Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Data Mining using Jaya based Genetic Algorithm

Privacy protection has emerged as a key concern in the field of data mining because of the rise in the sharing of sensitive data across networks among organizations, governments, and other parties. For knowledge extraction from these huge set of data, association rule mining is used to analyze the patterns of data. For a variety of optimization issues encountered in the real world, evolutionary algorithms (EAs) offer efficient solutions. The available EA solutions in the privacy-preserving area are limited to specific issues like cost function evaluation. Here, a JAYA based Genetic algorithm has been proposed for privacy preservation.“JAYA” means victory a word from Sanskrit origin..This algorithm doesn't need any parameters that are specific to it and it moves towards best solution avoiding the worst. Hence the name Jaya. Jaya based genetic algorithm is applied to original dataset of chromosomes. Privacy preservation is achieved by comparing the support of original dataset of chromosome with the simulation output.
Quantum Blockchain: Unraveling the Potential of Quantum Cryptography for Distributed Ledgers

The examination investigates the joining of quantum-safe cryptographic calculations into blockchain innovation, zeroing in on grid-based cryptography and hash-based marks. Because of the inescapable danger presented by quantum processing, this study proposes a quantum-safe blockchain system intended to upgrade the security and flexibility of circulated records. The cross-section-based cryptography calculation uses the computational intricacy of grid issues, offering protection from quantum goes like Shor's calculation. Simultaneously, hash-based marks give lightweight and quantum-safe choices for advanced marks, supporting the general validity of blockchain exchanges. The examination includes a multi-staged approach, incorporating a complete writing survey, hypothetical system improvement, algorithmic execution, and exhaustive investigation of versatility, execution, and information security. Reproduction results will illuminate ensuing equipment executions, approving the down-to-earth attainability of the proposed quantum-safe blockchain. Besides, the review digs into moral and administrative contemplations, adding to the foundation of capable rules for quantum-safe blockchain innovation. Insights into the performance of lattice-based cryptography and hash-based signatures, as well as the provision of a blueprint for future research in quantum-resistant distributed ledger systems, are among the anticipated contributions. The powerful idea of quantum advancements and blockchain requires continuous investigation, and the exploration makes way for future examinations concerning quantum-safe agreement components, upgraded Quantum Key Dispersion, and interdisciplinary coordinated efforts.
Emotion Detection and Classification using Hybrid Feature Selection and Deep Learning Techniques

Image sentiment analysis has gained significant attention due to the increasing availability of user-generated content on various platforms such as social media, e-commerce websites, and online reviews. The core of our approach lies in the deep learning model, which combines the strengths of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNN component captures local dependencies and learns high-level features, while the LSTM component captures long-term dependencies and maintains contextual information. By fusing these two components, our model effectively captures both local and global context, leading to improved sentiment analysis performance. During the execution first select the context and generate visual feature vector for generation of captions. The EfficientNetB7 model is applied in order to construct the image description for every individual picture. The Attention-based LSTM as well as Gated Recurrent Unit (GRU) greedy method are the two approaches that are utilized in the process of classifying sentiment labels. The proposed research has been categorized into three different phases. In Phase 1 describe various data preprocessing and normalization techniques. It also demonstrates training using RESNET-101 deep learning-based CNN classification algorithm. In Phase 2 extract the various features from the selected context of input image. The context has been selected based on detected objects from the image and generates a visual caption for the entire dataset. The generated captions are dynamically used for model training as well as testing to both datasets. The EfficientNet module has used for generation of visual context from selected contexts. Finally in phase 3 classification model has built using a Deep Convolutional Neural Network (DCNN). The proposed algorithm classified the entire train and test dataset with different cross- validations such as 5-fold, 10-fold and 15-fold etc. The numerous activation functions are also used for evaluation of the proposed algorithm in different ways. The higher accuracy of the proposed model is 96.20% sigmoid function for 15-fold cross validation.
Feature Selection based Sentiment Analysis on US Airline Twitter Data

Review document emotions are classified using sentiment analysis. Researchers grade features to remove non-informative and noisy attributes with low grades to improve classification accuracy. This paper utilizes six different NLP models to predict user sentiments based on Twitter reviews about airlines, using the Twitter US Airline Sentiment dataset. The best-performing models from both machine learning (K-nearest neighbor, Random forest, and Multinomial Naive Bayes) and deep learning (Artificial Neural Networks, LSTM, and Bidirectional LSTM with Glove embeddings) were implemented through Anaconda and Google Colab platforms. This paper introduces a new type of feature dimensionality technique termed "inquiry extension grade (IEG)," inspired by the inquiry extension term weighting technique. Additionally, we modified the traditional TF-IDF method, referred to as "improved TF-IIDF (IFFIDF)," specifically tailored for processing unbalanced text collections. To assess the effectiveness of the proposed methods, a series of simulations were conducted. The results indicate that the combination of IEG-ITFIDF Vectorization and Bi-LSTM with Glove embeddings yielded the best accuracy of 94.26% in sentiment classification for the Twitter US Airline Sentiment dataset.
System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition

Social recommender systems are getting up more attention for product advertisement and social connectivity. A good recommender should think about the system and the user. The user will have a preference list of some items and these preferences can be useful in suggesting the things which can help the endorsing system to identify better items. In this paper, the idea of social recommender systems as a pattern matching and regular expression making is used for unification of similarities. The concept of mutual profile pattern expression can be applied on various networking platforms. In these type of shared platforms, people all around the globe share resources and interact with each other. In order to manage or scrutinize users according to their interests and likeness, the mutual profile pattern of users can be used. Further predicting of membership function is performed to show how much extent does the profile matches.
HealthBlock: A Blockchain-IoT Fusion for Secure Healthcare Data Exchange

Managing healthcare data while ensuring its security and privacy is critical to providing quality care to patients. However, traditional approaches to healthcare data sharing have limitations, including the risk of data breaches and the lack of privacy-preserving mechanisms. This research paper proposes a novel hybrid blockchain-IoT approach for privacy-preserving healthcare data sharing that addresses these challenges. Our system incorporates a private blockchain for protected and tamper-proof data sharing, with privacy-preserving techniques such as differential privacy and homomorphic encryption to protect patient data. IoT devices are utilized to collect and transmit real-time data, equipped with privacy-preserving mechanisms such as data anonymization and secure transmission protocols. Our approach achieved an accuracy rate of 98% for access control and a 99.6% success rate for data privacy protection. Furthermore, our proposed system demonstrated improved data storage and retrieval performance, with a data storage overhead reduction of up to 86% and a data retrieval time reduction of up to 81%. These results indicate the potential of our approach to enhance the security, privacy, and efficiency of healthcare data management, contributing to improved patient care outcomes.
Secure and Decentralized healthcare applications using Blockchain

Before the Internet period started, we had Usenet wherein the clients needed to run their very own servers so as to impart and as web went along 10 years after the fact one needed to have their site on the server they claimed and needed to purchase a DNS area to point to their site making a decentralized framework, however this gave security which nobody expected in those days it was likewise unbelievably costly and consequently came the facilitating administrations which advanced a progressively concentrated framework which was without a doubt increasingly productive and less expensive around then. Brought together frameworks were increasingly advantageous to clients yet as the quantity of clients continued duplicating, it gave huge capacity to the organizations that claim these incorporated frameworks. These enterprises claim heaps of client information and control the web with the ability to impact all that we see and do on the web. The ongoing advancements in the Blockchain innovation have allowed to bring back the decentralized frameworks and it is right now being utilized by digital forms of money and in fintech yet with more research on blockchain could change different mechanical parts of our life including our administration and the ebb and flow human services framework
Enhancing Multi-Object Detection in Video Content: Exploring Hybrid Techniques and Method Combinations for Improved Classification Accuracy

The classification of objects within video content holds significant importance, particularly in the context of automated visual surveillance systems. Object classification refers to the procedure of categorizing objects into predefined and semantically meaningful groups based on their features. While humans find object classification in videos to be straightforward, machines face complexity and challenges in this task due to various factors like object size, occlusion, scaling, lighting conditions, and more. Consequently, the demand for analyzing video sequences has spurred the development of various techniques for object classification. This paper proposes hybrid techniques for multi object detection. The experimental analysis focused on a vehicles-openimages dataset containing 627 different catagories of vehicles. The results emphasize the profound impact of method combinations on image classification accuracy. Two primary methods, wavelet transformation and Principal Component Analysis (PCA), were employed alongside Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The evaluation encompassed performance metrics, including accuracy, precision, recall, specificity, and F1 score. In the analysis, Wavelet + RNN" combination consistently achieved the highest accuracy across all performance metrics, including accuracy percentage (96.76%), precision (96.76%), recall (86.32%), F1 score (87.12%), and specificity (87.43%). In addition, the hybrid classifiers were subjected for image classification of different vehicle catagories. In the analysis of different catagories, Wavelet + RNN" emerges as the standout performer, consistently achieving high accuracy percentages across all object categories, ranging from 82.87% for identifying People to 90.12% for recognizing Trucks.
Ideal Keyword Match in a Big Data Application Using Keyword Aware Service Recommendation Method

The big data movement additionally influenced service recommender systems. The emergence of alternative providers has created a big research issue in providing clients with relevant suggestions for services they want. Service recommender systems have proven to be helpful tools that help users manage the multitude of services at their disposal and provide pertinent recommendations. Because the quantity of customers, services, and other online information is growing exponentially, service recommender systems function in a "Big Data" context. This poses serious challenges for these systems. In this work, we address these difficulties by contributing the following: This makes use of a collaborative filtering algorithm that is user-input driven. Keywords extracted from user reviews reflect their preferences here. Additionally, we apply it to Hadoop, a distributed computing framework that builds on Map Reduce for processing. by applying a collaborative filtering process that is user-based. In the proposed system, we are using a user-based Collaborative Filtering method. It also has similarities to the existing system. We consider both customer reviews and company rankings. We provide KASR, a method for keyword-aware service recommendation. Key words in KASR serve as indicators of users' preferences, and recommendations are produced by a user-based Collaborative Filtering algorithm. A domain thesaurus and keyword-candidate list are provided to help better understand the preferences of the customers. The active user indicates their choices by selecting keywords and preferences from the keyword-candidate list.
Neural Machine Translation from Bengali Language to English language and vice-versa

Bengali ranks among the first ten spoken languages in the world with a native speaker numbering about 230 million people. With UNESCO declaring 21st February as International Mother Language Day to commemorate the laying down of lives by five Bangladeshi students for the cause of their mother tongue, Bengali has come into the radar of worldwide attention . Though significant amount of prose, poetry have been written in Bengali language and large number of newspapers in Bengali get published daily, technically it is still considered a Low Resource Language (LRL) unlike English or French which are High Resource Language (HRL). The reason is not far to seek as corpora in varied domains such as short stories, sports, politics, agriculture etc is less in number and even when they are available, the size is less. Machine translation (MT) is difficult to perform in Bengali as parallel corpora from Bengali to other languages and vice versa is few and far between and when they are available they suffer from the problems of size and quality. This work is aimed at implementing one state of the art model in Neural Machine Translation (NMT) which is called the self-attention transformer model to perform translation from English to Bengali and vice versa. Though a couple of research work has been published in the recent years on MT from English to Bengali, they are mostly domain specific. This paper does not focus on any specific domain for NMT from English to Bengali and as such may be conceived as a more of general domain NMT from English to Bengali which is more difficult than domain specific NMT. Performance evaluation of the model was done using BLEU version-4 vis-à-vis translations of well known English-Bengali MTsystems.
Analysis of the influence of website quality (webqual) 4.0 Sikhuli.umri.ac.id on student satisfaction at Muhammadiyah University of Riau

This research aims to determine student satisfaction with the SiKuli Muhammasiyah Riau University website, which is e-learning at Muhammadiyah Riau University. The population in this study were all students from the Faculty of Teacher Training and Education and the Management Study Program at Muhammadiyah University of Riau. The research sample consisted of 80 students in each study program who were taken using proportional random sampling. The data collection technique uses a questionnaire. Before the questionnaire is used to collect data, the questionnaire is tested for validity and reliability. Test the validity of the analysis instrument used by factor and item score analysis with a total score. The data analysis technique used is descriptive analysis with percentages. The results of research using the Chi Square and Webqual methods of measurement based on quality function deployment (QFD) are to measure the quality of a website based on research instruments that can be recommended into three variables, namely: usability, information quality, and interaction services. All of them are measurements of consumer or user satisfaction with the quality of the website.
The Influence of Foreigner Student’s Tourism Satisfaction on Acculturative Stress and Adaptation to College

As a solution to the current decline in the number of students, Korean universities are required to change their management due to the attraction and increase of international students. In this study, an empirical analysis was conducted through a survey on the effect of foreigner students
Proposed Watershed Technique Based on Whale Optimized Algorithm for Medical Images Segmentation (WAWOA)

Image segmentation is a way which applied to split an image for many parts. It will generate image smooth and easy to evaluate. A useful image segmentation technique is help the area authority in medical images. Also, a good segmented images are important in many medical fields like radiologist, pathologist of quick and successful analysis. It has used effectively in diagnosis for many disease. Nature inspired algorithms imitate the mathematical and innovative techniques for non-linear and actuality problems and can be achieved to segment or analyses the images. In this paper, a watershed as segmentation technique (WAWOA) has applied to segment the medical dataset image then proposed a whale based segmentation technique which can be used on medical bones image anyways the nature or style of the image. For fair analysis of these techniques, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and Universal Image Quality Index (UIQI) of segmented images are analyzed by using MATLAB. The success of the proposed technique has confirmed with evaluated via comparing the obtained with outed results beside a standard watershed technique. The results showed this technique develops the segmentation of medical images and can help with better diagnosis. The evaluation results of our proposed technique show the success and efficiency of segmented images in high rates.
Information Quality, Social Friendship and Social Interaction in Predicting Web Usage and Online News Readers’ Understanding

This research aims to reveal information quality, social friendship, and social interaction in predicting web usage and understanding of online news readers and also to reveal that web usage predicts online news readers’ understanding. This quantitative research uses an online survey design based on a purposive sampling technique using 423 respondents who are active readers of online news. The data in the study were analyzed using SEM Amos and SPSS 21. The results and findings showed that information quality and social interaction could positively and significantly predict web usage and understanding of online readers. Web usage could also predict online reader understanding positively and significantly. Social friendships could not predict web usage and reader understanding of online news based on research results. The theoretical implication of the research aims to fill the gap in the existing literature on information quality and social interaction that can explain web usage and understanding of online newsreaders and web usage as a predictor to explain the effect of comprehension as an individual’s intended media use. The practical implication of this study is that websites as news sources must pay attention to information and features that are more attractive to readers or potential online news readers.
Pemanfaatan Teknologi dalam Model Pembelajaran Olah Alur pada Pembelajaran Menulis Cerpen

The use of technology in learning is something that educators need to continue to innovate so that the learning process is in line with current developments. The use of this technology can be developed in selecting learning models because it is a learning component used to support the achievement of learning success. The Olah Alur learning model is a learning model developed by optimizing the use of technology and using modeling methods to emphasize short story literacy activities as a reference in producing a new short story according to the characteristics and needs of students. The purpose of this research is to explain the use of technology used in the Flow Process learning model. The method used in this research is descriptive qualitative method. Based on the results of data analysis, it was found that there were five uses of technology in the Olah Alur learning model, including the use of website spinner, Google Drive, PowerPoint, digital LKPD, and social media. These media are used as an effort to improve the quality of learning to write short stories at Al-Mubtadiin Babat Lamongan Integrated Middle School. Thus, it can be concluded that the use of technology is used as a medium and learning resource for students to be able to provide quality experiences.
Challenges Facing Procurement Processes Digitalization: A Case of Tanzania Electric Supply Company Limited - Dar es Salaam

Purpose: This study aimed to assess challenges facing digitalization of procurement processes at Tanzania Electric Supply Company Limited (TANESCO) in Dar es Salaam.&#x0D; Materials and Methods: Technology Acceptance Model was utilized in this study. This research utilized a mixed research approach, combining quantitative and qualitative methods, targeting all 408 employees across various Tanesco departments. Sampling techniques included simple random and purposive sampling, with data collected through questionnaires, interviews, and documentary review. Descriptive statistics, facilitated by SPSS version 26, were employed for quantitative data analysis, while content analysis was utilized to analyse the qualitative data obtained from interviews.&#x0D; Findings: The challenges identified in this study, include concerns about limited vendor relationship improvements and the need for enhanced transparency. Based on the findings, it is recommended that TANESCO continues to embrace digitalization in its procurement processes but focuses on addressing challenges to maximize its benefits.&#x0D; Implication to Theory, Practice and Policy: Future research should examine employee attitudes and the effectiveness of change management strategies in the context of digital procurement could contribute to a comprehensive understanding of the human dimension in the digitalization journey.