Reviewerly/exHarmony huggingface dataset:
Cloud of Things and Blockchain Integration: Architecture, Applications, and Challenges

The use of Blockchain technology is rapidly expanding. Blockchain’s autonomous, transparent, and secure properties will assist the next generation of varied industrial applications. Cloud technology and indeed the Iot Cloud from the Internet - Of - things Corporation are two examples (IoT). Here, blockchain offers novel approaches to Cloud of Things' difficulties with decentralization, privacy regulations, including traffic analysis, while Cloud of Things improves to the efficiency of blockchain operations with its flexibility and scalability. Since the BCoT model is an innovative paradigm that brings together digital currency and the Internet of Things, it has the potential to facilitate a broad variety of use cases. The purpose of this article is to introduce the BCoT from the several perspectives, including its background, justification, and integrated design, by presenting a state-ofthe-art assessment of BCoT integration. Further, we provide an in-depth evaluation of BCoT applications, discussing their potential in 5G technology and beyond, in addition to their specific use-case sectors. In order to help researchers and app creators locate and stay up with the most latest technologies in this rapidly growing market, we provide an in-depth review of the new BCoT tools and services.
Blockchain To Secure Cloud computing services

The fast progress of technology has led to a rise in recent decades in privacy and cyberattack issues. This effort focuses on job in keeping and anonymity-enhancing safe cloud computing services using a blockchain named. It is developed with two features—anonymous files and searches for illegally submitted content. On, cloud users may identify all users inside the application layer and access data using payment systems. Analysis is done on how well three different implementations pure ledger, composite block chain with a cache and a convention database—perform when it comes to obtaining data. The results show that the work with the caches beats the pure network and the conventional by 50% and 53.19 percent, respectfully.
Beauty of Blockchain Technology in Healthcare Industry

Blockchain technology has been used in the healthcare sector in a spectacular manner. Medical negligence is a worldwide problem that both developed and developing nations must deal with. Blockchain offers a safe and open system of coordinated medical management that puts patients first and enables the effective and efficient execution of national health insurance plans. This paper discussed about the 2017 National Health Policy of India. The social and technical barriers in the implementation of the scheme are examined in this paper. A Blockchain technology-based provider known as Aarogya Chain is suggested to remove these barriers.
Role of Machine Learning in Resource Usages and Security Challenges for Cloud Computing: Survey

An important topic that is currently being discussed extensively is how to integrate Machine Learning (ML) into cloud resource management and security. Now a days, cloud computing is becoming more popular. Many businesses are making investments in this area, for their own needs and to offer it as a product to others. The rise of the cloud has led to several security issues that affect both business and consumers. In this article, we present a Systematic Literature Review of the methodology and strategies used in ML and cloud security. In addition to security issues, many studies are used to make meaningful comparisons between the ML techniques used in various types of cloud resource management. In this paper, we investigate the cloud resource management issues and utilizing machine learning for securing the cloud.
Image Caption using CNN in Computer Vision

Programmatic captioning is the system of making captions or textual content primarily based totally on picture content material. This is an AI challenge that consists of each everyday speech processing (producing textual content) and PC vision (information picture content material). Program captioning is presently a completely lagging and evolving examination topic. Different new techniques are discovered little by little to obtain perfect outcomes on this area. Still, it takes a number of price to get human-equal outcomes. This studies is supposed to discover precise and present day strategies and fashions used for imaging with deep mastering in a particular manner. What strategies are carried out to apply those fashions, and what strategies provide right outcomes? To this end, we performed an green paper seek of late-level research from 2017 to 2022 from noteworthy datasets (Scopus, Web of Sciences, IEEEXplore). A overall of sixty one considerable research had been observed which can be applicable to the factors on this study. It seems that CNN is used to recognize picture content material and hit upon gadgets in images, even as RNN or LSTM is used withinside the age of languages. The maximum normally used datasets are MS COCO, utilized in all tests, and Glimmer 8k and Glint 30k. The maximum normally used grading framework is BLEU (1-4) that's utilized in all grading. It became additionally cited that LSTM with CNN beat RNN with CNN. We observed that the 2 maximum promising strategies for going for walks this version are encoder-decoders and attention tools. Combining those can power huge outcomes. This studies gives course and concept for researchers thinking about including programmed closed captioning.
Automated Nuclei Analysis from Digital Histopathology

In Histopathology analysis, an abnormal nuclear shape can be a strong parameter to detect malignancy. Similarly, by visualizing the growing amount of nuclei implies disease status such as grading of cancer. In clinical diagnosis there is a link between texture of the nucleus and disease current status. In digital pathology the most crucial & important step for computer-aided diagnostics system is automated cell nuclei segmentation. Regular studies are going on digital pathology for automatic analyzing the nuclei cell images. However, providing accurate cell nucleus segmentation rate through automation is still poor. Scientists are rigorously working on the development of computerized segmentation algorithm to provide faster diagnosis. This paper proposed a novel thresholding based unsupervised technique which is applicable to detect nuclei contours. Proposed approach is applied to seven multi organ histological tissue images. Threshold is calculated by taking into consideration the first nonzero gray level pixel value followed by finding the pixel connectivity with 8 neighborhood concept. Furthermore, the various nuclei feature including mean intensity, diameter, perimeter, centroid is computed to get better insight to cell functionality providing a support in early diagnosis to diseases. When compare with traditional adaptive thresholding this approach achieved better performance rate by 48.31 %.
Developing a comprehensive evaluation questionnaire for university FAQ administration chatbots

A chatbot is a domain-specific conversational interface that mimics human assistance for users of various systems. Recently chatbots have received much research interest in supporting university administrative operations. However, rapid and large-scale implementation of chatbots in university administration systems remains challenged. Extant literature reflects on this challenge from various perspectives including, technical, managerial and socio-technical lenses. This paper heralds a somewhat overlooked perspective namely, the processes and techniques for concise and rigorous evaluation of these chatbots. The distinctiveness of this paper lies in the tri-perspectives of anthropomorphism, usability and user experience which converge to provide a stronger lens for chatbot evaluation particularly in a university administration setting. Recent studies primarily devise heuristic methods that tend to evaluate chatbots in silos such as, user interface, usability or the conversation ability and quality. There is a noticeable lack of research that attempts combination of these seemingly complex areas of chatbot evaluation. This paper postulates improved rigour of evaluation if coverage is expanded to usability, anthropomorphism, acceptance, usage, and user interface. The aim of this paper is therefore, to design a novel evaluation instrument tailored for a university administration chatbot. This is achieved by implementing the well-known Unified Technology Acceptance and Use of Technology framework as the architectural underpinning. Constituent components of the instrument derive from recent literature and emerging trends in frequently asked questions-based chatbot evaluation. The major contribution stems from the identification and insertion of key and overlooked evaluation perspectives which culminate in a more rigorous and a more encompassing evaluation questionnaire.
Latest Developments in Smart Grid and Wireless Security Technologies Serve to Protect the Environment for Better Upcoming Generations

In addition to decrease emissions of greenhouse gases and safeguard the environment for future generations, smart grid technology is helping to transform government and industry. The next generation electricity system, known as smart lines, is primarily described as one that helps consumers and suppliers increase output and production overall. The goal of the smart grids is to increase efficiency, demonstrate two-way communication, and allow the application of dependability for future power systems. These networks may facilitate improved cooperation between alternative energy sources and renewable energy sources. The networked nature of these devices means that in addition to the previously mentioned qualities, Security issues may have an impact on the devices and disrupt the power supply. Therefore, it is crucial to adopt stronger wireless security that allows for the protection of data sent across communication networks, supports continuous power supply, and boosts dependability on widely used technology and infrastructure.That research is concentrated on outlining the key benefits of using power cities and Wi-Fi technologies to safeguard ecological issues for future generations. In order to better understand the factors that will affect the implementation of network connections and power generation, the research aims to gather information from respondents. The model for structural equations (SEM) is used to conduct the study.
Environment Quality Assessment Web Application

The full form of EQI is the environmental quality index. The three elements which are included in our study are the most precious naturally occurring elements on the Earth i.e., Soil, Water, and Air. These resources though are present in abundant quantity, the quality of these resources varies greatly over a small distance. The health of our body is dependent on these resources. The quality of all these resources must be checked before using them. The results of all these techniques help us to evaluate the quality of soil, water, and air results in single values of AQI, WQI, and SQI. These single values help in easily judging the quality of these natural resources. The quality of these resources helps in both economic and environmental aspects of life. This paper presents a detailed study on AQI, WQI, and SQI and focuses on the factors affecting these values. There is also a focus on precautions from the harmful levels of these factors and what should be done to protect ourselves.
New Validation of a Cybersecurity Model to Audit the Cybersecurity Program in a Canadian Higher Education Institution

This article presents the results of one empirical study that evaluated the validation of the CyberSecurity Audit Model (CSAM) for the second time in a different Canadian higher education institution. CSAM is utilized for conducting cybersecurity audits in medium or large organizations or a Nation State to evaluate and measure cybersecurity assurance, maturity, and cyber readiness. The authors review best practices and methodologies of global leaders in the cybersecurity assurance and audit arena, that puts in evidence the lack of universal guidelines to conduct extensive cybersecurity audits and the detection of existing weaknesses in general programs to deliver cybersecurity awareness training. The architecture of CSAM is described in central sections. CSAM has been tested, implemented, and validated in three research scenarios (1) a single cybersecurity domain audit (Awareness Education), (2) Cybersecurity audit of several domains (Governance and Strategy, Legal and compliance, Cyber Risks, Frameworks and Regulations, Incident Management, Cyber Insurance and Evolving Technologies) and (3) Cybersecurity audit of all model domains The study concludes by showing how the validation of the model allows to report significant information for future decision making that the target organization may correct cybersecurity weaknesses or to improve cybersecurity domains and controls.
Machine Learning for Data Flow Processing in Learning Process

Artificial intelligence is the set of information processing technologies aimed at conferring cognitive abilities comparable to that of humans to computers, machines or robots, through mathematical functions, algorithms but also modes of representation of the real environment. There are generally two main approaches in AI: symbolist approaches, where knowledge and reasoning are represented by a mathematical formulation or logic (with symbols) and connectionist approaches, where cognitive function is achieved by assembling formal, mathematical neurons, connected to each other in more or less complex networks (neural networks). Faced with the difficulties encountered by symbolist approaches to formalize robust reasoning in a real world with many exceptions, and also thanks to the opportunity represented by accumulated data (big data), learning cognitive functions from data becomes a promising option. This is the whole point of machine learning. In this research work, we are mainly interested in the methods of machine learning. A practical case will be discussed in this paper.
Estimation of Position of Lathe Tool Using Edge Detection Technique

Accurate positioning of tool in mechanical devices used in smart manufacturing, mechanical automation is of utmost importance. In applications like manufacturing of micro parts, an accuracy of micrometer is required. Thus, it is proposed to opt for an economic fast and accurate tool positioning system which may directly work on the images of the tool taken by a high-resolution camera. This work contributes to estimate the position of a lathe tool using image processing. The tool is considered to move in x-y plane. The movement of the tool is calculated from its origin using Euclidean distance. The coordinates of the vertices of the tool are detected using Edge detection technique. These coordinates are used to estimate the distance travelled by the tool from the origin. Prewitt and Sobel operators are applied for edge estimation and the errors obtained in both Prewitt and Sobel are compared. As a result of experimental analysis Sobel operator is found to give better results. The maximum error obtained is 272.2 µm and the minimum error obtained is 0.1 µm.
Hate Speech in Social Networks and Detection using Machine Learning Based Approaches

The use of social networking sites has increased considerably in last few years and as a result the user generated contents in the web also increased manifold. These data are mostly present in unstructured and quasi-structured formats. Many social media platforms are being affected due to the presence of hate speech. It is present in many forms such as verbal aggression and through photos which we know as memes and so on. This study considers twitter data for detecting hate speech on the internet. From the past few years, machine learning and natural language processing approaches are being used to detect hateful content on the web. This study aims to deal with the problem of hate speech detection in text data using machine learning algorithms. Feature selection for the dataset was performed before passing the dataset to the machine learning models. Different machine learning algorithms are implemented on an open-source twitter dataset. Performance of different algorithms are compared using standard performance measure metrics and presented in this paper. The experimental result shows artificial neural network outperforms the other algorithms considered in this study.
Rule-based Entity Recognition for Forensic Timeline

In digital forensics, the sequence of all events in a forensic image needs to be analyzed. Building a forensic timeline is one of the possible techniques. Naturally, the forensic timeline contains several standard entities, such as date, time, and host name. Another field provided by a forensic timeline is a message, which is a brief description of an event. To assist investigators in analyzing the incident, a more detailed identification of the entity name is needed from the message text. In this paper, rule-based entity recognition is proposed. We also discuss the advantages and disadvantages of this technique. Experimental results show that the entities in the message column have been annotated successfully.
Survey on Methodological Model of IoT in Digital Forensic

This paper provides a synopsis of research openings in IoT-based forensic investigation. Usage of IoT in digital forensic investigation process is reviewed systematically and the methodology of IoT based forensic investigation is discussed. Problems arising from IoT-linked trials during all the forensic investigation stages are brought forward. The opportunities IoT fetches to forensic analysis are mentioned. Examples of IoT forensics applications are also shown. A thorough research outline is provided, containing data about the chief research paths and a brief overview of all the methods and models relevant to the papers. Finally, future scope and server models currently being researched are brought to light in this paper.
Biometric as Secure Authentication for Virtual Reality Environment: A Systematic Literature Review

Virtual Reality (VR) is a type of technology that enables users to interact each other inside artificial environments. While this technology makes life easier, it also creates new issues, particularly in the areas of security and authentication. In contrast to authentication techniques in general, where users can use a pin or password safely and securely, in VR this security model creates a new security loophole and the better authentication method and more secure authentication in virtual reality have both been the subject of numerous studies including using biometric. In order to learn more about biometric authentication, which is the most common issue in VR authentication research, this research is conducted in a systematic literature review to answer the research questions what biometric media, HeadMounted Display (HMD), also examines what machine learning and biometric media are frequently used, and how accurate biometric authentication can identify individual person, and in the end, to help future researchers to shorten the time in conducting research or have the right authentication method for a VR service. Result of the research found that to build biometric authentication model, researcher often use k-Nearest Network and Support Vector Machine (SVM) machine learning, and finger vein and hand movement are the most accurate biometric authentication among others.
Cyberbullying Detection using Deep Learning Techniques on Bangla Facebook Comments

Bullying someone online is known as Cy-berbullying. As social media and the internet have grown in popularity, it has taken a very negative turn. People use the Internet's services to aggressively attack others while hiding behind a screen. It appears in various ways and is typically presented as text on most social networks. Cyberbullying frequently causes severe mental and physical distress, especially for women and children, and it even sometimes causes victims to attempt suicide. Because of the serious social consequences, online harassment draws attention. Therefore, researchers have begun to focus on identifying cyberbullies on social networks, particularly in the Bangla language. However, for accuracy and implementation, there is always room for improvement. This paper conducts an analysis using two deep learning algorithms, namely, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to identify cyberbullies in Facebook comments written in Bangla. A total of 8736 comments were collected, and 7072 of them were retained for the final analysis after being filtered out due to irrelevancy. In the study, we used data preprocessing steps such as text cleaning, the removal of punctuation and special characters, tok-enization, the removal of stop words, and later joining the stemmed words. Cleaned textual data was fed to the deep learning models for prediction. Our research showed that, with an accuracy of 83.55 %, the GRU over LSTM model produced the best results on the current dataset.
NTFS: Introduction and Analysis from Forensics Point of View

New Technology File System (NTFS) is a file system being used by Windows Operating Systems since 1993. NTFS is a file system used by the Windows Operating Systems for storing, cataloguing and discovering/finding files. In this work we will learn about the basics of NTFS to include its file structure and other important attributes. Then carry out analysis of NTFS file system using WinHex and Active@ Disk Editor tools from a forensics point of view, compare the results of both tools. Lastly, we carried out comparison of NTFS in Windows 10 and 11 Operating System to include forensics analysis using WinHex and Active@ Disk Editor tools.
Deep Learning-Based Transfer Learning for the Detection of Leukemia

Leukemia is typically diagnosed by using a microscope to examine blood and bone marrow smears as well as extremely challenging procedures like the Cytochemical test, which can be used to identify and categorize leukemia. However, these techniques are incredibly pricy, dependent, and time-consuming on the skill and cunning of the experts involved. In contrast to conventional methods, which have several drawbacks, this research suggests a classification model depends upon blood microscopic images for detecting leukemia by utilizing transfer learning. The suggested study demonstrates a computer-aided diagnosis method that uses Mobilenet V2 and Convolutional Neural Networks (CNNs) to identify leukemia images over healthy images. When it comes to using deep learning for image analysis and the comparison outcomes of the current study for diagnostic tasks, the implementation of Mobilenet V2 is relatively simple. Results demonstrate that the suggested model, having a general accuracy of 96.58%, a sensitivity of 95.17%, and a specificity of 98.58%, gives better predictions than separate models for classifying leukemia.