Reviewerly/exHarmony huggingface dataset:
Machine Learning Based Breast Cancer Detection Using Logistic Regression

In the medical domain, technology influence is immense, resulting in the processing of medical data electronically. Here the primary concern is security and time consumption while dealing with images. It raises the need for an automated system to retrieve and process images automatically. Medical diagnosis is a vital concept among which the diagnosis of the tedious disease has gained top attention among the research community. This paper focused on sensational breast cancer diagnosis using image processing methodologies. The most effective method of treating disease diagnosis is working with patient records in the form of mammography images. Using patient data is regarded as one of the finest input techniques for breast cancer prediction. Numerous image processing approaches were used to conduct this research and increase the image quality because determining the location and kind of breast cancers was crucial for producing an accurate outcome. The weka tool was used to evaluate this data against a trained set-in order to obtain the most accurate findings. In this work, we considered Naïve Bayes, classifiers for processing the mammogram images. The weka tool collects and processes the extracted data for further processing. The obtained results show that the performance achieved by the naïve bayes classifiers effectively works on predicting the region of breast cancer with tumors.
Single Image Dehazing Using Multi-Scale DCP-BCP Fusion

Dehazing involves finding the solution to an ill-posed problem. Improvement to the traditional prior based dehazing method has been introduced here. Considering the pivotal role of an accurate transmission map estimate in dehazed image restoration, a fusion based dehazing algorithm is proposed, where refined transmission maps from different scales are fused to obtain an overall transmission map estimate, with fewer block artifacts (owing to selection of a single patch size for a single scale model). At each scale, transmission is estimated using both weighted dark channel(DCP) and bright channel priors(BCP), effectively avoiding color distortions. Additionally, Weighted Dark channel and a Cross-Bilateral filter for refinement, helps prevent halo artifacts. The model has been compared with other sophisticated models using both ground truth and non-ground truth metrics. Experiments show that our algorithm can effectively diminish haze, while retaining the color of pure scene radiance, clearly evident in sky region. MATLAB has been used for simulation and analysis purposes.
Side Tracking in the Alimentary Supply Chain with Network Interface

In order to maintain sustainability and agricultural stability, the global world needs a complicated yet essential food supply system called a global food chain. In recent years, organisations involved in the food industry worldwide have often taken the food supply chain for given, forgetting that even a little disruption in the network might result in severe illness, shortages, or higher pricing. The most fragile individuals in society, such as the poor and small retailers and restaurants, are consistently impacted by this. The food supply chain has become broader and more complicated due to the expansion of the industry throughout the world, which prevents the old transportation model from meeting customer expectation. Food distribution chains face a number of difficulties, including poor traceability and connectivity, the availability of phoney food goods, and ineffective facility inspection. As a result, a system that provides accurate product details and a trustworthy transaction method are required. In this paper, we have outlined a complete approach to utilise Ethereum to improve the supply chain customer-centric. By providing genuine data about the products from the start, blockchain technology in the food business applies in a thoughtful and comprehensive approach to check and certify the food's quality items. This research effort includes discusses issue conceptualization, modelling, and performance monitoring.
Gesture Recognition system using Convoluted Neural Networks and Transfer Learning

Webserver is a web application designed to convert images to text, which may then be said out using the anticipated text generated by the image converter. For this application, images are captured through webcam as hand gesture inputs. In addition, Google Home and Amazon's Alexa may be used in conjunction with this software. The collected movements may be used for specific smart device functions, and the software can also call out the gesture inputs in various operating system voices (OS). Browsers as well as smart devices are able to handle these multiple OS voices. TensorFlow was used to create this HTML and JavaScript application. In order to run this JavaScript model, we need the Node.js runtime environment. Using a Machine Learning (ML) method, there is no need to install drivers or libraries in this application. Once a user has found the webpage, the software is ready for use.
Role of Cloud Management in Mitigating Vulnerabilities in Wireless Data Exchange Provider

Corporate IT architecture benefits greatly from cloud services, which is supported by virtual machine, which makes resource centralization simple and reduces cost, space, and administrative work. Nevertheless, a lot of clients are still hesitant to Due to worries about the security of sensitive and confidential data, companies are making the shift from their traditional internal IT infrastructure to a cloud storage service. The possibility of corporate data breaches and privacy losses is increased by the methods for physically locating the popularity of vulnerability assessment exploits, cloud-based virtual machine instances, and bridge assaults across virtual servers. This research offers a strategy for reducing such risks and fostering client confidence in business cloud computing. Even with well designed software systems, new flaws are found on a daily basis, and hacking methods are evolving over time. This situation makes it seem unlikely that a corporation computer processing will be completelysecure; software companies in the internet are prone to security breaches. A well-designed attack contingency plan is the practical answer to the security issues. On the plus side, if an adequate defensive mechanism is in place, cloud computing's communal infrastructure may be exploited to successfully neutralize threats. We suggest such a cloud-based threat mitigation architecture. Various software flaws in the cloud have varying degrees of severity and effects on security measures (confidentiality, integrity, and availability). We continually track and assess the risk of penetration in various security variables using the Markov model (For instance: a change in the potential to undermine the confidentiality of the data.) Our methodology would make it easier for tenants to determine their ability to deploy a dynamic mitigation approach if there is a Meanwhile to Security Flaw (MTTSF) a drastic shift in risk. This architecture adds an additional layer of safety to thedifferent cloud management, which can increase consumer confidence in business cloud services.
Research of Financial Robot Control System Based on Hopfield Neural Network

In order to improve the intelligence level of financial management, a financial service robot based on Hopfield neural network is proposed. The manipulator is one of the important components of the robot system, and the trajectory tracking control of the manipulator is the key problem for the robot to perform the follow-up work. Due to the influence of uncertain factors such as external interference, the trajectory tracking control of manipulator has poor stability, low accuracy and long time. Therefore, an improved HOPFIELD neural network trajectory tracking control method for manipulator is proposed. Using Lagrange function, this design defines the dynamic equation of manipulator system and establishes the dynamic model of manipulator. Using the term function of Newton algorithm, the system trains HOPFIELD neural network and realizes the trajectory tracking control of the manipulator. Combined with the principle of robot kinematics, HOPFIELD neural network is used to control the robot arm. Finally, the above scheme is verified by simulation. The results show that the financial service robot designed in this study can run normally, and the robot can realize the planning and control of the manipulator through the controller.
A DNN is Programmed Prediction Scholarly Accomplishment

Predicting student behavior and achievement in the present educational system is becoming more challenging. If we are able to forecast student performance in the past, it will be easier for both students and their teachers to monitor their progress and activities. Nowadays, the continuous assessment approach has been implemented by several colleges all around the world. Such technologies are helpful to students in raising their grades and performance, as well as to instructors in assessing the pupils and concentrating on those who exhibit poor performance. This assessment system's primary purpose is to assist all normal students and teachers. Artificial Neural Networks (ANN) have recently seen widespread and successful implementations in a wide range of data mining methods and applications, and are frequently far superior to other classifiers, whether they be machine learning representations and others like training algorithm, stochastic gradient descent, or minibatch. In light of educational data mining, the purpose of this article is to determine if artificial neural networks (ANN) are an effective predictive classifier to forecast students' performance using a dataset from a learning system. On this dataset of LMS, we will evaluate the performance of neural networks to that of several other classifiers in order to assess their applicability. Support Vector Machine (SVM) is one of these classifiers.
Prevention of Data Loss with SQLI

Nowadays, everyone uses web applications for the purpose of banking, e-commerce, entertainment, and so on. It bought a major change in the world. Its digitalized services made it available for the user remotely via the internet. It is also convenient for the user to access the services remotely. The majority of the services and transactions are happening with web applications, so it needs to store, process, send and receive the more sensitive data. The vulnerabilities present in the web applications allow the attacker to access the sensitive data of the legitimate users, the insecure web application allows intruders to access confidential data in an unauthorized way. The Structured Query Language Injection (SQLI) is a critical vulnerability for the web application. The existing SQLI is insecure for the sensitive data which is processed in the web applications. It allows the attacker to steal the sensitive data of the legitimate user. In this paper, we will discuss how this vulnerability is existing in the web application and also, how to avoid loss of the data even though SQLI is successfully performed on a website.
Standards and Security in Information Calling through Internet Banking

In the paper we discuss key security risks, which we feel pose danger to banking Sector and the government. In order to increase client confidence and happiness, the banking industry is constantly exploring for new platforms for service delivery. To accomplish this, the platform for delivering banking services must offer end-to-end security to protect the transmission of customer and bank information. Financial loss, data loss, and reputational and financial damage are the main causes of worry in the banking industry. We describe the survey of businesses conducted in India to determine the extent to which information security concerns are taken seriously. Different employee communication channels, historical security issues, security solutions put in place, and information security requirements are also covered. In this work, we have not discussed security issues like viruses and spam.
A Comprehensive Performance Analysis on Artificial Neural Networks

Neural Networks have dominated the sphere of Machine Learning and computerized decision trends for the past decade. The most straightforward neural architecture holds the key to some of humanity's most complex and vexing problems. When this concept of mimicking the human brain in digital or machine interpretation was first materialized in the late 1940s, the analysts were crippled by the technological reach of their time. But slowly, the advent of faster computational prowess and memory extensions paved the way for the intuitive backpropagation process in 1975, which was the first robust training procedure globally accepted. It becomes the fundamental requisite of almost all technological interactions we experience every day. Understanding the reflective activities, of an Artificial Neural Network is the first step toward more profound innovations and discoveries in machine learning. This paper specifically attempts to give an insight on various types of Neural Networks. Pros and cons of each Neural Network is summarized including their performance analysis in several application areas.
Utilization Prediction Technique and Analyze Data Mining Architecture

Data mining is the practise of extracting patterns from massive amounts of data in order to provide users with the services they desire. Data mining is mostly utilised in surveillance, fraud detection, and e-commerce. Users who want to choose their own items from a collection of data rather than viewing a vast lot of it will find that data mining is quite helpful. Data mining plays a part in avoiding the confusion caused by vast amounts of data by identifying patterns and presenting the user with the chosen data.
Smart Health Monitoring System For The Elderly

Smart health monitoring device provide improved health management and monitoring, and they also give doctors measured medical data for quicker patient diagnosis and better treatment planning. The project's goal is to create a working prototype for geriatric health monitoring using various sensors and sent alert messages to doctors and family members in the event of abnormal vital signs. The sensors used in this smart health monitoring system include a heartbeat sensor, a GSR for stress detection, a temperature sensor, an accelerometer sensor for fall detection as well as for step counting, alone with these sensor GPS Tracker, Arduino Nano, node MCV, and a display are used. Arduino receives data from the sensors and through node MCV these sensor values from the Arduino are transmitted. The sensor values are subsequently stored in the cloud by the node MCV after the data transfer. These values can also be visualized as graphs using MATLAB on the cloud platform Thing Speak. The results obtained with this monitoring device were accurate vitals measurements that were successfully coded to send out alert messages during abnormal ranges of parameters (smart monitoring), and the vitals graph was visualized on the Thing Speak platform. The device's size is larger than that of a wearable device, so it must be fitted inside a smaller enclosure for the patients' comfort in order for them to continuously monitor their health by wearing the device at all times. Nonetheless, an improved version of this work could help the patients experience comfortable monitoring and management of their health at ease.
An Analysis of Generative Methods for Multiple Image Inpainting

Image inpainting refers to the restoration of an image with missing regions in a way that is not detectable by the observer. The inpainting regions can be of any size and shape. This is an ill-posed inverse problem that does not have a unique solution. In this work, we focus on learning-based image completion methods for multiple and diverse inpainting which goal is to provide a set of distinct solutions for a given damaged image. These methods capitalize on the probabilistic nature of certain deep generative models to sample various solutions that coherently restore the missing content. Throughout the chapter, we will analyze the underlying theory and analyze the recent proposals for multiple inpainting. To investigate the pros and cons of each method, we present quantitative and qualitative comparisons, on common datasets, regarding both the quality and the diversity of the set of inpainted solutions. Our analysis allows us to identify the most successful generative strategies in both inpainting quality and inpainting diversity. This task is closely related to the learning of an accurate probability distribution of images. Depending on the dataset in use, the challenges that entail the training of such a model will be discussed through the analysis.
Compensated Convex-Based Transforms for Image Processing and Shape Interrogation

This paper reviews some recent applications of the theory of the compensated convex transforms or of the proximity hull as developed by the authors to image processing and shape interrogation with special attention given to the Hausdorff stability and multiscale properties. This paper contains also numerical experiments that demonstrate the performance of our methods compared to the state-of-art ones.
Image Reconstruction in Dynamic Inverse Problems with Temporal Models

This paper surveys variational approaches for image reconstruction in dynamic inverse problems. Emphasis is on variational methods that rely on parametrized temporal models. These are encoded here as diffeomorphic deformations with time-dependent parameters or as motion-constrained reconstructions where the motion model is given by a differential equation. The survey also includes recent developments in integrating deep learning for solving these computationally demanding variational methods. Examples are given for 2D dynamic tomography, but methods apply to general inverse problems.
Authentication-Based Hybrid Approach Using RSA for Data Security in the Cloud

Cloud computing is one of the important components of today's tech-savvy society. It creates a new paradigm for information exchange without investing in new infrastructure or licensing new software. In the last few years, cloud computing has grown tremendously from a promising business concept to the fastest-growing segment of the IT industry. At a remarkable pace, cloud computing has transformed how businesses and governments function by providing various services like IaaS, PaaS, and SaaS. Cloud computing presents another level of risk because essential services are often outsourced to a third-party vendor, which makes it harder to maintain data security and privacy. Consequently, the objective of this chapter has been divided into two sections; firstly, to identify the potential threats, and challenges and then evaluate the security of the existing cloud by identifying security requirements, and secondly, to present a viable solution to overcome these major problems. This chapter proposes a hybrid approach using RSA for data security within a cloud environment.
The QEEG Research as an Example of an Alternative Method of Evaluation of Didactic Activity – Popularization of the Concept and Effects of Own Research

The article presents the concept of applying QEEG research in the assessment of didactic activity in polytechnic education. In addition, the promotion of EEG research in the field of therapeutic activities and as an innovative research method in didactics will be shown.
A Person Re-Identification Method with Multi-Scale and Multi-Feature Fusion

In order to solve the problem of insufficient expression of pedestrian features and ignoring the semantic relevance of adjacent areas in person re-identification, a multi-scale and multi-feature fusion person re-identification model is proposed. Firstly, feature maps containing different information are obtained by multi-branch structure. Secondly, the semantic correlation between local features is emphasized by combining adjacent local regions. Finally, combining the advantages of maximum pooling and average pooling, the model can learn more comprehensive feature information from different directions. Experiments are carried out on Market-1501, DukeMTMC-reID and MSMT17 respectively, it can be evaluated that the model can effectively extract pedestrian feature and has high recognition accuracy under different lighting and camera angles.
THE ROLE OF EMOTIONAL TRIGGERS IN SCREEN CULTURE

The relevance of the research is connected with the need to explain the mechanisms and strategies of the screen's influence on modern man. The problem of emotional perception of screen culture by a person through the prism of "emotional triggers" is considered, some of their types are analyzed and examples of embedding in screen culture are given. A certain vision of the role of emotional triggers is also formulated and the prospects for further development of screen culture in which they will play a key role are described. Emotional triggers contribute to maintaining the process of endless consumption of mass culture products. The maintenance of this process occurs due to the formation of a sense of emotional closeness and deep, in the opinion of the viewer, emotional contact with the products and phenomena of modern mass culture. Triggers become a form of representation of reality in the consciousness of the subject, creating instead of real reality a new form of its representation filled with visualized images and phantoms, which brings a certain emotional load into the viewer's consciousness. In this sense, screen culture can be considered as a global tool that exerts an artificial influence on both the individual viewer and the public consciousness as a whole. Emotional triggers, influencing the thoughts and behavior of the viewer, form secondary ways of describing the world for him, set the coordinates of orientation in the world of screen culture, simulating the immediacy of personal experiences and feelings, creating the illusion of belonging, integrity to a certain informational whole, thus building their own identity. The role, or rather the power of the impact of emotional triggers in modern screen culture is due to the fact that they are always visualized, and emotions most vividly affect it through visual images.
Survey on the Research Progress and Development Trend of Vision-and-Language Navigation

Vision-and-language navigation is a newly emerging research topic developing rapidly in recent years, and it is one of the representative research tasks in the frontier field of vision-language interaction. The goal of this task is to realize autonomous navigation based on visual perception of environment according to language instructions given by human. This paper reviews the recent progress in vision-and-language navigation. Firstly, the research content of this task is introduced, and the three main problems and challenges of cross-modal semantic alignments, semantic understanding and reasoning, and generalization ability enhancement are analyzed. Secondly, commonly-used datasets and evaluation metrics are listed. Thirdly, the research progress of this task is summarized from four aspects of imitation learning, reinforcement learning, self-supervised learning and other methods, and the effects of the typical solutions are carefully compared and analyzed. Fourthly, the current research trends of this task are discussed, which mainly include continuous environment navigation, advanced complex instruction comprehension and common sense reasoning. Finally, the future development directions such as 3D visual-and-language navigation, embodied question answering and interactive question answering are further discussed and prospected.