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Twitter sentiment analysis using conditional generative adversarial network

International Journal of Cognitive Computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Improving security performance of Internet of Medical Things using hybrid metaheuristic model

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Multimedia Tools and Applications 2025年第9期84卷 6403-6428页

作者： Kanneboina, Ashok Sundaram, Gopikrishnan Assistant Professor School of Engineering Malla Reddy University Telangana Hyderabad India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

The security of the Internet of Medical Things (IoMT) is very important because it connects many medical devices to improve care operations and patient monitoring in real time. Researchers have developed high-security frameworks with attribute-based access control, hashing, encryption, and privacy preservation. Current security models are either too complex or too unreconfigurable for real-time monitoring applications. Current models’ security performance depends on their internal configuration, which is not adaptable to new attacks. This research proposes an improved hybrid metaheuristic model to improve healthcare IoT security to overcome these restrictions. The model initially uses blockchain but can change its internal hashing and encryption standards. The proposed model continuously optimizes the blockchain-based IoMT deployment’s security and quality of service by combining Elephant Herding Optimisation (EHO) and Grey Wolf Optimisation. The proposed model uses dual fitness functions to improve security and service quality against various attacks. Multiple state-of-the-art models and DDoS, man-in-the-middle, masquerading, and Sybil attacks performed to evaluate the model. According to the comparison, the proposed model improved network consistency by 8.7%, throughput by 6.4%, packet delivery ratio by 8.2%, and attack detection and mitigation by 9.4%. Real-time healthcare use cases could use the suggested model after this performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

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Multimedia Tools and Applications 2024年 1-29页

作者： Das, Bappaditya Raghuvanshi, C.S. Research Scholar Department of Computer Science and Engineering FET Rama University Kanpur India Professor Department of Computer Science and Engineering FET Rama University Kanpur India

Plant disease causes a highly significant impact on production due to its destructive characteristics. A variety of chemical techniques should be applied to agriculture at different stages of disease infestation to enable precise plant protection. The integration of unmanned aerial vehicles (UAVs) for obtaining high-resolution images has become necessary due to the growing demand for precision agriculture, making crop health monitoring more efficient. In this study, we developed an innovative approach that combines the Deep Radial Basis Function Network (DRBFN) with the Multidimensional Mixed Attention (MMA) method. Since DRBFNs use radial basis functions as effective feature extractors, the model can detect even the smallest changes in the composition, color, and texture of leaves. This capability is particularly helpful when dealing with a diversity of plant species and pathogens exhibiting different symptoms. By deliberately considering pertinent spatial and spectral attributes, MMA is applied to enhance the discriminatory abilities of the network. By integrating spatial and spectral attention, the network can efficiently acquire information from both local and global sources, thereby enhancing the overall resilience and precision of the detection system. Our proposed model leverages UAV-captured images to achieve a classification accuracy of 96.95% in identifying leaf diseases across two different plant species. The integration of mixed attention mechanisms improved feature representation, leading to a 47.63% reduction in Root Mean Square Error (RMSE) compared to traditional methods. Furthermore, our model demonstrates significant efficiency by processing 2000 images, making it suitable for real-time applications. These results underscore the potential of UAV-based systems combined with advanced deep learning techniques for precision agriculture, offering scalable solutions for early disease detection and management. © The Author(s), under exclusive licence to S

关键词： Deep learning

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Statistical assessment of Indian classical music’s role in diabetes, thyroid, hypertension management

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International Journal of Information Technology (Singapore) 2025年第1期17卷 423-429页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Research Scholar at Department of Computer Engineering Terna Engineering College Assistant Professor at Department of Computer Science and Engineering (Data Science) Vidyavardhinis College of Engineering and Technology University of Mumbai Maharashtra Mumbai India Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India

This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global health issues. The study examines how specific Ragas can enhance traditional treatments: Bhimpalas and Chhanayat for hypertension, Malkauns and Bhupal Todi for diabetes, and Shyam Kalyan and Yaman for thyroid disorders. Results show significant health improvements in patients integrating Raga Therapy into their care, highlighting its holistic benefits. The study underscores the profound connection between music and healing, positioning Indian classical music as a valuable complementary therapy. By addressing a critical gap in the literature, it advocates for further research to understand the mechanisms involved and refine Raga selection for optimal therapeutic outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Paired t-test Patient satisfaction survey Raga Therapy Wilcoxon signed-rank test

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Design and Implementation of IoT based flood alert monitoring system using microcontroller 8051

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Materials Today: Proceedings 2023年 80卷 2840-2844页

作者： Chaduvula, Kavitha Kranthi kumar, K. Markapudi, Babu Rao Rathna Jyothi, Ch. Assistant Professor Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head Department of Computer Science and Engineering Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Associate Professor Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Andhraparadesh Vijayawada India

In the Internet of Things (IoT) the physical objects network is described as "things" linked to sensors, software and other technologies to connect and share information through the Internet with other devices and systems. The floods lead to large losses of life and property in several countries. However, the absence of sufficient technology results in further death and property losses as a result of flooding in developed countries. This is because of a lack of flood alert systems. The objective of this paper is to monitor the flood situation and provide a text message warning in the event of a threat. The main aim of this paper is to detect rising river water levels at a safe distance away from the railways and to allow the respective authorities to take appropriate measures by means of SMS. A sensor is a system that senses and reacts to certain physical data. The 8051 microcontrollers were produced by Intel in 1981. The microcontroller is 8-bit. It has 40 DIP pins, 4 kb of ROM stores and 128 bytes of RAM and two timers and 16 bits. It is equipped with 2 timers. There are four parallel 8-bit ports that are programmable and adjustable as necessary. The hardware device that uses GSM mobile technology for the provision of an information link with a remote network is a GSM modem or a GSM module. A float switch is a level sensor type, a fluid control system that is used inside a tank. A condenser is a tool that stores electrical energy in an electric field. It's a passive electronic feature with two terminals. A LED is a semiconductor light source that transmits light through the flowing current. Liquid Crystal Display stands for LCD. It is a thin, flat display device used in a variety of electronics. A digital electronic level shifter is a system used in the transformation of signs from one logic or voltage domain to another, also known as logic level shifter or voltage level Translation. A diode is an electronic component with a two-terminal that mainly conducts curre

关键词： Microcontrollers

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A Robust and Energy Efficient Opportunistic Routing Protocol Design over Wireless Sensor Network to Enhance Communication Performance 2

A Robust and Energy Efficient Opportunistic Routing Protocol...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Shifani, S. Agnes Maheswari, S. Mary, A. Antony Metilda, M.I. Mary Rajkumar, G. Vinoth Muppudathi Sutha, S. Saveetha University Chennai Assistant Professor Division of Mathematics School of Sciences SRM Institute of Science and Technology Trichy Assistant Professor Department of Mathematics Sathyabama Institute of Science and Technology Chennai Assistant Professor Department of ECE J P College of Engineering Tenkasi Assistant Professor Department of ECE Sardar Raja College of Engineering Alangulam

ISBN: (纸本)9798350388916

Wireless Sensor Networks (WSNs) play a crucial role in monitoring and transmitting data in environments where traditional networks are impractical. This paper explores the essential principles that underpin the efficient operation of WSNs, focusing on energy efficiency, reliability, and adaptability. Energy efficiency is achieved through techniques such as duty cycling, low-power communication modes, and dynamic sleep scheduling, which collectively reduce energy consumption and extend battery life. Reliability is enhanced through robust error detection and correction mechanisms, as well as route redundancy strategies, resulting in improved packet delivery ratios: 95% with error detection, 97% with error correction, and 99% with route redundancy. Adaptability is ensured by opportunistic and topology-aware routing strategies that dynamically adjust to real-time network conditions. These advancements in energy harvesting, cross-layer optimization, and the integration of machine learning for smarter decision-making processes. © 2024 IEEE.

关键词： Adaptability Duty Cycling Dynamic Sleep Scheduling Energy Efficiency Error Correction Error Detection Low-Power Communication Packet Delivery Ratio Reliability Route Redundancy Wireless Sensor Networks

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Crop Yield Prediction Based on Weather and Soil Parameters Using Regression Tree Model 1

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4th International Conference on Communication, Devices and Computing (ICCDC 2023)

作者： Narasimha Reddy, Kandi Venkata Brahma Reddy, Evuri Assistant Professor Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopeta Guntur India Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopeta Guntur India

ISBN: (数字)9789819927104

ISBN: (纸本)9789819927098

The development of artificial intelligence and advances in computer science have provided a significant impetus for progress in the field of machine learning. In recent years, machine learning algorithms have been used effectively in the sector of agriculture with the purpose of accurately estimating the amount of crop output. The process of estimating the yield of crops using accessible historical data, such as characteristics relating to the weather and soil, as well as previous crop yields, is known as crop yield prediction. A function of machine learning known as regression makes predictions about a number. The use of regression methods may be highly beneficial when attempting to forecast the yield of crops. The development of regression methods applicable to the agricultural industry is the primary emphasis of this research. When attempting to estimate the amount of wheat, maize, and cotton that will be harvested, many types of regression analysis, including quadratic, pure-quadratic, interactions, and polynomial, are used. Finally, a regression tree model has been suggested that is capable of reliably predicting the yields of cotton, maize, and wheat. The metrics of Root Mean Squared Error (RMSE), R’, and Mean Percentage Prediction Error (MPPE) are used in the decision-making process to predict the yield in an effective way. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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Advancing Personalized Recommendation Systems with a Groundbreaking Collaborative Filtering Algorithm Driven by Machine Learning 2

Advancing Personalized Recommendation Systems with a Groundb...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor Information Technology St.Joseph's Institute of Technology Chennai India

ISBN: (纸本)9798350383867

By introducing this collaborative filtering algorithm, which is dependent on machine learning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personalized recommendation systems. Utilizing recent innovative designs of networks together with relevant contextual information, the algorithm provides high accuracy recommendation, scalability, and aids in improving user experience than traditional approaches. The model is shown to perform exceedingly well in all respects by its being subjected to a thorough assessment and compared with the baseline and enhanced models. It joins ranks with the best in all the indices of performance like precision, recall, F1-score, mean average precision and user satisfaction. In addition, hyperparameter tuning, and scalability metrics, so ending efficiency for model configuring and deployment recommendation models which could deal with large-scale datasets and offer users timely suggestions are of vital importance. To summarize, these results highlight the disruptive character of the latest machine learning algorithms that might create a new reality of recommendation systems with the trend of the time of individualization of relationships in the digital world. ©2024 IEEE.

关键词： Collaborative filtering

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Analyzing and predicting banking share dataset using data mining approaches

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Multimedia Tools and Applications 2024年 1-16页

作者： Vijayakanth, M. Veeramanikandan, V. Assistant Professor Department Computer Science Thiru Kolanjiappar Govt. Arts College Virdhachalam Tamil Nadu 606 001 India

Data mining is a process of finding and extracting hidden information from a larger dataset. It is used to analyse data patterns in volume of data using one or more data mining techniques. Data mining has applications in multiple fields, like science, engineering, banking, share marketing, and various research fields. A stock market is similarly called a share market. The stock market helps you trade financial instruments and shares of companies and allows trading of shares. Machine learning, within the realm of artificial intelligence (AI), is dedicated to crafting algorithms and models that empower computers to acquire knowledge from data and subsequently make predictions. In this paper, analysis and prediction consider State Bank of India (SBI) shares using different parameters, namely trading date, price, high, low, volume, and changes in share. The analysis and suitable parameters for prediction using supervised machine learning approaches, namely linear regression, random forest, random tree, and REP tree. Numerical illustrations also prove the results and discussions using different parameters. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Marketplaces

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Selective Text Encryption Using RSA for E-governance Applications for Pdf Document

Selective Text Encryption Using RSA for E-governance Applic...

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International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Adhikari, Subhajit Karforma, Sunil Assistant Professor BSH Department Institute of Engineering and Management University of Engineering and Management Kolkata India Research Scholar Department of Computer Science University of Burdwan Burdwan India Faculty Department of Computer Science The University of Burdwan Burdwan India

ISBN: (纸本)9789819944323

When data travels through an insecure medium, security must be enforced. The confidentiality of the exchanged data must be guaranteed with the help of encryption techniques. Selective encryption is a very powerful tool for encrypting textual data in a resource constrained environment. In the context of e-governance, textual data is very important and must be protected from any kind of security threat using selective text encryption. In this paper, a fast and efficient selective encryption technique based on an RSA asymmetric key encoding approach is presented. After fetching the whole textual information in the encryption phase, the user will search for a particular word or phrase using regular expression. After that, the selected data will be encrypted using the RSA-1024-bit algorithm and written to a new encoded document with the remaining text data. In the phase of decoding the data, only the encoded text of the document is considered. The experimental result confirms that the encrypting method is secure in terms of statistical security tests and fast in terms of computation time. Our encoding method can be implemented to encrypt any multimedia data like images, audio, and videos. This proposed technique can be used in IOT devices where resources are limited. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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