Reviewerly/exHarmony huggingface dataset:
Improving Twitter Sentiment Analysis Efficiency with SVM-PSO Classification and EFWS Heuristic

In the context of the increasingly interconnected world shaped by microblogging services like Twitter, sentiment analysis holds vital significance. The sway of others' sentiments and opinions over our perceptions is now undeniable, transcending individual viewpoints. Our investigation extends the boundaries of previous endeavours in Twitter sentiment analysis, adopting a strategic utilization of Distant Supervision. However, the existing method deals with challenges in analyzing the immense tweet volume due to computational limitations. To overcome this challenge, novel strategies are led in, aimed at expediting the sentiment analysis process. This involves connecting the subjectivity embedded within tweets to precisely select training samples. Additionally, the conceptual framework of EFWS (Effective Word Score) is introduced, intricately interlinked with tweets. This innovative heuristic, derived from polarity assessments of frequently encountered words, emerges as a catalyst for elevating the efficiency of sentiment classification through conventional machine learning algorithms. Experimental investigations encompass a dataset of 1.6 million tweets, showcasing the superior efficiency and heightened precision of the methods compared to prior approaches. Noteworthy is the overall accuracy of approximately 80%, which ascends to around 85% with the integration of the EFWS heuristic, utilizing a training dataset of 100K tweets – a dataset merely half the magnitude of the baseline model. Remarkably, the proposed model elevates accuracy by 2-3% relative to the baseline, all while accelerating training, achieving double the speed of the baseline. Furthermore, the classification leverages Support Vector Machine (SVM) with tuning via Particle Swarm Optimization (PSO), further enhancing performance.
Global Optimization using Random Adaptive Backtracking Particle Swarm Optimization (RAB-PSO)

Optimization algorithms, like the Particle Swarm Optimization (PSO), often suffer from premature convergence, providing poor convergence quality and slow convergence rates. In addition, striking a balance between exploration and exploitation adds complexity to its implementation. Moreover, while the algorithm's simplicity with a few parameters is advantageous for ease of use, it poses a significant challenge for improvement. This work presents a modified PSO variant, the Random Adaptive Backtracking Particle Swarm Optimization (RAB-PSO) algorithm. This algorithm combines three complementary modifications to address the limitations of PSO. Its main objective is to improve convergence quality while minimizing iteration counts required for achieving global minima. The first modification applied a delta parameter inspired by golf ball movements and incorporated it into the velocity formula. The second modification ensures that the inertia weight (ω) is adjusted to an S-shaped descending function to balance the search process. The third modification introduces a novel update strategy, enabling particles to backtrack when trapped, improving the algorithm's capacity to escape local optima and address premature convergence. These enhancements yield significant improvements, with the RAB-PSO algorithm demonstrating superior convergence performance on four test functions, requiring fewer iterations to reach optimal solutions.
Collaborative Learning based Recommendation System for Content Streaming Platform using Non-Negative Matrix Factorization Clustering

The Recommender System helps users find relevant content by leveraging their interest models. This paper focuses on content-based recommender systems, which determine recommendations based on previously selected products without needing user evaluations. These systems can now use machine learning to profile both consumers and items. We introduce a collaborative learning method for content streaming platforms using Non-Negative Matrix Factorization Clustering (NNMFC). The research employs a sliding window technique for clustering, implemented with gradient descent and further optimized using Particle Swarm Optimization (PSO). The model's performance was examined through its Root Mean Square Error (RMSE) across three techniques: gradient descent, sliding window gradient descent, and sliding window PSO. The result analysis of the proposed work achieved the lowest RMSE of 0.90, outperforming existing state-of-the-art approaches such as Collaborative Filtering, K-Mean, and UPCSim. The Sliding Window PSO emerged as the most proficient, presenting a notable 9% enhancement over existing methods.
Web Scraping using Natural Language Processing: Exploiting Unstructured Text for Data Extraction and Analysis

In recent years, combining web scraping techniques with Natural Language Processing (NLP) has emerged as a powerful approach to unlock deeper insights from unstructured textual data. This research study presents a detailed exploration of web scraping using Natural Language Processing (NLP) techniques, demonstrating how these methodologies can be synergistically integrated to extract and analyze unstructured text from diverse web sources. This research study analyzes the challenges posed by unstructured data on the web and how NLP can play a pivotal role in converting this text into structured and actionable information. The first part of the paper covers an overview of web scraping methods, including rule-based parsing, XPath queries, and the use of web scraping libraries such as BeautifulSoup and Scrapy. The second part of this research work focuses on applying NLP techniques to process and analyze the extracted textual data. Further, the preprocessing steps such as tokenization, stemming, and stop word removal, are analyzed followed by more advanced techniques like Named Entity Recognition.
Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text.
Supply Chain Management of Dual-Use Drugs using Blockchain

Supply chain management is a critical aspect of the pharmaceutical industry, as it ensures the timely and efficient delivery of essential drugs to end users. Dual-use drugs are those drugs which have both medical and non-medical applications. However, the management of dual-use drugs presents additional challenges, due to the possibility of misuse, diversion, and tampering. To address these challenges, it is necessary to track the supply chain of these dual-use drugs from the manufacturer to the end user. This research study investigates the use of blockchain technology in the supply chain management of dual-use drugs. This study explores the potential benefits of blockchain in improving the transparency, traceability, and security of the supply chain while reducing the risk of fraud and misuse. Additionally, the consensus algorithm inherent in blockchain technology makes it an ideal solution for preventing the manipulation and illegal export of dual-use drugs.
Dynamic Virtual Machine Consolidation in the Cloud: A Cuckoo Search Approach

The practice of consolidating virtual machines onto a smaller number of physical servers is known as Virtual Machine (VM) consolidation. This allows for a reduction in the total number of physical servers that are required to host virtual machines with minimum consumption of energy. There are lots of mechanism already developed by the various researchers in this filed to optimize the energy consumption rate. Therefore, in this research work, Cuckoo Search (CS) based VMs consolidation approach for Cloud Computing (CC). It is impossible to effectively optimize energy efficiency without compromising some form of system performance cloud service quality. The majority of current VM consolidation approaches are based on the concept of load and threshold, which affects the system performance in terms of Quality of Service (QoS) metrics such as energy consumption, Service Level Agreement (SLA) violation, and task scheduling overhead. Because of this, optimizing energy efficiency is currently impossible. Therefore, proposed a CS-based VMs consolidation approach to achieve this goal of research. When CS-based method is compared against literature in terms of energy consumption and SLA violation, the model achieves a considerable reduction in energy consumption while concurrently achieving a reduction in the quantity of service violations.
Evolving Convolutional Neural Networks with Meta-Heuristics for Transfer Learning in Computer Vision

In the rapidly evolving landscape of computer vision and artificial intelligence, transfer learning has emerged as a powerful tool for efficiently applying pre-trained models to new tasks. This article delves into the intriguing concept of evolving Convolutional Neural Networks (CNNs) with meta-heuristics for transfer learning in computer vision. The primary focus is on enhancing the adaptability and efficiency of CNNs, making them better suited for specialized tasks. The article covers the significance of transfer learning, the challenges faced in transfer learning with CNNs, the basics of CNN architecture, and the role of meta-heuristics in optimizing CNNs. Real-world applications and success stories demonstrate the transformative potential of these techniques in fields like medical image analysis and autonomous vehicles. It explores emerging trends and potential developments in the domain, emphasizing the impact on various sectors, including healthcare, natural language processing, and robotics. The promise of evolving CNNs with meta-heuristics lies in their capacity to tackle intricate problems with greater precision, ultimately reshaping the landscape of artificial intelligence and machine learning. Ongoing research ensures a promising future for this amalgamation of technologies, promising breakthroughs that will have a lasting impact on the world of computer vision and beyond.
Enabling an On-demand Access to Community Sentiments using LSTM RNNs Web Service Architecture

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users’ digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results.
Efficient Single Secret Image Sharing in Resource-Constrained Environment using Counting-Based Secret Sharing over Cloud

This paper presents a novel approach for secure and efficient single-secret image sharing in a resource-constrained environment. Traditional secret-sharing techniques are effective but often resource-intensive, making them less practical in scenarios with limited computational resources and storage overhead. While counting-based Secret-Sharing (CBSS) Schemes have been developed for numeric data, there has been a lack of schemes designed specifically for multimedia images. In response to this gap, our proposed model introduces a single-image sharing scheme based on bit permutation and counting bit operations, distributed over a cloud network. The key components of the proposed scheme include the group generation process, share generation and distribution, and secret reconstruction. In the group generation process, the secret image undergoes bit permutation to generate intermediate shares, which are then grouped into sets. Share generation involves performing counting bit operations on these groups to generate individual shares, which are subsequently distributed over a cloud server. The secret image can be reconstructed by combining all the shares and applying counting bit operations. The experimental findings illustrate that the proposed approach generates almost random shares. An assessment of the scheme's performance, utilizing metrics such as correlation, RMSE, and PSNR, indicates its superior results. The limitation of the scheme is that fewer than the required n shares may reveal the secret information.
Real Time Implementation of Biometric-based EVM System for Distinct Verification

In current existences, many techniques have been developed to cast votes. The existing scenario is tremendously aiming towards the development of an efficient voting system, which ensures individual authentication and reduces malpractices. It is a challenging perspective to recognize each and every individual person casting the vote. The proposed work considers this scenario and developed a controller based electronic voting machine using R305 Fingerprint sensor for biometric authentication of every individual. The controlled electronic voting machine gives ample scope to cast the votes in simpler manner without minimizing the efficient results. The proposed digital Biometric-based EVM is presented in this work. The processed flow for creating this biometric based EVM is implemented on FPGA board using Verilog software on Xilinx ISE. The experimental results produce efficient verification of unique authentication on selections of political parties.
I-Randomly Exploring Algorithm for Wheeled Mobile Robot Path Planning

An Improved-RRT-based route planning technique with navigable trajectories was proposed. In this research work, the classical RRT technique is enhanced by optimizing the Non-linear equations and extending the mechanism of the technique. At first, the proposed I-RRT Expansion procedure is done and a task called N-action is performed that could identify the navigable trajectory in the configuration space. Then, an efficient extend-based-obstacles avoidance action has been carried out in the chosen navigable trajectory that was traced by the proposed I-RRT Expansion procedure. Finally, a subsequent-operation process was performed to carry out the node trimming process and smoothening of the final optimal path traced by the I-RRT Expansion method. Trajectories that have been generated using the proposed Improved Rapidly-Exploring Random Tree (I- RRT) method not only satisfied direction constraints approach on both start and goal points, but also simultaneously guaranteed the effectiveness and continuity of the trajectory. Consequently, the trajectories produced by the non-linear vehicle systems like Wheeled Mobile Robots (WMRs) are found to be geometrically viable and dynamically feasible. The optimal trajectories for five identical scenarios each of grid size of 100 × 100 and each scenario consisting of different number of nodes (500, 750, 1000, 1250 and 1500) were generated and the proposed method Improved Rapidly-Exploring Random Tree (I-RRT) was applied to all the scenarios and the performances were evaluated. The simulation results illustrate that the time traversed by the wheeled mobile robot and the path lengths traced by the WMR to reach the goal using the proposed I-RRT algorithm in each scenario are reduced considerably when compared to the corresponding time traversed by the wheeled mobile robot and the path lengths traced by the WMR to reach the goal using the existing RRT algorithm.
A Proportional Work Analysis to Significant Approaches in Blockchain for Supply-Chain Technology

Modern technologies have replaced many of the conventional managerial work areas. Blockchain, one of the newest head-turn technologies, is attempting to highlight this apparent tactic. Improvements in the supply chain and logistics are desperately needed. There is constant pressure on these sectors to develop and seek out better solutions because almost all businesses rely on them to function efficiently. Blockchain technology, with its decentralized and publicly accessible nature, has provided answers to many of the problems that industry stakeholders are currently considering. The traceability, transparency, and openness of BCT are highlighted in this study as ways in which it benefits the supply chain sector. The top nations with publications about blockchain technology include China, the United States, Singapore, India, and Sweden. The rapid advancement of technology has made it necessary to stay current with the latest developments to stay ahead of the game. The main involvement of this work is a methodical, cyclical assessment of research and reference analysis to improve application and offer a better knowledge of the trajectory of these developments.
Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures.
Updated Particle Swarm Optimization for Mechanical Engineering Design Optimization

In the optimization area, particle swarm optimization is an excellent optimization algorithm. It influenced thru position and velocity update of the particles. Particles velocity update significantly depend on its factor inertia weight (with previous velocity) and acceleration coefficients (with cognitive and social components). Also, PSO has various advantages like simplicity and fast convergence. However, PSO easily trapped into local optima due to simple search procedure. To improve the presentation of PSO, this article advises an updated-PSO (U-PSO) for solving mechanical engineering design optimization problems. In U-PSO, novel parameters (inertia weight, acceleration coefficients and lively coefficient) for velocity update and position update equation are employed to retain exploitation and exploration. To verify the efficiency and robustness, proposed U-PSO applied to solve two complex mechanical engineering designs optimization problems. The experimental outcomes validate that the suggested U-PSO has significant advantages over the compared algorithms.
Decentralized and Multi-Authority based Public Key Infrastructure for Sharing Electronic Health Records

In healthcare systems, users require keys and certificates to communicate and share electronic health records (EHRs). The traditional EHR sharing system typically involves a single centralized trusted third-party (STTP) for authentication, auditing, key generation, and storing and managing patients' EHRs. To securely collect and share EHRs from multiple hospitals, users perform cryptographic operations. The cryptographic operations require keys that are generated by STTP. However, compromised or malicious STTP can disclose the users' information that is vulnerable to users' privacy and data sovereignty. Therefore, to enhance the trustworthiness and reliability of the STTP, we need to provide a solution that eliminates the dependency on the STTP. In this article, we propose a decentralized and multi-authority based public key infrastructure for summarized EHR sharing using Interplanetary File System (IPFS). The proposed system generates cryptographic key pairs for users using a binary tree-based multi-authority structure instead of relying on STTP. The proposed system prevents a single point of failure (SPOF) and is resilient to key-escrow problem. In addition, the proposed system contributes to the construction of privacy preserved summarized EHRs by securely collecting EHRs from multiple hospitals. Thus, summarized EHRs help in patients' well-being and enhance the quality of treatments. Furthermore, the performance analysis demonstrates that the proposed system is lightweight and efficient in aspects of execution time and storage overhead compared to existing solutions.
Genetic Algorithm Optimized Topic-aware Transformer-Based Framework for Conversational Question Generation

Recently, transformer models have gained interest with their varied range of natural language applications in several domains like education, healthcare, entertainment, and the like. Design of inquisitive bots which ask questions on a given topic of interest is one such application. To improve the quality of questions generated by such a bot while leveraging the power of transformer models, it is necessary to add a component to the pipeline that also considers the topic of discussion. This research examines transformer models pre-trained on natural language with the addition of topic context and subsequent optimization using genetic algorithms to generate more relevant questions. In our hybrid framework, we first use topic modeling for extracting context from the conversation history and then use these topics as input in the conversational training pipeline for generating questions on the given category. We attempt to fine-tune the framework to find the optimized weights using genetic algorithm. Our experiments are carried out using a large-scale dataset that is readily available to the public for the purpose of conversational question generation. We further evaluate the trained models on a dataset of Java passages performing qualitative analysis. Our analysis suggests that utilizing the genetic algorithm-optimized topic aware mechanism improves the quality of generated questions substantially in the conversational set-up.