Reviewerly/exHarmony huggingface dataset:
Energy Preserving ABE-Based Data Security Scheme for Fog Computing

A cutting-edge technology called fog computing makes it possible to offer services and applications for IoT/end devices at the network's edge quickly and effectively. As the fog computing paradigm is driven from the cloud computing paradigm, the security and privacy concerns have been also passed down to fog. But fog and cloud differ in terms of architecture and capabilities, thus the available cloud solutions are irrelevant to the fog. Moreover, fog is resource constrained in nature as well as it has limited energy as it runs over batteries. Therefore there is a need to develop an energy preserving lightweight security scheme to secure the data as well as to provide a fine grained access control mechanism. Thus, in this article we have developed a cryptosystem based on Chosen-Ciphertext Attribute-based encryption (ABE) with dynamic key change in order to secure the data and to prohibit unauthorized access in an energy efficient manner. We have evaluated and analyzed the sturdiness and the security of our proposed cryptosystem considering the energy consumption and mitigating attacks as primary aspects. Additionally, our cryptosystem is effective and energy efficient compared to other techniques.
Modern methods of forming the image of an organization (study group) as an element of a marketing strategy

The image of an organization (study group) is one of the forms of managing the choice of a potential client (a student and a teacher). The research problem remains relevant: firstly, society has become more conscious: students choose a university based on the image (status) of the organization; teachers make a choice in favor of cooperation and work in certain educational institutions, relying solely on the image of the organization, which corresponds to the personal values of teachers, secondly, there are still students in higher education institutions who have made the choice to enter the university based on proximity to home, and teachers do not identify themselves and their personal brand with the organization in which they work.
Do Prior Information on Performance of Individual Classifiers for Fusion of Probabilistic Classifier Outputs Matter?

In this paper, a class of classifier fusion methods are compared to verify the impact of the use of some prior information about individual classifiers during fusion of probabilistic classifier outputs. In particular, we compare two versions (i.e., uninformed and informed versions) of a performance-agnostic fusion of probabilistic classifier outputs from Masakuna et al. (2020) (called Yayambo). Yayambo is iterative and treated black-box classifiers. For this paper, cases where prior information, i.e., performances of individual classifiers in the form of accuracy is taken into account for fusion of classifier outputs, are considered. Then we discuss the relevance of prior information for combination of probabilistic classifier outputs. The experiments have demonstrated that classifier fusion methods, for both informed and uninformed fusion methods, achieve different performances, i.e., the differences are significant in general (using the p-value and the effect size (Gail & Richard, 2012)). Surprisingly, in some particular cases and under the same experimental conditions, the two versions of Yayambo achieve similar results (using the $$p-$$ value). This means that one might not need to carefully, for some situations, select a classifier fusion method. We consider 12 classifier fusion methods (5 uninformed and 7 informed), use 8 data sets and apply different experimental settings to address our research question.
An Efficient Classification for Light Motor Vehicles using CatBoost Algorithm

This paper proclaims the application of light motor vehicles (LMV) classification employing the unique algorithm named CatBoost (CB) algorithm to boost the accuracy in vehicle classification concerning features of colors and shapes. Normally, the classification grounded on non-identical parameters including label features, various classes, peculiar structures, features tobe extracted, segmented portrayal and connotation classification is greatly protested to incorporate in machine learning model. In this circumstance, the CatBoost algorithm has been utilized to gain high performance in LMV classification from the huge surveillance dataset. The empirical outcome accuracy is gained in LMV classification with high grade of resolution images.
Chess Game to Improve the Mental Ability of Alzheimer's Patients using A3C

Cognitive Agents' human-like reasoning is making them popular. Hence, they can aid with time-consuming jobs, complex challenges, and consumer interactions. Therefore, such technology might be better understood as a guide or assistance, speeding up and simplifying routine yet time-consuming activities. This research work explores how to use cognitive agents as an assistant for helping people (someone with a cognitive disability) to play chess by improving their brain activity and cognitive skills. Here, The Asynchronous Advantage Actor Critic (A3C) algorithm is used for training the agent about the chess game. The primary purpose of the Cognitive Agent is not to assist the player in winning games, but rather to improve the player's skills by providing the opportunity to acquire new approaches and tactics. Here, the Cognitive Agent will provide the player with information about the possible outcomes of their present line of action and provide them with a series of suggestions or guidance on the next possible moves, whether offensive or defensive. The Cognitive Agent gets activated when the Player has not made any move for some time (When the Player is unable to decide on the next move) and also when the Player's winning percentage falls dangerously below a certain threshold (When the Players course of action will definitely lead to a possible defeat outcome). The goal of this work is to help people with Alzheimer's or cognitive disabilities to learn to play chess and improve their brain activity and thinking skills.
Diagnosis of Brain Tumor using MRI Techniques

This study has developed a computer-based technique in order to detect tumor regions in the human brain by employing magnetic resonance imaging (MRI) images. MRI is one of the medical imaging methods employed in radiology for the visualization of the internal structures of the human body. Using this technique, the acquired detailed anatomical data are used to examine the human brain development and detect its abnormalities. Initially, based on their health status, human brains are categorized into healthy brains and brains with tumors. The latter category is subdivided into those with malignant and benign tumors. There are a variety of techniques used to classify MRI images of the human brain today. The algorithm presented in this research is focused on preprocessing, augmentation, segmentation, and eventually morphological operations. The first step taken in the analysis of an MRI image is its preprocessing, which employs image improvement and noise reduction approaches used to enhance the quality of the image. The simplest technique of image segmentation is thresholding.
SIFT algorithm-based Object detection and tracking in the video image

Recently, it's been a breeze to snap photos of high quality and good size, thanks to the widespread availability of low-cost but technologically advanced photo-capturing devices. A video is a series of still photographs taken at regular intervals. Since video can capture more of a scene over time, it can tell us more about our item in dynamic. A static backdrop frame is compared pixel-by-pixel with the current video frame for easy object recognition. The initial step of existing methods in this field is identifying the item of interest within the video sequences. Among the numerous challenges inherent in object tracking is selecting appropriate characteristics and models for locating and following the target item in a video. Intensity, form, color, and feature points are often chosen as relevant characteristics for categorizing visual objects. The methods of mean-shift trailing using the color pdf, optical drift surveillance using strength and motion, and SIFT (Scale inalterable feature transmute) tracking using scale-inalterable local feature points were all investigated in this research. Experimental findings thus far suggest that the chosen technique can successfully follow targets that undergo movement, rotating, motion blur, and distortion situations. For this reason, it is impossible to process movies manually. Therefore, a machine is required to analyze these movies. In this research, we make an effort to do just that via video object tracking. Technology and algorithms have advanced to the point that they can now automatically track the movement of an item inside a video clip.
An Analysis of Chaos-based Cryptographic Algorithms

Both wired and wireless media are used in the healthcare industry to transmit medical images or data from their source to their destination. More security must be used while transmitting this information. Image data is transferred from one location to another using encryption and decryption procedures. Encryption is a technique for transforming data and information from their initial state into one that hides the information inside. The image data must be protected from unauthorized access. Using encryption increases data security. In order to prevent any form of cryptanalysis, the image has been encrypted. There have been numerous explorations into chaos-based cryptosystems in the past decades. Despite these approaches are less well-known than AES, DES, RSA, etc., they can increase security when combined with classical public key cryptosystems like RSA and Elgamal. Although chaos-based cryptography approaches are frequently suggested for image or media encryption due to their quick processing, classic cryptographic techniques like AES have typically been selected.
Accurate SMS Spam Detection Using Support Vector Machine In Comparison With Linear Regression

The aim of the study is to detect SMS spam using Support Vector Machine (SVM) and linear regression (LR). The dataset used in the study contains 5573 sentences, and accuracy is measured for SMS spam detection. The classification process is carried out using SVM and LR with sample sizes of N=27, which were obtained using a G-power value of 80%. The accuracy of SVM is found to be 97.67%, which is higher than LR with an accuracy of 92%. The p-value for the significant accuracy difference is 0.02 (p<0.05), indicating that SVM performs better than LR in achieving accuracy.
Automation of Short Answer Grading Techniques: Comparative Study using Deep Learning Techniques

Automatic short answer grading (ASAG) techniques have been shown to cut down on the time and work needed to grade exams, and it is a method that is becoming more and more common, especially with the rise in popularity of online courses. This study compares the results of 7 pre-trained embedding models using just one feature to automatically grade brief responses: the similarity between the model answer's and the student answer's embeddings. Regression models are developed and evaluated to predict a short answer's score based on the similarities between all pairs of answers in the Mohler dataset. The predictions are evaluated by comparing the Root Mean Squared Error (RMSE) and Pearson correlation scores of each model.
Reactive Fault Tolerance aware Workflow Scheduling Technique for Cloud Computing using Teaching Learning Optimization Algorithm

Cloud computing has led to a metamorphosis within the delivery model of information technology, from a product to a service. The resources of cloud computing should operate without mistakes or malfunctions is one of the primary challenges. Nevertheless, the cloud environment's ever-changing characteristics lead to a variety of unforeseen problems and malfunctions. Cloud services are hampered by their own vulnerability to disruption as a result of faults or failures in operations, causing them to perform less well than they could. To ensure the availability and dependability of crucial resources, fault tolerance is a fundamental concern. Failures should be anticipated and handled proactively. In order to achieve resilience and reliability in cloud computing, effective failure evaluation and management are required. To anticipate these problems and take the right action before they really happen, many proactive fault tolerance techniques have been proposed and are being deployed. However, most of the proactive fault tolerance methods could not yield a significant solution or help anticipate the faults. Failure to complete a task is no longer an accident but rather a typical feature in cloud computing environments. In this paper, a reactive fault tolerance-aware workflow scheduling approach (RFT A WS) for cloud environment is proposed, that is optimized for teaching and learning. This approach aims to reduce the likelihood of dynamic tasks failing before they are supposed to by taking into account the resources that are already available. The effectiveness of the suggested approach RFT A WS is assessed using the failure ratio, performance improvement rate, and rejection ratio to estimate the scheduling task. The result shows that the suggested system works better than the ones that are already in place and makes data more accessible and reliable.
Role of Blockchain and Information Classification Management

The hyper-network achieved by Industry 4.0 has not just prompted a quick development in the volume of data, however it has likewise prompted an expansion of the districts and resources that should be protected. With regards to information insurance, this has brought about a colossal ascent in the cost of the numerous and fluctuated safety efforts that have been carried out to protect the developing supply of resources. Furthermore, it has made it more challenging to oversee such worries attributable to variables like shared obstruction, a mind-boggling number of safety occasions and logs' information, and other comparative elements. Inside this security climate, an association ought to distinguish and characterize resources as indicated by the worth of the information and their security point of view. For successful security the executives, the association ought to then apply suitable assurance measures as indicated by the resources' security characterization. Be that as it may, there are still difficulties to be looked because of the need to deal with an enormous number of safety frameworks to protect characterized resources. This article, presents a proposition to a data classification the executives administration that depends on blockchain innovation. This helps the two presents and utilizes a model of the connection between the worth of information and the perspective of safety. In a blockchain-based disseminated record, it records exchanges including the order of resources and the administration of resources as per each class. By utilizing blockchain innovation, the proposed administration will diminish the quantity of resources that should be safeguarded and the quantity of safety arrangements that should be applied. Furthermore, the assistance will give safety efforts at the stage level as opposed to giving individual security arrangements. This recommended administration permits financial security, offers another coordinated security stage, and demonstrates the worth of the assistance with regards to the quick changing security climate that Industry 4.0 presents.
A Novel Hybrid Approach to Detect Camera Tampering

Camera Tampering gained immense interest among researchers with an increased number of cameras deployed in both public and private places by different agencies. Camera Tampering Detection can be used widely to help security officers in their scrutiny of the deployed camera in real time. In this research study we propose a 2-stage approach, in the first stage, rule-based method have been used to detect movement of the camera and in the second stage neural network-based model have been used to detect defocussing or covering of the camera. Foreground have been extracted from the background subtraction method, which is then used to determine the percentage of area covered by foreground objects. Finally, a threshold has been used to determine the movement of the camera. In neural network-based method, Convolutional Neural Network based MobilenetV2 architecture have been used to separate the blur and defocussed tampering from normal camera view. Our research study shows that combined methods provide state-of-the art performance with around 97% of F1 score and properly detect different types of camera tampering.
Categorization &amp; Classification of Acute &amp; Chronic Leukaemia using Visual Geometry Group -16 Deep Convolutional Neural Network Architecture

A significant issue in the field of disease diagnosis is the accurate differentiation of malignant leukocytes with minimal expense in the early stages of the disease. This is necessary for early detection and diagnosis of leukaemia. Although there is a lot of leukaemia, there is a dearth of flow cytometry tools, and the techniques used at laboratory diagnostic centers take a lot of time. The CNN-based architecture will be a promising solution for the same. In this, we have developed the VGG-16-based model for the detection of leukemia & achieved an accuracy of around 90%
Amount and Location Based Credit Card Fraud Detection

During the pandemic, online transactions have become a usual activity for any purchase. Use of credit card has been growing rapidly, as a result, the number of frauds regarding the credits cards has increased enormously. This includes fraudulent transactions or transfer of money from our accounts. In our paper, an evaluation of machine learning, artificial intelligence, and deep learning algorithms like CNN, Local Outlier Factor, Decision Tree, Cat Boost, XG Boost, Isolation Forest, ANN, K-Means, Naive Bayes, Ada Boosting, Bagging, Multi-Layer Perceptron, KNN, Logistic Regression, Random Forest, SVM, Gradient Boosting, Over Sampling, SMOTE, Extra tree, have been contemplated for credit card fraud detection. In the previous papers where time parameter is considered during the credit card fraud detection whereas, in our paper, we will be considering other factors like location as well as the amount for transaction done. As a result, a distinction was made among different algorithms and datasets based on the factor accuracy.
UEye: Insights on User Interface Design Using Eye Movement Visualizations

The internet has become a primary need in the present, and understanding the importance of a well-designed User Interfaces(UI) is a prerequisite. Eye-tracking can help design appropriate UI that can have a positive impact on any business. Eye Tracking is the method of knowing where a person is looking. This study presents a novel eye-tracking system that uses a high-resolution webcam to record raw eye gaze data. The system includes an initial calibration process that adapts to the user’s screen size and lighting conditions to improve tracking accuracy. After calibration, the system records eye movement data during a user’s interaction with a designated test subject. Preprocessing is applied to ensure data reliability, and fixations are calculated based on Euclidean distance, time differences, and velocity thresholds. The resulting fixations are used to generate visual heat maps and scan paths that reveal user behavior on the test subject. Further analysis, such as region-wise attraction percentages, helps identify important UI components that users focus on the most. Overall, this eye- tracking system provides valuable insights into user behavior and can inform UI design decisions to enhance user experience. The proposed system is not only cost-effective but also provides decent accuracy of 83%, making it a useful tool for designers and organization to optimize their UI components for user attention.
Deep Learning Model Based on a Transformers Network for Sentiment Analysis Using NLP in Sports Worldwide

Currently, the amount of information processed through the Internet reaches 396 exabytes per month, almost triple what was generated in 2017, and the number of users has gone from 18 billion to almost 29 billion in 2022. This information, generally extracted from unstructured sources, must be processed to obtain data of interest. Users of social networks in 2022 reached a number of 4.7 billion, increasing by 5% compared to last year. Artificial Intelligence, specifically Natural Language Processing, offers an alternative to information processing. In this sense, the model based on Transformer DistilBERT carried out based on BERT allowed the analysis of sentiments of the tweets issued on the Twitter platform related to the Qatar 2022 World Cup. For this, the Google platform located in the cloud was used, as well as Vertex IA, Storage, and BigQuery services. Another resource used was the Tweeter API to collect the data and the CRISP-DM methodology. The results showed the relationship between the variables, with a normal distribution and few outliers. To improve the work in future research, it is proposed to eliminate the outliers and tune some hyperparameters of the model.
Automated Data Adaptation for the Segmentation of Blood Vessels

In the field of image analysis used for diagnostic processes, domain shifts constitute a significant obstacle. Domain shifts lead to an incompatibility of an otherwise well-performing AI model for image segmentation. Accordingly, if two different machines image the same tissue, the model may provide better results for one of the two images depending on the similarity of the image data compared to the training data for generating the AI model. In this paper, we analyzed how the input images of a neural network have to be adapted to provide better segmentation results for images which are previously not compatible with the used model. Therefore, we developed two approaches to increase a model’s segmentation quality for a dataset with initially poor results. The first approach is based on heuristic optimization and creates a set of image processing algorithms for the data adaptation. Our algorithm selects the best combination of algorithms and generates the most suitable parameters for them regarding the resulting segmentation quality. The second approach uses an additional neural network for learning the incompatible dataset’s recoloring based on the resulting segmentation quality. Both methods increase the segmentation quality significantly without the need for changes to the segmentation model itself.