Reviewerly/exHarmony huggingface dataset:
A Hybrid Multi-user Cloud Access Control based Block Chain Framework for Privacy Preserving Distributed Databases

Most of the traditional medical applications are insecure and difficult to compute the data integrity with variable hash size. Traditional medical data security systems are insecure and it depend on static parameters for data security. Also, distributed based cloud storage systems are independent of integrity computational and data security due to unstructured data and computational memory. As the size of the data and its dimensions are increasing in the public and private cloud servers, it is difficult to provide the machine learning based privacy preserving in cloud computing environment. Block-chain technology plays a vital role for large cloud databases. Most of the conventional block-chain frameworks are based on the existing integrity and confidentiality models. Also, these models are based on the data size and file format. In this model, a novel integrity verification and encryption framework is designed and implemented in cloud environment. In order to overcome these problems in the cloud computing environment, a hybrid integrity and security-based block-chain framework is designed and implemented on the large distributed databases. In this framework,a novel decision tree classifier is used along with non-linear mathematical hash algorithm and advanced attribute-based encryption models are used to improve the privacy of multiple users on the large cloud datasets. Experimental results proved that the proposed advanced privacy preserving based block-chain technology has better efficiency than the traditional block-chain based privacy preserving systems on large distributed databases.
Graph Neural Networks for E-Learning Recommendation Systems

This paper presents a novel recommendation system for e-learning platforms. Recent years have seen the emergence of graph neural networks (GNNs) for learning representations over graph-structured data. Due to their promising performance in semi-supervised learning over graphs and in recommendation systems, we employ them in e-learning platforms for user profiling and content profiling. Affinity graphs between users and learning resources are constructed in this study, and GNNs are employed to generate recommendations over these affinity graphs. In the context of e-learning, our proposed approach outperforms multiple different content-based and collaborative filtering baselines.
Use of Key Points and Transfer Learning Techniques in Recognition of Handedness Indian Sign Language

The most expressive way of communication for individuals who have trouble speaking or hearing is sign language. Normal people are unable to comprehend sign language. As a result, communication barriers are put up. Majority of people are right-handed. Statistics say that, an average population of left-handed person in the world is about 10%, where they use left hand as their dominating hand. In case of hand written text recognition, if the text is written by left-handed or right-handed person, then there would not be any problem in recognition neither for human and nor for computer. But same thing is not true for sign language and its detection using computer. When the detection is performed using computer vision and if it falls into the category of detection by appearance, then it might not detect correctly. In machine and deep learning, if the model is trained using just one dominating hand, let’s say right hand, then the predictions can go wrong if same sign is performed by left-handed person. This paper addresses this issue. It takes into account the signs performed by any type of signer: left-handed, right-handed or ambidexter. In proposed work is on Indian Sign Language (ISL). Two models are trained: Model I, is trained on one dominating hand and Model II, is trained on both the hands. Model II gives correct predictions regardless of any type of signer. It recognizes alphabets and numbers in ISL. We used the concept of Key points and Transfer Learning techniques for implementation. Using this approach, models get trained quickly and we could achieve validation accuracy of 99%.
Design of Multiplexers using Reversible Logic Technique

Reversible logic is promising as it can be applied to different applications in low power like nanocomputing especially in quantum computing. Reversible logic is a technioue for power reduction. Reversible circuits are similar to digital circuits but they work using reversible logic gates. This study focuses on reducing the garbage output and ancilla inputs in reversible multiplexers, thereby reducing the power consumption. In this study two designs of Multiplexers are given. Design1 is using TwinSJ gate and AJ gate. 2:1, 4:1 and 8:1 multiplexers are built. In Design 2, a new gate (SJ gate) is built which functions as 2:1 multiplexer. It has 4*4 configuration. The inputs are suitably configured so that it performs various logic functions. Using this SJ gate and other basic reversible logic gates, 2:1, 4:1 and 8:1 multiplexers are built. In 2:1 multiplexer, Ancilla inputs are improved to '0' from 5 and garbage output has been reduced to 2 against 7 in existing design. 4:1 multiplexers are built with ‘0’ ancilla inputs against 2 and 11 in existing designs. Garbage output of the proposed 4:1 multiplexer is 5 against 6 and 16 in existing designs. 8:1 multiplexer is built with 1 ancilla input and 11 garbage output against 2 and 12 respectively in existing design. This is designed using VHDL code - xilinx 14.7 for verification purpose and simulated on ISIM.
Motion Embedded Images: An Approach to Capture Spatial and Temporal Features for Action Recognition

The demand for human activity recognition (HAR) from videos has witnessed a significant surge in various real-life applications, including video surveillance, healthcare, elderly care, among others. The explotion of short-form videos on social media platforms has further intensified the interest in this domain. This research endeavors to focus on the problem of HAR in general short videos. In contrast to still images, video clips offer both spatial and temporal information, rendering it challenging to extract complementary information on appearance from still frames and motion between frames. This research makes a two-fold contribution. Firstly, we investigate the use of motion-embedded images in a variant of two-stream Convolutional Neural Network architecture, in which one stream captures motion using combined batches of frames, while another stream employs a normal image classification ConvNet to classify static appearance. Secondly, we create a novel dataset of Southeast Asian Sports short videos that encompasses both videos with and without effects, which is a modern factor that is lacking in all currently available datasets used for benchmarking models. The proposed model is trained and evaluated on two benchmarks: UCF-101 and SEAGS-V1. The results reveal that the proposed model yields competitive performance compared to prior attempts to address the same problem.
Complaints with Target Scope Identification on Social Media

A complaint is uttered when reality fails to meet one's expectations. Research on complaints, which contributes to our understanding of basic human behavior, has been conducted in the fields of psychology, linguistics, and marketing. Although several approaches have been implemented to the study of complaints, studies have yet focused on a target scope of complaints. Examination of a target scope of complaints is crusial because the functions of complaints, such as evocation of emotion, use of grammar, and intention, are different depending on the target scope. We first tackle the construction and release of a complaint dataset of 6,418 tweets by annotating Japanese texts collected from Twitter with labels of the target scope. Our dataset is available at \url{https://github.com/sociocom/JaGUCHI}. We then benchmark the annotated dataset with several machine learning baselines and obtain the best performance of 90.4 F1-score in detecting whether a text was a complaint or not, and a micro-F1 score of 72.2 in identifying the target scope label. Finally, we conducted case studies using our model to demonstrate that identifying a target scope of complaints is useful for sociological analysis.
IMPLEMENTATION OF DESIGN THINKING METHODS IN UI/UX DESIGNING JOB SEARCHING APPLICATIONS

Major changes in the job search have occurred with the development of the digital age. Therefore, user experience enhancement (UserExperience, UX) and user interface (UserInterface, UI) in job search applications is becoming an urgent need. This study aims to explore the application of the methoddesign thinkingin designing the UI/UX of a job search application namedCareer Hunt.Design thinking, a user-centric approach, enabling developers to understand deeply the needs, challenges and expectations of users, and create innovative and effective solutions. There are five stages used in this study: empathy, definition, ideation,prototype, and testing / test. In this study, interviews and contextual observations were conducted to gain a deeper understanding of user behavior and needs. Based on these findings, the main issues need to be solved, and then facilitate the sessionbrainstormingto generate a variety of potential solutions. Then, developprototypebased on those ideas and do user testing.feedbackreceived is used for literacy and further improvement. As a result, useDesign Thinkingin designUI/UXThis job search application has succeeded in increasing user satisfaction levels as well as job search efficiency. Keywords:Job Search,Career Hunt, Design Thinking, UI/UX
COMPARATIVE ANALYSIS OF STROKE CLASSIFICATION USING THE K-NEAREST NEIGHBOR DECISION TREE, AND MULTILAYER PERCEPTRON METHODS

Stroke has become a serious health problem; the main cause of stroke is usually a blood clot in the arteries that supply blood to the brain. Strokes can also be caused by bleeding when blood vessels burst and blood leaks into the brain. In one year, about 12.2 million people will have their first stroke, and 6.5 million people will die from a stroke. More than 110 million people worldwide have had a stroke. Handling that is done quickly can minimize the level of brain damage and the potential adverse effects. Therefore, it is very important to predict whether a patient has the potential to experience a stroke. The K-Nearest Neighbor, Decision Tree, and Multilayer Perceptron algorithms are applied as a classification method to identify symptoms in patients and achieve an optimal accuracy level. The results of making the three algorithms are quite good, where K-Nearest Neighbor (K-NN) has an accuracy value of 93.84%, Decision Tree is 93.97%, and Multilayer Perceptron (MLP) is 93.91%. The best accuracy value is the Decision Tree algorithm with an accuracy difference of no more than 0.10% with the two algorithms used.
IMPLEMENTATION OF DATA MINING TO PREDICT THE VALUE OF INDONESIAN OIL AND NON-OIL AND GAS IMPORT EXPORTS USING THE LINEAR REGRESSION METHOD

Indonesia's export-import activities in recent years, the value of Indonesia's exports and imports has decreased due to global conditions. The problems that occur are the uncertainty and complexity in estimating the value of international trade in the oil and gas and non-oil and gas sectors, dependence on just one or a few markets, and the problem of unfair competition, unfair competition between business actors can reduce export-import prices. The value of oil and gas and non-oil and gas exports and imports is influenced by several external factors that are difficult to predict, such as fluctuations in oil and gas prices, changes in trade policies, and global economic factors. The prediction results are obtained every month from the export value data using the rapid miner application. From the export data, the value of non-oil and gas exports obtains a very high value compared to the export data of oil and gas values. Then the results from rapid miner using the linear regression algorithm are obtained. The predicted import value of oil and gas and non-oil and gas value data in June is 209,162,268, and the predicted export value of oil and gas and non-oil and gas value data in June is 349,285,781 and non-oil and gas which more are predicted to have the highest value compared to the value of oil and gas in each month.
Comparative Analysis of Functionality and Aspects for Hybrid Recommender Systems

Recommender systems are gradually becoming the backbone of profitable business which interact with users mainly on the web stack. These systems are privileged to have large amounts of user interaction data used to improve them. The systems utilize machine learning and data mining techniques to determine products and features to suggest different users correctly. This is an essential function since offering the right product at the right time might result in increased revenue. This paper gives focus on the importance of different kinds of hybrid recommenders. First, by explaining the various types of recommenders in use, then showing the need for hybrid systems and the multiple kinds before giving a comparative analysis of each of these. Keeping in mind that content-based, as well as collaborative filtering systems, are widely used, research is comparatively done with a keen interest on how this measures up to hybrid recommender systems.
Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model

Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written, polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove &amp; fasttext) or context-aware (like XLNet) to effectively represent the features needed for processing. To improve the performace towards emotion or sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well.
An Approach of Stipulation Change Management Using Cloud Computing

Every technology project's successful implementation depends on the requirements. Changes in stipulations at any point of the software development life cycle are considered a healthy operation. Nevertheless, this transition is a little simpler in a co-located setting than in a decentralized system in which participants are spread over more than one area. This presents numerous challenges, such as coordination, communication &amp; control, effective and efficient management of changes and the management of central repositories. Cloud computing can therefore be used to mitigate these stakeholder problems. We used a case study to test the system of cloud computing.
Image Tagging using Modified Association Rule based on Semantic Neighbors

With the rapid development of the internet, mobiles, and social image-sharing websites, a large number of images are generated daily. The huge repository of the images poses challenges for an image retrieval system. On image-sharing social websites such as Flickr, the users can assign keywords/tags to the images which can describe the content of the images. These tags play important role in an image retrieval system. However, the user-assigned tags are highly personalized which brings many challenges for retrieval of the images. Thus, it is necessary to suggest appropriate tags to the images.&#x0D; Existing methods for tag recommendation based on nearest neighbors ignore the relationship between tags. In this paper, the method is proposed for tag recommendations for the images based on semantic neighbors using modified association rule. Given an image, the method identifies the semantic neighbors using random forest based on the weight assigned to each category. The tags associated with the semantic neighbors are used as candidate tags. The candidate tags are expanded by mining tags using modified association rules where each semantic neighbor is considered a transaction. In modified association rules, the probability of each tag is calculated using TF-IDF and confidence value.&#x0D; The experimentation is done on Flickr, NUS-WIDE, and Corel-5k datasets. The result obtained using the proposed method gives better performance as compared to the existing tag recommendation methods.
Classification of Acute Lymphocytic Leukemic Blood Cell Images using Hybrid CNN-Enhanced Ensemble SVM Models and Machine Learning Classifiers

Acute Lymphocytic Leukemia is a dangerous kind of malignant cancer caused due to the overproduction of white blood cells. The white blood cells in our body are responsible for fighting against infections, if the WBC increases the immunity will decrease and it would lead to serious health conditions. Malignant cancers such as ALL is life threatening if the disease is not diagnosed at an early stage. If a person is suffering from ALL the disease needs to be diagnosed at an early stage before it starts spreading, if it starts spreading the person’s chances of survival would also reduce. Here comes the need of an accurate automated system which would assist the oncologists to diagnose the disease as early as possible. In this paper some of the algorithms that are enhanced to detect and classify ALL are incorporated. In order to classify the Acute Lymphocytic Leukemia a hybrid model has been deployed to improve the accuracy of the diagnosis and it is termed as Hybrid CNN Enhanced Ensemble SVM for the classification of malignancy. Machine Learning classifiers are also used to design the system and it is then compared with enhanced CNN based on the performance metrics.
A Novel Chaos Quasi-Oppositional based Flamingo Search Algorithm with Simulated Annealing for Feature Selection

In present situations feature selection is one of the most vital tasks in machine learning. Diminishing the feature set helps to increase the accuracy of the classifier. Due to large number of information’s present in the dataset it is a tremendous process to select the necessary features from the dataset. So, to solve this problem a novel Chaos Quasi-Oppositional based Flamingo Search Algorithm with Simulated Annealing algorithm (CQOFSA-SA) is proposed for feature selection and to select the optimal feature set from the datasets and thus it shrinks the dimension of the dataset. The FSA approach is used to choose the optimal feature subset from the dataset. In each iteration, the optimal solution of FSA is enriched by Simulated Annealing (SA). TheChaos Quasi-Oppositional based learning (CQOBL) included in the initialization of FSA improves the convergence rate and increases the searching capability of the FSA approach in choosing the optimal feature set. From the experimental outcomes, it is proved that the proposed CQOFSA-SA outperforms other feature selection approaches in terms of accuracy, optimal reduced feature set, fast convergence and fitness value.
An Advanced Knowledge Based Graphical Authentication Framework with Guaranteed Confidentiality and Integrity

The information and security systems largely rely on passwords,which remain the fundamental part of any authentication process. The conventional authentication method based on alphanumerical username and password suffer from significant disadvantages. The graphical password-based authentication system has recently been introduced as an effective alternative. Although the graphical schemes effectively generate the passwords with better flexibility and enhanced security, the most common problem with this is the shoulder surfing attack. This paper proposes an effective 3D graphical password authentication system to overcome such drawbacks. The system is based on the selection of click points for generating passwords. The proposed work involved a training phase for evaluating the model in terms of the success rate. The overall evaluations of the model in terms of password entropy, password space, login success rates, and prediction probability in the shoulder surfing and guessing attacks proved that the model is more confidential and maintains a higher range of integrity than the other existing models.
Analysis of Challenges for Blockchain Adoption in Enterprise Distributed Applications

Decentralization, auditability, smart execution, and security are four ways that blockchain technology (BCT) differs from current cutting-edge technologies based on client-server architecture. Without the need of any middlemen, blockchain technology builds trust between untrustworthy parties. By employing its distinctive properties, blockchain technology is presently used to address the problems of enterprise distributed applications (EDAs) to some extent. As a result, businesses involved in a wide range of industries have shown interest in it. Despite being praised as tool for businesses to create secure applications, BCT is still not widely used. The objective of the current study is to use an extension of the technology acceptance model (TAM2), constituted by 15 hypotheses (H1–H15), to address the factors that influence professionals' desire to adopt the BCT in the EDAs. In order to achieve the research objective, the study consists of a quantitative non-experimental correlational method with the goal of creating an empirical model to evaluate the relationship between perceived usefulness, perceived ease of use, scalability, effort, performance, adaptability, maintainability, experience, and blockchain adoption in India with a focus on EDAs. Descriptive analysis, discriminant analysis, multiple linear regression, ANOVA, homoscedasticity, multicollinearity, reliability, linearity, survey question's normality, and independent errors are conducted to analyze survey data from a sample of 396 IT professionals from various firms in India. The findings show that IT professionals' desire to employ the BCT in EDAs are positively impacted by all the hypothesis except H3 and H8 that has no impact on IT professionals' desire to employ BCT.