrated by simulation results.

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A Descriptive Research on Internet of Things Implementation in Healthcare Sector 5

A Descriptive Research on Internet of Things Implementation ...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Veeraiah, Vivek Ravi Kumar, G.K. Singh, Devesh Pratap Chaudhary, Akshita Elkady, Ghada Ross, Dhyana Sharon Adichunchanagiri University Department of Computer Science Karnataka Mandya India Adichunchanagiri University Department of Computer Science and Engineering Karnataka Mandya India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India RCCV Girls College Department of Computer Science and Application Uttar Pradesh Ghaziabad India College of International Transport & Logistics Arab Academy for Science Technology & Maritime Transport Cairo Egypt Tamil Nadu Chennai India

ISBN: (纸本)9798350398267

The Web of Things (IoT) is critical for the virtual change in medication because it provides innovative business models to facilitate and promote improvements in practice operations, control costs, enhance productivity, and improve patients' experiences. The use of innovative IoT techniques in personalized health, however, remains incredibly challenging due to several issues, such as incoherent IoT program structures, diversity among connected wearable devices, a lack of accurate and affordable smart medical sensors, the complexity of the generated data, and an increased need for interoperability. This research delves at the history of IoT in healthcare, with a focus on how these technologies are being used to the rapidly expanding area of personalized medicine. It covers both common health scenarios and cutting-edge, intricate methods generated from the Internet of Things. It highlights the scientific, ethical, and economic challenges of developing a better medical system for early illness detection. At long last, the Web of Things (IoT) is permitting present-day medication to answer the requests rapidly of the time by creating a platform for communication across different health sectors, providing digital help at every stage, and so on. © 2022 IEEE.

关键词： Internet of things

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Next Generation HR: Transforming Talent Management with AI and ML Innovations

Next Generation HR: Transforming Talent Management with AI a...

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Disruptive Technologies (ICDT), International Conference on

作者： Niharika Singh Pooja Singh Sudeshna Chakraborty Dept. of Master of Computer Application GL BAJAJ Institute of Technology & Management Greater Noida U.P. Department of CSE School of Computing Science & Engineering Galgotias University Greater Noida (U.P.) Department of Computer Application School of Computing Science and Engineering Galgotias University Greater Noida

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-depth analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success.

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Study of Natural Language Processing for Sentiment Analysis 3

Study of Natural Language Processing for Sentiment Analysis

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Kavitha, C.R. Rajarajan, S.J. Jothilakshmi, R. Alaskar, Kamal Ishrat, Mohammad Devi, V. Chithra Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Bengaluru560035 India Department of Mechanical Engineering Tamil Nadu Thiruverkadu600077 India R.M.D. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai601206 India Institute of Management Department of Computer Application Maharashtra Kolhapur416003 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522502 India Department of English Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350322842

The study of opinions, sentiments, assessments, perspectives, and feelings as they are expressed in written language is the focus of sentiment analysis and evaluation mining. It is commonly the subject of text mining, information mining, and web mining because it is one of the most active study fields in natural language processing. Sentiment analysis has been applied in many different contexts, including the study of how events affect unofficial groups and the exploration of presumptions about goods and services. The importance of sentiment analysis is increasing at the same time as virtual entertainment such as surveys, group chats, web blogs, micro sites, Twitter, and interpersonal organizations. In this work, we provide the results of our preliminary studies on twitter sentiment analysis. This study seeks to distinguish subjects from sentiment in tweets. It recognizes the sentiment that refers to the particular problem using Natural Language Processing techniques. Our analysis consists of three main processes, subjective grouping, semantic affiliation, and extreme order, to characterize sentiment. By describing the syntactic relationship between sentiment dictionaries and subject, the study makes use of sentiment vocabularies. Exploratory results demonstrate that the proposed framework performs better than existing message sentiment analysis tools because tweets don't have the same architecture as traditional messages. © 2023 IEEE.

关键词： Sentiment analysis

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Enhancing Dynamic Abstract Generation for Code Summarization using CODE-OAN Models and DBWTFF Frames

Enhancing Dynamic Abstract Generation for Code Summarization...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Chandradeep Bhatt Manish Sharma Sanjay Sharma Shefali Gupta Teekam Singh Mukesh Kumar Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Computer Application Department SGRR Univeristy Dehradun India Chitkara University Institute of Engineering and Tech Chitkara University Punjab India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India

This research presents a robustness code abstraction generation method based on an attention mechanism. The method involves extracting high-quality code and its corresponding description language material from the programming community, specifically focusing on the text of query specifications and code responses. Redundant code and its description language material are filtered out, and the query specifications are transformed into declarative sentences for code conversion. Finally, a series model based on the attention mechanism is utilized to generate code summaries. The method effectively eliminates redundancy and noise in the code, leading to improved accuracy rates in automatic judgment, artificial evaluation, and testing. The evaluation results demonstrate superior performance compared to existing baseline methods.

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Neural Network Strategies for Optimizing Near Field Sensor Array Processing

Neural Network Strategies for Optimizing Near Field Sensor A...

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Surendra Yadav Deepak Kumar Sinha Sweta Kumari Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science Engineering JAIN (Deemed-to-be University) Banglore India Department of ISME ATLAS SkillTech University Mumbai India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

In this research, we look at how well using neural network techniques may improve the processing of near field sensor arrays. As the introduction explains, near field sensor array technology is becoming more important across many fields, therefore there is a pressing need for cutting-edge techniques that improve spatial resolution, signal-to-noise ratio, and computational efficiency. The study presents a unique neural network technique for processing near field sensor arrays, capitalizing on the flexibility and learning characteristics of the network to boost performance. The suggested neural network technique is shown to be better in all measures by the findings. It outperforms more conventional approaches, which only managed a score of 0.78, because of its 0.92 spatial resolution. Furthermore, the suggested technique achieves a signal-to-noise ratio of 18.5, which is much higher than the greatest signal-to-noise ratio produced by conventional methods (15.2). The suggested method is more accurate, with significantly reduced mean absolute error (0.027) and mean squared error (0.001) values. In addition, the suggested method's processing speed is competitive, coming in at 3.8 ms whereas more conventional approaches take between 4.2 and 5.0ms. With an efficiency of 95% in computation, it outperforms the efficiency of conventional approaches, which ranges from 82% to 88%. Medical imaging, environmental monitoring, and wireless communication are just a few of the many potential applications highlighted by the results, validating the efficacy of the suggested technology. The findings call for further investigation and cooperation at the crossroads of sensor technology and deep learning, which should lead to novel processing techniques for near field sensor arrays that may be used in a wide variety of contexts.

关键词： Wireless communication Accuracy Array signal processing Neural networks Pressing Real-time systems Computational efficiency Spatial resolution Sensor arrays Signal to noise ratio

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An Overview of the Use of Deep Learning Algorithms to Predict Bankruptcy 7th

An Overview of the Use of Deep Learning Algorithms to Predic...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Choudhury, Tanupriya Kotecha, Ketan School of Computing Graphic Era Hill University Dehradun India Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Pune India Pune411045 India CSE Department Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Computer Engineering & Application’s GLA University Mathura India

ISBN: (纸本)9789819995615

The financial forecasting of different firms in the area of financial status aims to determine whether the company will go bankrupt in the near future or not. This is a critical problem for these companies. Several companies have shown a strong interest in this area, particularly since they are concerned about the future of their companies from a financial perspective and want to determine whether or not they will go out of business. Therefore, in the work that we have done, we have presented three well-known technologies of deep learning in conjunction with ensemble classifiers and boosting ensemble classifiers for the purpose of failure prediction. During our investigation, we used an uneven dataset consisting of businesses from Spain, Poland, and Taiwan. In addition to this, we applied approaches such as oversampling, hybrid balancing, and clustering-based balancing to get rid of the inconsistent data. When taking into account a real-life financial dataset with an appropriate amount of complexity, it was discovered that the MLP-6L model with the SOMTE-ENN balancing approach had the most remarkable performance when measured against the metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Learning algorithms

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Knowledge Atlas on the Medical Artificial Intelligence Research-A Bibliometric Analysis Based on CiteSpace and VOSviewer 2

Knowledge Atlas on the Medical Artificial Intelligence Resea...

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2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2022

作者： Shi, Li Han, Lingling Huang, Ke Ge, Fangzhen Zhang, Lele School of Computer Science and Technology Huaibei Normal University Huaibei235000 China School of Economics and Management Huaibei Normal University Huaibei235000 China Engineering Research Center of Cognitive Behavioral Intelligent Computing and Application Huaibei235000 China

ISBN: (纸本)9781510663107

In order to draw and analyze the current status of the research on the application of artificial intelligence in the field of health in China and abroad, we explored the hotspots and frontiers of this research field and the evolution path from 2012 to 2022. This research uses 3743 articles included in the core database of Web of science and 728 related research articles contained in CNKI as the basis for data analysis, and uses the advantages of bibliometrics visualization software of CiteSpace and VOSviewer to study institutions, countries, authors and literature keywords. In this research area, the research of medical artificial intelligence is growing day by day. By analyzing and sorting out the current research status, research hotspots and evolution path in this field of research, and trying to point out the deficiencies in the research in this field in China, the paper hopes to provide more references for researchers and practitioners of medical artificial intelligence related research. © 2023 SPIE.

关键词： Data visualization

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An Improved Designing of Neuroprosthetics Arm Using LDA

An Improved Designing of Neuroprosthetics Arm Using LDA

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Anuj Singh Kamlesh Chandra Purohit M. Anand Kumar Sachin Sharma Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun Department of Computer Application Graphic Era (Deemed to be University) Dehradun

Neuroprosthetics have experienced a remarkable evolution in recent times, particularly with the advent of intelligent neuroprosthetics that leverages artificial intelligence (AI) technology. These devices have the potential to improve both the input and output of neurological systems, allowing individuals to control prosthetic limbs with their thoughts and perform everyday tasks such as picking and placing objects. the key to this innovative technology lies in the ability to collect brain signals through electroencephalography (EEG) devices, which can be then deciphered and utilized by the mechatronic components of the prosthetic limb. However, effectively separating relevant signals from irrelevant ones has been a significant challenge for the design and development of neuroprosthetics, as these signals fall into three distinct categories of brain communication. In this research paper, we explore the utilization of split signals in neuroprosthetics using LDA and how this varies from person to person depending on their mental state. Incorporating LDA methods into these devices seems to be the most viable solution for society to build a system that can efficiently differentiate and utilize brain signals, bringing us one step closer to unlocking the full potential of neuroprosthetics. It is found that with the adaptation of the LDA approach, 91% of the results are properly categorized and are best among the existing literature available to date.

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