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Developing Lexicons for Enhanced Sentiment Analysis in Software engineering:An Innovative Multilingual Approach for Social Media Reviews

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computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib Ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood Alam Fuad AAwwad Emad A.A.Ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

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Watermarking technique for document images using discrete curvelet transform and discrete cosine transform

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Multimedia Tools and Applications 2024年第40期83卷 87647-87671页

作者： Singh, Balkar Sharma, M.K. Post Graduate Department of Computer Science Gujranwala Guru Nanak Khalsa College Punjab Ludhiana India School of Mathematics Thapar Institute of Engineering and Technology Punjab Patiala India

In this paper, a watermarking technique based on discrete curvelet transform and discrete cosine trans- form is proposed to protect the color document images. The six layers of the document image are created using the Discrete Curvelet Transform (DCuT). The sixth layer is chosen for the embedding process, while the remaining layers are discarded. Discrete Cosine Transform (DCT) is applied on the 8 × 8 blocks of the real part of sixth layer. DCT coefficients from low to medium are selected for the embedding process, excluding the first one. To choose the Direct Current (DC) coefficients of DCT to embed the watermark bits, a zigzag function is used. The same process is applied on the receiver side watermark image is extracted. Since this is a non-blind method, we require both a cover image and a watermarked image. DCT of watermarked image and the cover image are compared to extract the watermark image. The resistivity of the watermarked image against image processing attacks is measured using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), Normalization Correlation (NC), Bit Error Rate (BER) and Universal Quality Index (UQI). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mean square error

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computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

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Classification of cervical spine disease using convolutional neural network

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Multimedia Tools and Applications 2024年第41期83卷 88963-88979页

作者： Bezabh, Yohannes Agegnehu Salau, Ayodeji Olalekan Abuhayi, Biniyam Mulugeta Ayalew, Aleka Melese Department of Information Technology University of Gondar Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Cervical spondylosis myelopathy (CSM) is a degenerative disorder of the cervical spine’s disks and joints, leading to neurological disability. Typically, this condition causes increasing discomfort and neurologic deterioration. Cervical spine pain disease classification using deep learning can assist medical professionals in making early and more precise medical diagnosis of cervical abnormalities. The main focus of the assessment for doctors is the classification of cervical spine diseases from X-rays images, which is valuable for patients to indicate the various symptoms they are faced with. Identifying cervical spine pain injuries (CPI) is a critical aspect of the diseases segmentation and classification. Manually explaining the high-dimensional feature space makes it challenging to identify the exact category and level of severity. This paper introduces a Convolutional Neural Networks (CNN) concatenation feature extraction method to help in the diagnosis of cervical spine pain disease from X-ray images. An innovative CNN-concatenation model-based fully connected classifier and a four-class system are applied to classify X-ray image data in this research. The CNN approach is employed to automatically classify X-ray images into healthy (normal) and with cervical spine disease (abnormal). CSM is currently diagnosed mostly through clinical evaluation using imaging methods such as X-ray and computed tomography (CT). Due to its inexpensive cost and minimal radiation dose, X-ray is commonly used. The proposed approach achieved a training accuracy of 99.98%, a validation accuracy of 98.29%, and a testing accuracy of 97.82% of classification from experiments. These results indicate that the image preprocessing, data augmentation, and CNN approaches provide an efficient classification method for identifying and accurately classifying cervical spine diseases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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E-waste management in the digital era: A sustainable computing approach

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IEEE Potentials 2024年第2期43卷 30-36页

作者： Ramasamy, Rajkumar Gopi, Rahul Sanmugam Palanisamy, Rohini Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai600062 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India

In the digital era, the proliferation of electronic devices has led to a surge in electronic waste (e-waste), posing significant environmental and health challenges. This article proposes a sustainable computing approach to e-waste management. The article explores the complexities of e-waste management and recycling frameworks within the context of sustainable computing and applications. It delves into the challenges posed by the rapid advancement of technology and the resultant surge in electronic waste. Strategies to mitigate e-waste are discussed, including approaches in product design. The article emphasizes collaborative efforts to address these challenges effectively. By adopting sustainable computing practices and leveraging innovative solutions, the aim is to create a more sustainable approach to managing electronic waste and its environmental impacts. © 1988-2012 IEEE.

关键词： Electronic waste Recycling Green computing Metals Maintenance engineering Soil Regulation Water pollution Energy efficiency Resource management

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Wireless Sensor Networks: Revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. Ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats. © 2024 IEEE.

关键词： Terrorism

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Senthilvelan, Prasanth Li, Jialong Tei, Kenji Waseda University Department of Computer Science and Communication Engineering Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9798350337952

In this paper, we present an efficient design of a shield called Parameterized Shield which allows for easy modification of its requirement parameters in response to the changes in the environment instead of manually making many shields during the development time and changing the whole shield during runtime. We propose to generate one proper shield that has many requirement parameters where even when there is any complex situation, there is not any need for extra effort from engineers to develop additional requirements and prepare the shield but only the parameters need to be changed when there is a change in the environment which avoids choosing a whole new shield every time an environment change occurs during runtime. And similarity-based adaptation will be used to choose the right parameters for the shield according to the environment with the help of the MAPE-K loop framework and reinforcement learning and to give the appropriate policy for the agent to act according to that environment. We will demonstrate using a cleaning robot as an example. © 2023 IEEE.

关键词： Reinforcement learning

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The Development of Inference in Healthcare System using FEFL System 4

The Development of Inference in Healthcare System using FEFL...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Vikram Saini, Dimple Rastogi, Shailesh Raman, Ramakrishnan Verma, Apurv Meenakshi, R. Md Srv Media Pvt. Ltd India India Symbiosis Institute of Business Management Nagpur Department of Cse Pune India Department of Cse India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

In the modern world, access to health care remains an important issue, especially in impoverished regions where farmers lack access to the highest quality medical centers and expert practitioners. computer technology has proven groundbreaking in the healthcare sector, corresponding its good effect in different industry sectors. Wearable technologies are important in sophisticated medical applications, leveraging the tracking and analysis of bodily data from healthcare device sensors. The inclusion of Fog computing covers delays that come with cloud computing, critical for enabling quick analysis of bodily data. However, the fog setting presents difficulties such as delay in emergency health state and system overloading. Addressing these problems, this study suggests a novel Fog-enabled Flexible Clinical Decision-making System (FICDSS) health design for improved treatment and diagnostic efficiency. The system features four layers: a sensor layer, edge layers fog layer, while cloud layer. Patient data from worn or non-wearable sensors is transferred to the peripheral layer, the network layer responsible for gathering, processing, and sending information into the fog layer below. A Fuzzy Law Inference The system (FLIS) beginning at the boundary layer uses timing modifications to data to predict a user's immediate form health state. The FLIS system uses fuzzy language factors, produced through fuzzification, to express the user's health state on the patient's nurse or doctor. The fog layer is utilizing fitness parameter traits, determines a person's health declare, with both the dense fog and cloud elements reacting quickly based on that user's medical history. A thorough test in MATLAB confirms the suggested fuzzy logic prediction system's superiority in terms of delay, processing time, as well as detection accuracy. © 2024 IEEE.

关键词： Fog computing

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ABLE: Personalized Disability Support with Politeness and Empathy Integration

ABLE: Personalized Disability Support with Politeness and Em...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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