Reviewerly/exHarmony huggingface dataset:
Blockchain Usages In Hospitality Management

The bulk of digital payments in the era of Industry 4.0 are done through programmes that are supported by gateways of different payments. Thanks to these diverse payment channels, attackers can engage in destructive activities including hacking into e-wallet accounts, and assaults on payment clearance cycles. For tourism, economic data is kept on a centralised cloud server, which might lead to payment problems during times of high traffic. The issues mentioned above can be resolved by using a decentralised approach like Block-Chain, which enables trust and reputation management across many stakeholders including banks, travel agencies, airports, railroads, cruise ships, hotels, restaurants, and local taxis. In response to the aforementioned discussion, a framework called BeSHMT (Block-Chain Enabled Smart Hospitality Management and Tourism) has been proposed. It enables visitors to interact with key parties through a mono-wallet identification number associated with a Crypto-Currency server to undertake payments. BeSHMT employs an immutable ledger so that travelers can travel without having to worry about producing proofs. The BeSHMT framework also includes a deep-learning enabled by tourism (DLeT) framework that is taught using data from past visitors. It offers potential tourists’ ratings for the sites that have recently been visited by other travelers. Finally, we show through case studies that BeSHMT outperforms conventional frameworks in the tourism sector in terms of Return on Investment (ROI).
Detecting phishing attacks using Convolutional Neural Network and LSTM

Cybersecurity threats are increasing immensely due to the evolution of digitization. Due to this evolution, many security threats are happening around the world. One serious threat is phishing. It is a fraudulent activity that aims to steal a victim's private data using fraudulent emails and websites. Sensitive data such as login information, passwords, and bank account details are obtained and misused. In these types of attacks, the attackers appear to be legitimate. It is very hard to differentiate between what is real and phishing. A lot of research has been carried out to classify URLs as legitimate or related to phishing. However, a more efficient detection approach is needed to accurately predict and distinguish genuine sites from phishing sites. This issue can be solved by using deep learning algorithms such as convolutional neural networks (CNN) and long-term short-term memory (LSTM). This paper proposes a hybrid (CNN-LSTM) approach that provides maximum accuracy.
One-Axis Scheduling Solar Follower Optimization for Various Weather Conditions

The primary goal of solar power production is to increase solar panels performance. Sun monitoring technology is just one of the techniques. A perfect alignment to the Sun is one of the tracking systems' most crucial characteristics. The performances of individual solar monitors built on scheduled and light independent resistors light sensor, in addition to a static photovoltaic system, were examined in this study under varied weather situations. In addition to comparing how a produced scheduled solar trackers as well as an LDR solar monitor worked in various weather scenarios, a straightforward approach for figuring out a solar finder rotation angle depending on the encoding was put forth. Eventually, the constructed sun tracker' efficiency was measured
Facial Feature Estimation with CNN Classifier

Because of the differences in stance and lighting in the actual world, it is extremely difficult to identify facial characteristics from faces in the wild. In order to handle these difficulties and challenges, the solution to this challenge is to construct appropriate feature representation. Convolutional neural networks (CNNs) have proved successful at classifying images, hence this issue has seen extensive use of the rising CNN feature as a logical and sensible solution. Instead of using high-level CNN features for attribute prediction in this research, we instead take a look at mid-level CNN features. It is based on the realization that different facial qualities exist, some of which are local in character whereas others are global in nature. According to our research, mid-level deep representation exceeds high-level abstractions in terms of accuracy in prediction. Just on CelebA and LFWA dataset, we show that the mid-level models attain cutting-edge predictive accuracy. Our research also demonstrates that face identification and attribute predictions may both be accomplished using a single neural cnn by exploiting the mid-level representations.
Critical monitoring of the crops and conditions though IoT for agricultural systems

A real time management system is the major part of well-designed IoT (Internet of Things). Under IoT methods, wireless networks accomplish energy usage through power management. Sensing nodes, constructed on sensor networks, deliver effectual energy management meant for innovative connections. To encourage smart agricultural systems over conventional methods, it is necessary to execute IoT and other technologies. In a survey of execution and benefits of IoT, mainly three questions have been asked form 70 people. Whose analysis has been presented in the research. After analysis simple IoT system can be affordable but after including extra features increase the cost. Also, in villages there is a problem of poor internet connectivity that leads to lack of transfer of data from farmers and agricultural analysts.
Moving toward an intelligent edge: Machine Learning and Wireless Communication

In order to improve performance, efficiency, and yield, the intelligent wireless system concentrates on collaborating with cutting-edge techniques such as machine learning (ML) and similar methodologies. The research includes evaluating precisely the important factors that ML methodologies use to assist more intelligent network communication in many sectors. For the research, the researchers tried to collect both primary and secondary data. In order to have greater input and perspectives into the research field, the participants are recruited from the business. The main aspects taken into consideration for the research are spectrum availability and control, important congestion point detection, ML-influenced hotspot management, and control of hotspots. According to the research, which was conducted using the SPSS data analysis tool, it can be concluded that all of the variables have a significant impact on intelligent communication. As a result, ML contributes significantly to improving the user experience.
Addressing Network Security Concerns

Safety is the absence of possible environmental sources' damage. Here, we're focusing on cybersecurity, which guards against external enemies known as attacks. More individuals are connecting to the Internet, and around half of globe may view your router, networks, including smart phones without even any security measures. When there is no protection, your info is publicly accessible and anybody may view it. IPsec prevents any attacks and gives information about objects attempting to connect to your connection and if they have changed.
Convolution Neural Networks for Clinical Image Segmentation with Evolutionary Compression

Convolutional Neural Networks (CNN) have been very useful for image-related applications as a result of their high level of accuracy and their ability to analyze data in real time. Neural networks have a variety of potential applications, some of which include the classification of pictures, the fragmentation of images, and the identification of objects in photographs. Images are often used in the medical industry for the purposes of diagnosis as well as surgery. In this article, a study on the use of convolutional neural networks (CNNs) for clinical picture segmentation is presented, coupled with an approach for evolutionary model compression. On a collection of clinical pictures, four distinct models—UNet, ENet, ECO-UNet, and ECO-ENet—were assessed. The accuracy of their segmentation was gauged using the Dice coefficient, sensitivity, and specificity. The findings indicate that although the UNet and ENet models had high segmentation accuracy, the ECO-UNet and ECO-ENet models had even greater accuracy. The model size was subsequently decreased using the evolutionary compression technique, and even at high compression ratios, the compressed models' performance was comparable to that of the original models. These findings show how successful the evolutionary compression approach is in reducing model size as well as the potential of CNNs for clinical picture segmentation tasks. In environments with limited resources, this strategy may be very helpful. Overall, the research demonstrates that evolutionary compression and CNNs provide a potential method for clinical picture segmentation..
Text Analytics in Modern Healthcare using NLP

The healthcare industry is facing a major shift in how patient care is administered. Data-driven decisions are becoming increasingly important to reduce costs and improve patient outcomes. Text analytics is one of the tools being used to help make sense of unstructured data, such as medical notes and records. This paper provides an overview of sources of health data, current work that employs text analytics and the challenges of text analytics. Text analytics has the potential to revolutionize the way healthcare is administered. It can provide a better understanding of patient health, yield more accurate diagnoses and treatments, and ultimately, improve patient results. The use of text analytics can also improve the accuracy of patient records for data-driven decisions and reduce costs. However, there are many challenges associated with the implementation of text analytics in healthcare. These include issues related to data privacy, accuracy, and data collection. The implications of using text analytics in modern healthcare are far-reaching. With the right implementation and data-driven decisions, the healthcare industry can improve the treatment experience and reduce costs. Text analytics has the potential to provide meaningful insights into patient health, yield more accurate diagnoses and treatments, and ultimately revolutionize the way healthcare is delivered.
A Survey of Dynamic Exploration Symmetric Encryption with Backward &amp; Forward Secrecy

A sophisticated type of symmetric encryption is called Multiuser Dynamic Searchable Symmetric Encryption. It expands the capabilities of conventional symmetric encryption to enable actions such as adding and removing encrypted data as well as the ability for a verified set of information users to access their preferred encrypted data within the dynamic database. The latter can use using erased data and search results to comprehend the content, while the former allows attackers to discover by performing file injection attacks, keywords from the injected data were exposed. can do. To our knowledge, the available literature has not completely addressed these privacy problems for M-DSSE. In light of this, our attention is directed toward multiuser dynamic searchable symmetric encryption that satisfies the requirements for forwarding and backward security. It uses Inverted indexes are created and updated using random number generator Functions (RNG) and Authenticator primitives search tokens to suggest specific solutions. In the accidental model, the suggested approach is shown to be secure. Additionally, the performance study demonstrates that the suggested approach provides increased security assurances at a fair cost to efficiency.
Retooling Non-IID Metadata at IoT Endpoint with Automated Mentor Training for Algorithms

The infiltration of decentralized ml algorithms, such as supervised learning, has been made possible by the increment in computer resources in endpoints. This technology allows for the creation of result obtained that can be trained regionally in the endpoints, trying to improve both utilization and secrecy for model prediction instruction. Although machine learning needs a primary key to coordinate the training of the model, in certain IoT networks the communication across gadgets and transmission lines might be restricted. This hinders the implementation of deep learning. Mentor training, that does not necessitate such an integrator, seems to be a viable way around problem. The security issues with IoT installations, on the other hand, have encouraged the usage of computer science for threat and fault diagnosis. When using supervised methods to solve these issues, the trained samples are often unbalanced, meaning that there are more harmless pieces of information than anomalous. This necessitates the employment of rebalance strategies to demonstrate the effectiveness of the methods. In this research, we present a new mentoring technique, P2PK-SMOTE, which result in increasing to locally s actually the training examples by synthetic production of datasets from either the minority class, to train unsupervised abnormality identification models for machine learning in non-IID contexts. In order to lower the danger of data and improve results in semi contexts, we additionally integrate a method for exchanging a small portion of synthetic data first from minority class between devices. The advantages of our suggested technique are validated by our practices require in actual dataset and IoT image classification across a variety of situations.
IoT Implementation in Various Applications: A Detailed Review of Cyber Security Issues and Challenges

Data must be protected against cybercrime as the network revolution grows and becomes more complicated. With the purpose to intentionally damage sensitive and secret information, cybercrimes cost the world economy billions of dollars. Because these crimes are perpetrated often, it is vital to increase cyberspace security to lessen and perhaps even prevent the effects of cybercrime. The internet of things (IoT) phenomena is now the subject of study, as privacy and safety are acknowledged as the primary concerns for Iot., particularly in light of the fact that they are being used in crucial contexts like healthcare systems. The current state of safety in the IoT sector is examined in this paper, along with security-related problems. We look at particular security needs and methods for resolving these problems. IoT safety is being recognised as a problem that blockchains may help with.
Predictive Design for Quality Assessment Employing Cloud Computing And Machine Learning

In this work, we investigate a unique effective framework for projected design inspections in industrial manufacturing combining machine learning methodologies and edges cloud computing technologies. We recommend a thorough approach that involves targeted data gathering and broadcast, forecasting and supplying appropriate, as well as innovation with the current IT plant infrastructure, in contrary to state-of-the-art contributions. To highlight the steps and advantages of the suggested solution, a genuine business use case in the manufacture of SMT is described. The outcomes demonstrate that the proposed strategy can dramatically reduce inspection volumes, leading to economic gains. A key achievement basis for the drawn-out presentation of assembling ventures is the creation of imperfection-free, top-notch items. Indeed, even with the intricacy and assortment of items and the requirement for savvy production, an exhaustive and dependable quality investigation is habitually required. Along these lines, high examination volumes cause producing bottlenecks in the review processes. Thus, the purpose of this paper is to develop a predictive design for quality assessment regarding employing cloud computing and machine learning. Concerning this particular research, a descriptive research design has been used by depending on secondary data due to its reliability and validity. Moreover, it has been found that both cloud computing and machine learning contribute effectively for employing quality assessment because of their innovative features and infrastructure.
Role of Machine Learning for Solving Satisfiability Problems and its Applications in Cryptanalysis

The efficiency of "conflict-based Clause-Learning Boolean Fulfilment (CDCLSAT)" solvers on engineering problems from several fields has been seeing notable modifications during the last 20 years. Various analyses have proposed SAT-based tactics for cryptanalysis, like finding conflicts in hash capabilities and disassembling symmetrical encryption schemes, as just a result of the availability of efficient broad sense look solvers like SAT Solver. The majority of the currently suggested SAT-based cryptanalysis techniques are Black Box inventions, in which the cryptanalysis problem is encrypted as SAT and illuminated using CDCLSAT Solvers. The method has the issue that now the encoding created in this method may be too extensive for any slashing arrangements to decode successfully. This approach's solution is not manner set up to handle a specific or provided example, which is a further notable drawback. The disadvantage of this technique is that decoding created in this manner may be too extensive for any sophisticated organization to correctly decode. One notable drawback of this strategy that the solution does not way set up to deal with a specific or given scenario. In order to understand these problems, SAT Solutions have provided solutions to two related existing gaps (exhibited in Fast Programme Encrypted 2009) about the presence or absence of these minute secrets. SAT nameservers have provided an effective solution, even though that such problems might appear difficult to address. SAT nameservers have provided a useful plan of action, despite the fact that such problems it might seem difficult to fix and also discuss the experiment that was conducted to see how well an intermediate SAT Solution performed when doing encrypted cryptanalytic tasks that proved to be relatively challenging. Allocation and or before assaults here on MD4 computation are far less similar.
A Study on the Potential Role of Blockchain in Future Wireless Mobile Networks

The "Internet of Things" or IoT has recently transformed the established sector into smart infrastructure with 6G data-driven design. Because of their decentralization, straightforwardness, absence of range assets, intrinsic protection and security, absence of interoperability, mystery, and prospering shrewd application regions like IoT Innovation and Industry 4.0, blockchains (BCT) has drawn in a ton of consideration. These facts served as the impetus for this paper's extensive survey, which focused on the potential benefits and difficulties of integrating blockchain technology into 6G cell networks, Industrial iot, and smart industries. These issues included power grid sharing, mathematical loads, response time, transmission capacity above, plans of action, maintainability goals, and edge insight. Specialists accentuated the combination of blockchain and IoT to empower knowledge dispersion in modern IoT later on, as well as the innovation model of 6G to understand the effective execution of BCT plans. This paper talked about the interesting issues that are now being faced, mitigating strategies, and potential future research directions that could aid in the realisation of this vision.
A Detailed Investigation on Various Cyber Security Models and their Comparisons with the Detailed Performance Analysis for the Application of IIOT Systems

Data security & Cyber security are sometimes used indiscriminately, however the first emphasizes that the exact point does have the potential to be a victim, whereas the second one addresses the function of the individual in the security process. Nevertheless, because it emphasizes the ethical component of civilization holistically, this debate of cyber security holds significant implications. Different architectures & designs have been proposed to handle the problem of cyber security. Additionally, it covers the ideas behind cyber security, including its organizational structure, human resources, and advice on how to safeguard private data on computers. This essay examines such designs, their shortcomings, as well as the prior methods employed to lessen these dangers. Additionally, the paper makes suggestions for additional study.
Convolutional Neural Network-Based Anomaly Detection for Cyber Security: A Survey

There are many security risks as computer and communication technologies continue to advance. To reduce these worries, several different kinds Numerous processes with cyber-security enabled have been developed. Anomaly detection is one of these methods. Finding anomalies involves employing a variety of approaches and procedures to identify various patterns that do not adhere to predetermined characteristics of the entire body of data. Deep learning techniques have recently been accepted as an adequate option due to their capacity to take data properties directly from the data. Convolution neural network (CNN) is employed primarily because it can handle input with many dimensions. This study offers a thorough analysis of the use of CNN as a primary tool for anomaly identification. The majority of the known remedies have been compiled and categorized in the literature. Additionally, this research provides a bound together cross framework that mimics complete anomaly detection methods that were previously offered examinations, as per the information source. In-depth assessment from a brought together cross system upgrades this paper’s top to bottom conversation of how CNN is utilized in the writing’s response to the irregularity identification issue. Finally, this article makes a number of recommendations for future study areas that the audience may find useful in their own further activities in this area.
Mengers Authentication for efficient security system using Blockchain technology for Industrial IoT(IIOT) systems

The Internet of Things (IoT), which allows computers from different settings to connect and cooperate on a same activity while ensuring better levels of privacy and safety for some of the connected equipment, must manage the whole process as manufacturing procedures grow considerably more complex. Some well-liked things that solve important issues and provide third-party security. Since the problem is still open, privacy and security must come first. According to this research, Mengers identification, a unique and potent security technique made possible by blockchain, would foster confidence in a variety of industries. In this instance, access control carries out security. To allow authentication across linked devices while protecting device privacy, the Mengers authenticating mechanism is defined. Before delivering keys to linked parties, an appropriate path must first be established. The findings show improved performance during the verification process when the simulation is run in the simulation environment.
Role of human-centred artificial intelligence in facilitating learning management system

Learning management systems, often known as LMSs, represent the most desirable alternative for the provision of instructional materials in higher education since they make high-quality educational resources available to students from all over the globe. As a bridge, we utilize programs that manage the learning that occurs between students and teachers. This paradigm shift occurs as a result of the presence of intelligent agents who emulate the astute strategies utilized by the instructor and the pupils. The technologies that make up artificial intelligence are based on the reproduction of concepts that are present in human intelligence. This research gives a broad overview of the LMS environments in which AI-based techniques were used. In addition to that, the examples stress more instances of the huge range. Learning management system (LMS) users, including students, professors, and managers, may all benefit from the investigation of AI-related LMS techniques, which have been investigated as potential solutions to a variety of LMS issues.