Reviewerly/exHarmony huggingface dataset:
To Understand the Critical Measures of Enhanced Security in Cloud Computing for Creating Better Data Protection

Today, the use of cloud computing for better data protection has been increased to a great extent. One of the essential reasons for using cloud computing as a means of data protection is that users can easily access relevant data with the proper security protocols. However, various sectors such as healthcare, finance, automotive, education, manufacturing, etc. have experienced a lot of challenges regarding large and confidential data protection in the past few decades. With the advent of Cloud-computing in recent times, all the issues related to data security and information protection have been easier than before. Researchers have conducted an effective quantitative data collection approach by performing primary analysis through surveys. With a probability sampling technique, researchers have asked around 60 participants from different industrial fields about their opinions regarding the research topic. As per research findings around 60% of people have supported that cloud computing is essential for maximizing data protection. Around 30% people agreed with the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$24 \times 7$</tex> information monitoring and data encryption challenges can be solved with cloud computing. On the other hand, around 10% of participants have placed their positive opinion over the error reduction in data protection by using cloud computing. Researchers in this particular research paper are going to explain the importance of using cloud computing as a data security tool. In order to create better data protection systems across various industrial departments, cloud computing can be used in the future research as well.
Nomenclature of Diverse Feature Selection in Sentiment Analysis using Machine Learning Techniques: A Comparative Study

Sentiment analysis has gained in importance in recent decades as the amount of digital text documents has increased in tandem with the advent of IT. The goal of sentiment analysis in the field of natural language processing (NLP) is to extract positive or negative polarity from social media text. Feature Selection (FS) is important based on critical analysis and efficiently identifying the necessary information for better segregation. The purpose of this research is to assess and compare the performance of several sentiment analysis FS techniques. Several Feature Selection (FS) procedures are studied in order to select the optimal Selection of features from a feature set. Various Machine Learning techniques such as Decision Tree (DT), Naive Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbour (KNN) and Logistic Regression (LR) are used to train the selected features on the Online Movie Reviews Dataset. According to the findings of the experiments, the proposed system decreases data dimensionality, picks efficient aspects for better analysis
Artificial Intelligence for Software Engineering: Configurability Perspective

At first view, AI utility in software program engineering appears an ambiguity in phrases due to the fact synthetic intelligence is set routinizing processes with the aid of using depending on “shrewd machines” even as software program engineering, is an innovative and know-how in depth action, commonly related to human experts. Early estimation of configurability constantly facilitates to enhance layout, and might in the end result in nice promise. Practitioners emphasize at the want of getting a prepared and green method for configurability valuation. For this motive there's a want to expand an extra logical answer for configurability valuation at layout section. A for mentioned records inspired the academician to create the exertion on this path in addition to to expand a complete configurability valuation structure besides version to degree item orientated software's program configurability by the side of layout section of improvement existence cycle. In order to cope with the above studies gaps, the hassle announcement that has been formulated for the observe is recognized as ‘artificial intelligence for software engineering: configurability perspective’ In order to cope with the above studies gaps, the hassle announcement that has been formulated for the observe is recognized as 'configurability valuation framework and valuation model of object oriented software.
An Efficient Face Recognition System using Deep Transfer Learning

Face recognition is an AI-based innovation used to find and recognize human appearances in videos and images. Organizations can apply face recognition to many different kinds of fields which may include biometrics, regulation of law, security and individual wellbeing; so as to take observation of individuals in any scenario. Face recognition has advanced from simple vision methods to progress in ML; and further to progressively refined neural networks (ANN) and related advances. It currently assumes an indispensable part as the initial phase in numerous basic applications, including the task of tracking a face. Face recognition is utilized to focus cameras or count the number of individuals present in a particular region. The innovation likewise has showcasing applications, for instance, showing recommended promotions when a specific user is detected.
Multimodal Framework Using CNN Architectures and GRU for Generating Image Description

Convolutional Neural Network (CNN) architectures and natural language processing have become vital in analyzing and generating text from images and videos in the present era. To attain image description in natural languages, and capture the semantic image information, the authors proposed a multi-modal framework that automatically describes an image's content. This multi-modal framework aims to create relevant natural language information about images and their regions using deep learning techniques (VGG16 and Xception over image regions and GRU for sentence development) and then convert the text into Google Text-to-Speech (GTTS), which will be used to produce speech as it will be helpful to visually impaired people. The authors used the MSCOCO-17 dataset and achieved the best accuracy of 80% i.e., 0.8 BLEU-1 score using the Xception model framework and 70% accuracy i.e., 0.7 BLEU-1 score using the VGG16 model framework.
Evaluation of Digital Wallet Transaction Accuracy using Machine Learning

Non-card retail payments, such as direct-from-the-bank programs and E-wallets, have become increasingly common at both brick-and-mortar and online establishments. In recent years, the use of mobile wallets has increased. It relates to any form of computer program that can “learn” on its own beyond the intervention of a human. Machine learning can detect out-of-the-ordinary purchases and flag them as fraudulent by continuously monitoring data in real-time. Machine learning models are being improved by developing new methodologies based on pre-existing models. An example is a hybrid model in which the dataset is under-sampled and over-sampled; for example, credit card transactions. Digital wallets and machine learning are the main focus of this article.
Hybrid Deep Learning Techniques for Sentiment Analysis on IMDB Datasets

Social media is an important platform of interaction between people to share their thoughts in the way of views and opinions on some issue or story, resulting in a massive volume of unstructured knowledge. Sentiment analysis is the most commonly applied method for predicting user feedback. In the past era, numerous machine learning and natural language processing-based methods were utilised to investigate these feelings. Deep learning-based methods, on the other hand, are rapidly gaining popularity due to their high efficiency. This study uses a combination of deep learning methods, Convolutional Neural Networks (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) models to analyse movie feedback. With respect to Precision, Recall, F-measure, and Accuracy, the suggested CNN-BiLSTM Hybrid System surpasses traditional Deep Learning and machine learning methodologies, according to the results of the assessments. This methodology provided good results on the IMDB movie reviews dataset using hybrid Deep Learning algorithms.
Detection of Abnormal Driving Behavior Detection Using ADBDConvolutional Neural Networks

Monitoring anomalous driving behaviours in real time is a critical component of increasing vehicle safety. To improve driver behaviour and driving practisesin order to avoid car accidents. The use of vision-based anomalous driving behaviour detection is growing in popularity because it is fundamental to the safety of drivers and passengers in cars and is a crucial step toward attaining automated driving at this level at this time. This difficult detection task can be greatly aided by recent advancements in deep learning approaches, such as advanced deep learning models' remarkable generalisation power and the large volumes of video clips required for completely training these data-driven deep learning models. To wrap off the research work, novel deep learning-based models, inspired by the newly developed and widely used fully connected convolutional network named the Abnormal Driving Behavior Detection (ADBD Net), are presented.
Student Project Management System (SPMS)

Most undergraduate programs in Thailand require their students to develop student projects as the partial fulfilment of the program completion. The students who cannot initiate any project idea may not graduate on time. Therefore, the coordinator-led method can be implemented to help them select a project title. Manual management of student projects in the coordinator-led method is complicated and inefficient. This paper introduces a web-based application called Student Project Management System (SPMS). The application helps students, project advisors, and faculty administrators manage the project from the beginning to the end. The main features of this system are (i) project title recommendation, (ii) project and team prioritization, (iii) project and team matching, project management and scoring, and report generation. Currently, the system contains 205 projects and 228 users: 201 teams, 25 project advisors, and 2 administrators of the faculty.
Histogram Equalization variants preserving non uniform illuminated images

It is always a big concern to improve the condition of an image when it comes to fine details of under and over-exposed images. To enhance the fine details of images, an optimal enhancement technique is required. A lot more techniques are suggested in past. Among them, histogram equalization (HE) would be considered as best. The histogram is broadly used to amplify the contrast of an image for its clarity and directness. The main drawback of this technique is the flattening effect in graph that uniformly distributes the gray level. Several modifications of the technique have been made to overcome the barriers of the traditional histograms and enhance the non-uniform illuminated images. This paper provides insight into histograms and their versions. Moreover, the proposed paper proceeds with checking the quality parameter of techniques. The paper concluded the optimal method to be implemented on non-uniform illuminated images using the histogram equalization method.
Case Machine Learning Experience into Modelling and Simulation to Improve the Efficiency of Construction Engineering Process Management

The research work has highlighted that the main purpose of this research work is to identify the role of ML (Machine Learning) in simulation modelling and its effect on construction project engineering. In this research, it has been clearly identified that ML achieves the advantages such as risk identification, quality enhancement, and reduction of financial burden. These critical aspects are integrated into the engineering of the construction projects enabled by ML. The present research also shed light on the machine learning technologies for the completion of a construction project with much ease. Lastly, the research consists of some of the data collection methods by which the researcher could appropriately gather the research data and information for the smooth completion of the construction project.
Facial Recognition Automation System Using Machine Learning

The first crucial identity of human beings is through an individual's face in our evolving technical era. Biometrics has been explored and has given scope to a lot of recognitions technique. However the techniques involving fingerprint scan or card scanning system has been failed to prevent frauds and forgery to a large number of extent. The failures of these methodology gave rise to the facial recognition model. This methodology results in less frauds and forgery. The algorithm used that is Histogram Orient of Gradients. HOG along with effective Machine Learning approach boasts the previously occurring methodology to become more robust. The proposed method accumulates the information with the input face image and corresponding name and roll number; hereafter once the information is stored the face would be recognised and accordingly attendance would be updated. The whole automation of Facial Recognition is carried out with a user-friendly and effective architecture and robust Machine Learning approach.
Image Captioning using Deep Learning

With the improvement of Deep learning, the combination of computer vision and natural language processing has drawn in a ton of consideration. over the most recent couple of years. Picture subtitles address the means to figure out how to utilize at least one sentence to comprehend the visual substance of the picture. The method involved in creating a significant depiction of an undeniable degree of picture Semantics requires not just the impression of an article and a spot, yet the capacity to investigate the nature, elements, and connections between these items. Even though photograph subtitling is troublesome work, numerous analysts have tracked down critical enhancements. In this paper, we portray exhaustively the three inscriptions for symbolism utilizing profound neural organizations: CNN-RNN-based, CNN-CNN-based. What's a more dynamic-based structure. Then, At that point, we have available the fill-in that addresses three techniques in succession, clarifying the insightful measurements and sum up the principle benefits and burdens.
A Systematic Study of Deep Q-Networks and Its Variations

Recently, reinforcement learning has been at the forefront of the automation revolution. Reinforcement learning has proved to be a particularly powerful technique since it can understand the actions that will lead to ultimate success in an unfamiliar environment without any of the intervention of a supervisor. Due to the advent of Neural Networks, the unison of Deep learning and Reinforcement learning has led to unprecedented scaling of previously thought computationally expensive algorithms. This has caused an emergence of autonomous systems that can play games and automate robotics using visual cues, to name a few. In this paper, we intend to analyse a particular reinforcement learning algorithm called Deep Q-Networks and its numerous other variants. We cover several approaches including Improved Replay Memory, Distributional Outlook of Deep Q-Networks. Also, we discuss their advantages and disadvantages and conclude by detailing the current area of research in this field and future progress in sight.
The Application of the Internet of Things (IoT) to establish a technologically advanced Industry 4.0 for long-term growth and development

Nowadays, industries are observing the potential benefits of using the Internet of Things (IoT) and it increased their interest to invest in IoT technologies. The fourth revolution of industries (industry 4.0) influenced many companies, organisations and individuals to implement the IoT in their respective fields. Industry 4.0 not only looks for its implementation; however, it also demands long-term growth and development. This research has found recent technologies which help the industries to gain significant profit through IoT. However, the major barriers that do not allow long-term growth are security issues, lack of governance, lack of skilled staff and a lack of technological advancement. Therefore, this research is going to identify the new technologies and ideas via an online survey that will help industry 4.0 to rely on IoT for long-term growth and development. The online survey has been carried out with 68 experts who had experience in IoT. They provided their responses via a google form and the collected data were analysed. Findings showed that the major concern of IoT in industry 4.0 is security issues and it can be mitigated by implementing blockchain technology. More appropriately, consortium and public blockchains are more secure which will bring future prospects in the context of long-term growth. Problems related to big data can be addressed by advancing the hardware and software system and encouraging staff training.
The Effect of Separation of Task Functions and Application of Accounting Information Systems on Efforts to Disclose Accounting Fraud

This study aims to examine the effect of separation of task functions and the application of accounting information systems on efforts to disclose accounting fraud. The subjects in this study were 33 MSME actors in Tangerang as respondents. The data used in this study are primary data. This research uses quantitative methods, where the data collected from the results of distributing questionnaires are processed using the SPSS application, and presented and analyzed quantitatively (numbers) and descriptively (sentence descriptions). Based on the results of hypothesis testing using the T-test, the significance value of the Separation of Task Functions variable (X1) is 0.03, which is smaller than the significance level of 0.05 and the Application of Accounting Information System variable (X2) has a significance value of 0.022, which is smaller than the significance level of 0.05. So it can be concluded that the variables of the Separation of Task Functions (X1) and the Application of Accounting Information Systems (X2) can affect efforts to disclose accounting fraud. The results of this study indicate that the variables of the Separation of Task Functions and the Application of Accounting Information Systems can affect the Efforts to Disclose Accounting Fraud. This means that the detection of accounting fraud at MSMEs in Tangerang can be affected by the Separation of Task Functions and the Application of Accounting Information Systems. It can be concluded that the two hypotheses in this study are accepted.
Maintainability Quantification Model of Object Oriented Software: An Innovative Perspective

Quantifying software's maintainability prompt in the system development life cycle particularly at design and development time is a benchmark of fundamental importance to object-oriented designers, computer programmer as well as object-oriented software quality controllers. Early quantification of software maintainability, completely at design time helps programmers to increase their software designs beforehand the implementation starts. Practitioners as well as software developers regularly stated that maintainability evaluation must be scheduled at object-oriented design stage of system improvement life cycle. This research paper recommends a maintainability quantification model that workings at design stage of project improvement lifecycle. Additionally, statistical investigation is implemented to rationalize the correspondence of maintainability by means of its foremost donors Modularity as well as Reusability. The proposed model has been validated by means of experimental evaluation. In conclusion, it includes the firsthand validation of the proposed maintainability Quantification method.
Design of Weather Forecast System Using Python

Weather forecasting is critical for hydropower plant operation and flood management, for example. Computationally intensive mechanistic models are well-known. As a result, developing results that can anticipate weather forecasts more quickly than standard forecasting models are of interest. The scientific community is very interested in the topic of machine learning. It's worth looking at an ANN that can be a strong contender for predicting weather forecasting when combined with large file sets, given its wide range of applications. It is advantageous to have access to meteorological data from a variety of internet sources. A Python API for reading meteorological data was created to make data retrieval easier, and TensorFlow was used to create ANN models.
MTN: Tranformation from Mixed Dataset into Pure Numeric Dataset

Sometimes in real world problems we obtain mixed dataset having numerical as well as categorical attributes. Researcher and analysis while need the suitable format of dataset to use or develop any of the machine learning algorithm. This paper proposed a transformation method to transform the Mixed inTo pure Numeric dataset (MTN). This transformation method is based on similarity among the categorical and numeric attribute values.
Machine Learning Based recommendation system Using User-Item Interaction

Electronic commerce, or e-commerce, is the activity of trading services and commodities through the internet. Identifying the item that the consumer may buy from the enormous number of possibilities accessible to solve this difficulty is now one of the key difficulties encountered by most E-commerce businesses. Recommender systems have been implemented. Recommender systems (RS) are systems that collect information from users about their preferences and allow them to make decisions from the available options. Today, various recommender systems are growing with the advent of web-based information. As you can see from various articles, such recommender systems are used in a variety of industries, from simple objects to more sophisticated objects.RS has gained popularity in the previous decade, particularly in the realm of E-Commerce and related sectors. This report aims to identify recent developments as well as their potential for improvement. It is intended to elaborate on a number of points. And also work more on user-item based recommendation. These types of user-item based recommendation will be more effective in fashion area.
Web Information Retrieval Models Techniques and Issues: Survey

The main objective of information retrieval models is to searching and retrieving information from databases and provides users with that information which satisfy their needs. There are various information retrieval models have been developed till now. All these models are categorized based on their characteristics. To compute the efficiency of information retrieval model, we require a database of documents, detailed description suite of user needs expressible as queries and accurate assessment of each query-document pair. The information retrieval models have been used to judge effectiveness of information retrieval systems and considered a baseline for search engines evaluation. This paper focuses main feature of IR models and provides a comparison of these models.