Reviewerly/exHarmony huggingface dataset:
A Formal Language for Actors’ Interactions

The gap between the design and implementation of software for IoT security and forensics are often ambiguous and loose. Formal languages in the class of process algebra based on mathematical rules, logic and symbols, as a mean of expressing system specifications, proved to be valuable to systems’ design and verifications. There are several semi-formal and formal languages as such JSON, XML, predicate logic, and regular expressions and formal model of computations such as Turing machines etc. Each dedicated to specific areas. This paper introduces a new formal language, called ATFL (A Transaction Formal Language), developed initially for IoT forensics and authentications. The specifications of two cases, an IoT device login and an SDN wireless controller, are presented and verified, to expose the propensity and the generalization of the proposed formal language.
High Performance Cervical Cancer Detection Using Gradient Boosting Algorithm and Bayesian Optimization

Early treatment, a higher survival rate, fewer fatalities, and a higher standard of living for individuals who have been diagnosed with the disease are all benefits of cervical cancer screening. It is an important field for study and development because precancerous lesion treatment can help stop the progression of the cancer and stop the onset of cervical cancer. Based on the dataset, this paper provides a method for detecting cervical cancer using a gradient boosting technique. Selected characteristics and preprocessed data were used to train the model. Bayesian optimization was used for hyperparameter tuning to determine the ideal hyperparameter values. Using evaluation criteria such as precision, accuracy, F1-score, recall and AUC-ROC the effectiveness of the model was assessed. The findings demonstrate that the suggested method performed well in detecting cervical cancer, with precision, accuracy, F1-score and recall all exceeding 88%. In addition, the area under the ROC curve was 92% (AUC-ROC). The results show that utilizing the gradient boosting algorithm in conjunction with Bayesian optimization for hyperparameter tweaking on the dataset can be a successful method for detecting cervical cancer. fostering community, dialogue, and outcomes in the fields of leadership development, mentoring, entrepreneurship, and social good in order to support the professional achievement of South Asian women.
Blockchain Exploratory Analysis in Strategic Management

With the use of consensus mechanism, cryptocurrency, and accurate asset tracking, block chain technology, is a peer regulated framework. The current methods of doing business in the construction industry might be drastically altered by adopting a distributed database system. The construction sector has frequently been criticized for its delayed adoption of new technology through its supply networks in a way that is not efficient. Widespread fragmentation, outdated supply chain, harmful competitive pressure, a lack of collaboration and accountability, slim profit margins, and a dearth of human assets are all often mentioned as major contributors. Through an explorative approach, this paper examines ways to get construction supply chains ready for blockchain technology as it gains traction in many other sectors, including finance and banking. Focus groups and moderately discussions with 17 subject specialists helped gather the study's empirical data. The study also provides a SWOT (strengths, weaknesses, opportunities, and threats) analysis and outlines the prerequisites and processes toward a Blockchain-enabled building supply structure.
HVAC Attack Detection Using Novel Machine Learning Model

HVAC systems are heating, ventilation, and air conditioning units that allow for maintaining indoor air conditions to a desired degree. Other than maintaining occupant comfort, its use in industrial applications and critical infrastructure is vital to maintain operations since many systems and components must operate under controlled environments, such as, to avoid overheating. HVAC systems by design do not incorporate security. Given this, most HVAC systems , especially those connected to the internet are vulnerable to cyber attacks from various angles. Such an example was the Stuxnet virus which sabotaged a nuclear plant in 2010. Due to this and among other events, HVAC security is now under the spotlight, and the need for a solid security model for HVAC security is necessary. In this paper, we present an anomaly-based detection machine learning model to detect malicious sabotage of HVAC systems which achieves an accuracy of 99% and recall of 98.2%.
The Most Efficient and Accurate Face Mask Detection in Crowded Area using Machine Learning Algorithm

Since 2019, the COVID-19 epidemic has been rapidly spreading throughout the world. The complexity and volubility of human existence have increased as a result of the epidemic. As a consequence of this infection, many individuals have perished. One of the elements promoting the spread of the Covid-19 virus is the absence of antiviral medications. This illness is spreading swiftly and readily because of a number of universal human flaws, including the way sick people breathe, cough, and sneeze. The primary symptom is the typical flu. Therefore, the greatest defence against this sickness in the current situation is a face mask that totally covers the mouth and the nose. It still causes damage to several people and organisations in 2022. While the world heals from the virus and prepares to resume regular operations, everyone feels tension, particularly those who will continue to communicate in person. According to studies, having insurance not only provides a means of confidence but also greatly reduces the probability of an irresistible expert action. The solution to solving this issue and minimising this downside lies in this effort and innovation. It provides a broad, a system built around deep learning for spotting situations were face masks were not worn as planned. We are shielded as from symptoms of many ailments by the mask. Given the epidemic concern, gloves now are widely accepted to be necessary. The Covid virus spreads to new persons because there is a dearth of understanding about face masks as a result of the quick decline in Covid cases. Despite the fact that fewer occurrences occurred as a result of the efforts of many, people began to disregard the face masks. To combat the Corona disease, various simple tools will be required. One of the most basic is the face mask. Although nobody was required to cover their face at first, experts and medical professionals now recommend that everyone do so.They may now be used by the general public for a number of purposes, including at airports, hospitals, businesses, and educational institutions. Airport personnel may use this structure to decide the fact that a traveller is concealing their face. In order to recognise mask, they are building a framework. Due to women's disrespect for safety protocols and helmets, the third industrial revolution will start.The Covid retrovirus "Omicron" has just developed a brand-new variant. This technique, for instance, may be used to identify someone who is not wearing a mask using a camera or security system. Our understanding of the operation of older face mask detection systems is aided by the most current study.
Vaxina: Decentralized Vaccination Tracking System

With the epidemic of Covid-19, a realisation has come into effect that the healthcare industry requires a more efficient method of storing non-tamperable vaccination certificates as well as sharing them with the relevant parties when required. As such, the need for efficient and secure vaccination tracking has led to the development of decentralized solutions. This paper aims to bring forth a Ethereum platform-based vaccination blockchain certificate system which supports authenticated consensual vaccination certificate sharing through the use of solidity smart contracts. We analyse the system architecture, implementation and evaluation, which will support the system's ability to resolve some of the security issues associated with centralised database systems such as low availability, integrity of the vaccination information as well as confidentiality and privacy of patient medical records. Vaxina system is a real-world implementation of a decentralized vaccination tracking system based on the Ethereum platform. It focuses more on the practical implementation and addresses some of the limitations of the previous proposed systems. It has been implemented and tested on the Ethereum blockchain and is available on GitHub for public use. The paper provides valuable insights for future work in the field of decentralized healthcare solutions.
Industry 4.0 Networks' Federated Blockchain Learning Framework For Cognitive Computing

Cognitive computing is a ground-breaking notion in AI which replicates the thought pattern of the human mind. As a result, Industry 4.0 robotics is slowly flourishing owing to cognitive computing. The development of a variety of Machine learning and artificial intelligence techniques has already made it possible to see the beginning of the march toward improved decision-making and data-driven intelligent manufacturing. However, there are a number of additional issues that need to be addressed, such as issues with performance, limitations of data resources, and attacks including poisoning. Recent research efforts have just scratched the surface of the problem, which often leads to erratic performance, inefficiency, and the disclosure of private information. We built a decentralized paradigm for massive data-driven cognitive computing (D2C) in this research by mixing blockchain technology with federated learning. D2C stands for data-driven cognitive computing. The problem of a "data island" may be solved by federated learning if privacy is protected and efficient processing is carried out, while blockchain technology gives incentives in the form of a potent anti-poisoning system in a truly decentralized method. Convergence may be sped up with the use of blockchain technology enabled federated learning by taking advantage of better verifications and member choices. The results of a comprehensive review and assessment demonstrate that D2C is more effective than the designs and models that are now the industry standard.
Exploring The Use of Blockchain Technology in Cybersecurity and Data Science

Blockchain technology has demonstrated to be extremely efficient at processing distributed transactions in a secured manner. It consists of a wide range of applications. B. To handle smart contacts and Bitcoin cash. Blockchain systems may allow automatic data exchange and thinking about, resulting in increased effectiveness and lower costs. This is made possible by the adoption of contract technology and decentralized ledgers. Additionally, blockchain technologies can enhance integrity and security of data, enabling more precise and reliable analysis of information. Ethereum along with information science integration may make it easier to create decentralized apps, opening up novel industries and income sources. However, there are still issues with adaptability, interconnection, and complexity in the combination of blockchain technology and data science. For this type of technology to achieve all of its potential advantages, it is imperative that studies and advancements in this field persist. Blockchain applications for data science are currently being investigated. This essay examines how blockchain technology is used in cybersecurity and data science.
Integration of Blockchain Technology in Health Care System: An Intensive Review

This study paper will investigate the effectiveness of the block chain system to give more data. Below, it will be shown how to synchronise a dataset's information based on a number of patient tiers. This research attempts to provide a brief summary of the technology known as block chain and how it affects a comprehensive review of the relevant literature. The benefits and drawbacks of the block chain method will be discussed in the context of care sector to help apply its core principles. Problems in medical institutions may be prevented by applying block chain innovation to comprehend accomplished features. Medical policy processes have been observed to benefit from blockchain - based. The review of literature portion will provide an overview of the overall health system as well as its functions. A thorough assessment of group papers and other materials revealed that the use of block chain technologies is considered to be a workable goal in the healthcare market. Tracking and visibility are apparently some of the major objectives for chain technologies in the medical sector. This article will provide invaluable information on the issue that will change how people think about cryptocurrency and technology in general. In order to point readers to good information, the comparative distinctiveness of this topic will be assessed further. A survey will be conducted to support the methodology used for this research study.
Estimation of Face Attributes Using Standard CNN Features

A difficult machine vision challenge is predicting qualities from real-world facial photos. Traditionally, 3 technological blocks—face location, facial description creation, and attribute classification—must be cascaded in a process in order to automatically characterize face attributes from images that include faces. The variety of the definable face characteristics being acknowledged, our facial descriptor, which are built from several layers of the CNNs for various characteristics in recognition of the diversity of the describable face qualities, are designed to make face attribute prediction as simple as possible. In-depth comparisons between our method and the state-of-the-art are demonstrated through experiments on the two significant datasets LFWA and CelebA. Our research not only presents a successful method for predicting a facial characteristic, but it also raises a crucial issue of how to best apply the capabilities of pre-built CNN models to new applications
Recommender System for Predicting Students' Academic performance in association with Cognitive state and Affective state using Sentiment Analysis and Association Rule Mining on the closed ended questionnaire

The recommender systems (RS) are significant in academics, business, and industry. They are frequently employed in various fields, including shopping, music, movies, travel, dining, and writing. Recently, RS can be used in education to suggest student learning styles. This paper proposes a recommender system for predicting student personality with emotions. One of the common recommender system methodologies, collaborative filtering, generates the best suggestions by finding related individuals or things based on their prior transactions. One of the main issues with the collaborative filtering process is the poor accuracy of ideas. This paper uses association rule mining to recommend student personality with emotion based on closed-ended questionnaires. This work initially uses the sentiment analysis technique to identify the student's emotions based on the answer provided for a closed-ended questionnaire. Then, polarity-based sentiment analysis is used to classify student emotions. This paper uses the Association rule mining concept to predict student personality with emotion. This is the first study of a recommender system for the student based on closed-ended questionnaires. The real- world closed-ended questionnaire like Emotional intelligence, Eysenck personality, Self-determination scale, Self-efficacy, Rosenberg's self-esteem, Positive and Negative affect schedule, and Oxford Happiness is used to evaluate the performance of the proposed research work.
Real Time Car Speed Detection Using CNN Algorithm

In addition to determining car speed, the method can be used to track traffic patterns along a road or motorway. The speed of the car is rarely determined with the security cameras already in use. The majority of the currently used methods require ongoing maintenance and employ expensive sensors. As a result, we suggest a technique for using security cameras to monitor traffic and estimate car speed. To detect a car, pinpoint its location, determine the distance it has travelled over time, and ultimately determine its speed, various image processing techniques are applied. The proposed method's ability to predict speed even in videos with low resolutions or quality will be evaluated. The traffic surveillance system (TSS) is a crucial instrument for gathering important data (speed). Convolution neural networks (CNNs) with a single pipeline have been the focus of a significant amount of research up to this point. Even though this research produced highly accurate results, they needed a sizable dataset and an implementation on specialized hardware.
Movement analysis of Individuals Using ML for different window size on raw accelerometer Time series Data

Individual movement analysis is becoming more open and common as sensors incorporated in mobile devices and machine learning algorithms progress. In the proposed framework, different classification techniques of machine learning algorithms such as Logistic Classifier and Support vector machine (SVM) are used to recognize human activities. The Fast Fourier transform (FFT) of tri-axial accelerometers that measure acceleration in all three spatial dimensions of smartphones is considered with different window sizes which correspond to the different number of samples from the sensor to predict the activities of an individual. The Performance analysis of individual classification for different window sizes is evaluated using accuracy, recall, precision, and F1-score. Results show that SVM performed better when compared to other model but there is a significant increase in accuracy as the window size increase for both the ML classification techniques.
A Proper Inter Neural Perceptron has Been Applied for Meaningful Mono Video and Images Remarkably

In terms pf reconstructions reliability and computing cost for single picture ultra, a number of fully convolutional network-based solutions have recently seen phenomenal progress. And to use a continuous filter, often excel formulas, the low resolutions (LR) incoming picture is increased in comparison to the spatial fidelity (HR) spaces in these techniques before reconstitution. In other words, the ultra (SR) procedure takes place in Clinical arena. We show that this increases computing overhead and is not the best solution. The first convolutional network (CNN) capable of carrying out legitimate SR of 720p movies on a solitary K2 GPU is shown in this study. In order to do this, we suggest an unique Dcnn in which the image data are derived in the LR area. In order to upmarket the finished LR region proposals into the Hc outcome, we also develop an effective inter cnn model that applies a variety of lower resolution filters. This essentially replaces the manually created adaptive mesh screen in the SR route with more sophisticated upscaling filters that are specially learned for each dataset, while simultaneously lowering the computational time of the whole SR procedure. We put the new system using videos and photos from freely searchable databases, and the results demonstrate that it fares notably better (+0.15dB on Pics and +0.39dB on Movies) and is a factor of ten times quicker than earlier Clinton news network techniques.
Enhancing IoT Intrusion Detection System Performance with the Diversity Measure as a Novel Drift Detection Method

The emergence of the Internet of Things (IoT) has revolutionized various sectors, such as healthcare, intelligent homes, agriculture, transportation, and manufacturing. Nevertheless, the rapid growth of IoT networks has introduced new security challenges, making them susceptible to a variety of attacks. In response, machine learning-driven intrusion detection approaches have been developed, which analyze IoT devices' behavior and communication patterns to detect and counteract suspicious activities. While these approaches exhibit high accuracy and low false alarm rates in static contexts, their performance stability in dynamic, evolving environments is yet to be determined. Model drift, the decline in a machine learning model's predictive capacity over time, is a crucial issue that can considerably affect machine learning-based intrusion detection systems if not identified and addressed promptly. Our work presents an innovative IoT Intrusion Detection System that incorporates the Diversity measure as a drift detection method to address model drift in IoT networks. The proposed concept can detect unknown attacks in IoT networks by employing a cutting-edge drift detection technique.
Cyber Security Development and Critical Evaluation About Current Barriers and Opportunities

The majority of today's economic, commercial, cultural, social, and government-related activity and contacts between nations, at all levels (individuals, NGOs, governments, and governmental institutions), take place in cyberspace. Cyber-attacks and the risks associated with wireless communication technology are a growing threat for businesses and governments throughout the globe. The modern world relies heavily on electronic devices, making cyber security a pressing concern. Cybercriminals target businesses in order to steal money. The motivations for cyber-attacks don't always lie in the realm of politics or preventing civilian harm. Infections on personal computers, leaks of sensitive information, disruptions in the delivery of data, and other forms of attack are all potential causes of financial and operational loss. To that goal, many different kinds of measures are used by many different kinds of organizations to avert the disastrous effects of cyber-attacks. Internet safety now relies on constantly updated data from the IT world. Researchers from all around the globe have offered several strategies for preventing or mitigating the effects of cyber assaults. Not all of the techniques are now in use, however; others are still in the testing or research stages. This research aims to analyze the obstacles, flaws, and benefits of the offered approaches while also providing a survey and complete analysis of the standard developments provided in the area of cyber security. An array of novel descendant assaults is carefully studied. The evolution of security measures in the digital realm is traced, and conventional security architectures from the early days of the field are examined. Cyber security, security risks, and issues, as well as new trends, are also discussed. Researchers in the fields of information technology and cyber security might perhaps benefit from the offered thorough review study.