Reviewerly/exHarmony huggingface dataset:
AI Enabled Smart Campus for Health Safety and Monitoring

COVID 19 has had a major effect on society. In order to keep people's spacing, new requirements have been placed in place regarding the amount of users authorized in individual rooms in offices, shops, etc. Along with social distance, regular temperature verification at mall entrances are indeed permitted. An excellent embedded machine learning system is proposed in this work to identify face masks automatically and detect the body's temperature in a real-time application. The proposed system, in particular, utilizes a raspberry pi camera to capture real-time video simultaneously by identifying face masks with the help of a classification technique. The face mask detector is constructed by utilizing mobilenetv2 and imaging net pre-trained weights to consider three scenarios: wearing a mask correctly, wearing a mask incorrectly, and not wearing any at all. By placing a temperature gauge on a Raspberry Pi, a framework has also been developed for determining a person's body temperature. The numerical outcomes show the feasibility and performance of our integrated devices in compared to many cutting-edge research. This temperature and facemask detection device monitors a person's body heat and detects whether or not that person is wearing a facemask. Consequently, any organization's entrance could contain this device. In this study, the door is only released if the temperature is below 99° F, which would be calculated by the Electro Selective Pattern-32 images, the MLX sensor, and the fact that a person's face is 80% protected by a facemask.
Controlled Drugs Administration using Block Chain

Controlled drugs are drugs that when used ethically may prove life-saving for patients but these drugs also have the potential for abuse, misuse, and addiction. Controlled drugs are under strict government regulations, but still few people manage to diverge these drugs from the ethical system. Few recent techniques that are considered for administration of controlled drugs are Electronic Health Records (EHR), Machine Learning and Advanced Analytics, IOT, etc. But the techniques mentioned above have some disadvantages such as EHR is centralized, ML requires high computing power and is less scalable, IOT devices are expensive and are vulnerable to cyber-attacks thus compromising sensitive information. The system proposed in this paper uses an Ethereum blockchain for administration of controlled drugs. Blockchain ensures transparency, traceability, immutability, decentralization and security in the system.
Detecting and Preventing Unauthorized User Access to Cloud Services by CASBs

It is possible for businesses to protect their cloud-based data using the ease and integration offered by Cloud Access Security Brokers (CASBs). This article explains about the ways to incorporate CASBs into a company, how CASBs can help to secure the cloud footprint, and how CASBs can help to secure the cloud footprint. This process will get initiated with CASB deployments. For enterprises, data protection remains as a major concern while transferring data to a third-party infrastructure. All the user requirements can be satisfied with a single CASB. In order to pre vent cloud malware and dangers from spreading, organizations must guarantee their workers and related sync clients and administrations using distributed storage frameworks. At the point when a worker needs to share or transfer a contaminated record, there is an option to survey and remediate the transfer across the inward and outside networks continuously. Identifying and hindering the undesirable entering into cloud administrations and information is also a part of this research study, which may aid in the discovery of accounts that have been hacked. Cloud-based risks and malware may be protected via CASBs. The ability to combine prioritized static and dynamic malware investigation for cutting edge danger knowledge is critical for ensuring a company's safety. When it comes to cloud-based risks, appropriate threat prevention may serve as a barrier for an organization.
A Survey on Prediction of Risk Related to Theft Activities in Municipal Areas using Deep Learning

Deep learning techniques have been increasingly used technique in prediction and analysis. Analyzing the temporal patterns in the crime data and extracting relevant features from the demographic information is a big task. Machine learning involves using algorithms to learn patterns present in data and make predictions. It can be used to identify crime hotspots, predict criminal behavior, and forecast the likelihood of theft in specific areas. Deep learning, on the other hand, involves using artificial neural networks with multiple layers to model complex relationships in data. It is well-suited to large datasets and can be used to analyze images, audio, and text data in addition to numerical data. Deep learning can be used for theft crime prediction by identifying patterns in criminal behavior and helping to detect crime before it happens. Algorithms including Random Forest, Naive Bayes, XGBoost, and other models were used for prediction but all the mentioned models have drawbacks including low accuracy, low performance, etc. Overall, our study shows the potential of deep learning for crime prediction, emphasizing the value of using both demographic data and historical crime data in the modeling process and the shortcomings.
A Systematic Literature Review on Security in Cloud Technology

Cloud technology is one of the most emerging technologies to provide the on-demand resources over Internet. It gives the alternative way to use available resources (hardware devices and software applications) with minimum use of physical hard drives. It reduces the investment cost in purchasing, maintaining and updating the resources. Since, resources are shared over Internet, there is threat issue with cloud technology. This issue can be tailored with the help of security. Security is the process of protecting network and infrastructure from intruders. The popular and emerging cloud security techniques are designed to protect the data that are stored in cloud. Cloud Security includes various methods for authentication, protection and various protocols to protect the applications in the cloud. In this paper, authors have elaborated the issues and challenges of cloud security because lack of authentication and so on. Finally, the literature survey outlines the suggestions that can be implemented to bring confidentiality, integrity and availability.
A Qualitative and Comprehensive Analysis of Software Testability Metrics and their Trends

Advancement in technology has resulted in birth of critical and complex software. These require thorough testing to ensure production of reliable and high performance software. Testing is the most expensive part of the software life cycle and an estimate of testing efforts can result in smart utilization of resources. Testability is the ease of finding faults in a software and its estimate can reduce costs and increase life of the software. However the area lacks adequate research and standardisation. Current studies majorly report on Object Oriented paradigm and code level testability. This study aims to provide a broader review on Testability metrics, models and establishing relationship between program attributes and testability. Through this survey 29 studies have been selected for analysis. Our studies conclude that testability metrics at code level and design metric corresponding to size are commonly used. Relationships amongst these metrics with their test efforts are established using various machine learning models and presents testability trends with various program attributes. This comprehensive review helps in identifying suitable metric, expected trends with various program attributes and selection of suitable models to automate processes.
Blood Cancer Identification using Hybrid Ensemble Deep Learning Technique

Blood cancer-related illnesses may be challenging to examine and diagnose, both of which can take a significant period of time. During the course of the preceding decade, a variety of methods for detecting, analysing, and categorizing blood cancer in people were established. These methods include: However, as of right now, there is neither a method nor a model available that can automate the process of inspecting human blood cells to determine whether or not cancer is present. A model with these characteristics has the potential to improve disease detection and prevention, which would lead to a prompter medical diagnosis. This study presents the research progress made in the area of Deep Learning Model to identify the abnormalities in blood cells. In order to detect blood cancer, this study has used Hybrid Ensemble Deep Learning method. This method results with an accuracy of more than 95%.
A Combination of RSA Algorithm with Image Steganography to Ensure Enhanced Encryption

Data is a collection of raw facts and there are lots of ways to protect it securely. Cryptography and steganography play crucial roles in the protection of data in the modern world. In order to maintain security, this paper implements a combination of both cryptography and steganography techniques. In cryptography, this work implements four levels of encryption for odd lines of text. The techniques used for encrypting the odd lines of text are scrambling alpha-numeric sequence, Cryptographic message syntax padding technique, ASCII conversion, and finally the famous RSA algorithm. Even lines are encrypted with regular substitution techniques following which the entire encrypted text is injected into an image and this technique is known as image steganography. Image Steganography is one of the popular techniques used to hide messages, where the image is in the form of dimensions. These dimensions are converted into pixels and according to the pixels the encrypted text will be embedded in the image. The same process is applied inversely for decryption and also the performances measured by the parameters MSE and PSNR. Through these parameters, the normal image and stego image is compared and provide good results. The entire process proposed in this paper results in a high-security level and good performance.
A Systematic Approach towards Security Concerns in Cloud

Cloud security includes controls and process enhancements to avoid potential attackers, and track down problems when they occur. When considering cloud security in the event of a security breach or other emergency, a data backup strategy should be considered. There are numerous cloud security solutions that use various methods for the hybrid cloud, private cloud, and public cloud. The cloud provider is responsible for the security of hardware and software, while the customer is responsible for protecting their own assets, such as virtual machines, apps, and data. In public cloud environments, this shared responsibility paradigm is used. Even though cloud provides many benefits and services, it does have some issues with secure data access and storage. There are several issues associated with cloud security, such as seller lock-in, multi-tenancy, loss of control, carrier disruption, data loss, and so on. This study examines the major cloud challenges and techniques to overcome the challenges. This study has considered the data loss as a major issue, and discusses about the different techniques to protect data in the cloud by implementing authentication, encryption algorithms, and so on.
Concealment of Data using RSA Cryptography and Steganography Techniques

The enhancement in technology leads to an increase in cyber thefts. By relying solely on techniques such as cryptography and steganography, maintaining data security becomes more complicated. Hence, to improve that, data compression is critical in maintaining information security because it increases reliability and adaptability. The main purpose of this is to understand that it not only reduces storage space but also increases data transmission speed. In data compression, there are two techniques: lossy and lossless. In this analysis, DWT (Discrete Wavelet Transform) is used as a lossy compression technique for cover images, whereas Huffman coding is used as a lossless compression technique for encrypted messages generated by the RSA (Rivest-Shamir-Adleman) algorithm. These data compression techniques will save space and time, resulting in cost-effectiveness. Moreover, this paper shows the comparison of the hybrid technique (combination of one or more algorithms) with other techniques and calculates its performance using a few metrics.
Analyzing Different Digital Image Authentication Methods

A technique called authentication is used to protect photos, audio, and video files from unauthorized access. This study reviews various image authentication techniques. It divides various watermarking schemes into classes that are then further subdivided into different types. This study presents basic techniques that can be used by the attacker to destroy photographs along with important features and quality measuring matrices for image authentication. The limitations and research gaps are also highlighted, and discussed in the conclusion along with potential future directions.
Cosmetic Suggestion based on Skin Condition using Artificial Intelligence

In the field of beauty and cosmetology, certain developments utilize artificial intelligence for the advancement and betterment of personal care. The actual challenge in the beauty industry is the identification of the skin type and the identification of product that does not include any harms and allergies for the clients. There are a lot of E-Commerce applications that provide customer service for the purchase of essentials like cosmetics and facial creams but there is no application that suggest products by the prediction of the skin condition. The aim of this application is to highlight the current and developing applications of artificial intelligence and machine learning in beautification and cosmetology by digital analysis of skin condition and suggest better products based on the type of skin and digitally check the skin tone and buy harmless products for their skin using the Convolutional Neural Network (CNN) algorithm to carry out the workflow of the process.
Voice-based Virtual Assistant with Security

With the advancements in speech recognition and AI technology, there is a growing demand for convenient and efficient ways to interact with technology. A Voice-based Virtual Assistant is a technologically advanced solution that uses speech recognition and artificial intelligence to provide users with a convenient and efficient way to interact with devices, access information, and perform tasks. It makes use of the GPT-3 language processing model by OpenAI to respond intelligently. With the help of spoken commands and natural language processing, users can communicate with the virtual assistant using this technology, which is intended to function as a chat interface. However, as with any technology that collects and stores personal information, security is a major concern. The added security feature addresses this concern by incorporating encryption, authentication, and access controls to ensure that personal data remains secure. This not only protects the user’s privacy but also helps to prevent unauthorized access to sensitive information, such as bank account numbers, passwords, and other confidential data.
Image Security is Improved by Super Encryption using RSA and Chaos Algorithms

The need for image encryption is increasing as a result of the increased usage of the Internet and other communication tools. It is easy to attack and steal images sent over unsecured open channels. Techniques of encryption are necessary to safeguard the photos from attackers. In the modern period, the area of encryption is growing significantly. Given how dangerous cyber assaults become, image security is of the highest importance. One of the most well-liked methods to hide information is using image encryption. Based on various algorithms, there exist several ways for picture encryption. Some of them include the RSA algorithm, which offers faster encryption s peed and less security by encrypting data with public and private keys. Even though it takes longer, a different algorithm known as the Chaos-based algorithm offers greater picture security. The RSA and Chaos-based algorithms may be combined to produce advanced encryption, which gives high security in a shorter period of time. By using an RSA- generated key with a sufficient key s pace to thwart brute force attacks from succeeding, the encryption performance may increase without compromising the security.
Cyber Security Control Systems for Operational Technology

Digital related occurrence reaction plans stretch out their network safety projects to address OT [Operational Technology] frameworks. Numerous organizations have insufficient OT digital protection and do not have the controls that are normal spot in IT [Information Technology] organizations, like design the board, emergency courses of action, access control strategy and validation strategy. OT framework administrators are essentially worried about the accessibility and dependability of the framework and less so with the security in light of the fact that, verifiably, OT frameworks have been confined from IT organizations. The ascent of computerized advancements brings another degree of digital intricacy to manufacturing plants. The Fourth Industrial Revolution proclaims a period of enormous potential for advancement and development. It additionally brings new dangers and difficulties. Cycle should cover the danger evaluation, security design, and consistence reviews. Innovation should cover control, and noteworthy knowledge. Data Technology (IT) and Operational Technology (OT) networks that controls task progressively to control framework items alluded to as framework Control Systems, Energy Management Systems and Manufacturing Execution Systems relying upon the business. There has been a lot of conversation and discussion around the assembly mediate Information Technology (IT) and Operational Technology (OT). With an end goal to give better performance and data stream between income creating OT resources and endeavor applications, these frameworks have frequently been interconnected, as a rule without appropriately getting the control frameworks from digital assault first. On the off chance that the IT and OT networks are interconnected, yet not appropriately got, a break to one organization can undoubtedly cross over to the next, leaving the whole processing foundation in danger. IT and OT networks seem to have comparative advances and digital protection approach carried out for security reasons. There may be numerous significant contrasts in IT and OT organizations will be uncovered. The novel qualities of OT frameworks and organizations block numerous conventional IT endeavor security items from working securely without debilitating activities.
Performance Analysis of Olympic Games using Data Analytics

This paper is used to analyze data from the Olympic Games in order to identify patterns and trends. The data set includes information on athletes, events, and results from the Games. The analysis will focus on identifying trends in athlete performance, event popularity, and results over time. The study will begin by cleaning and preparing the data for analysis. Next, descriptive statistics will be used to summarize the data. Finally, inferential statistics will be used to identify patterns and trends in the data. It is expected that the analysis will reveal patterns and trends in the data that will be helpful in understanding the Olympic Games and predicting future results.
A Hierarchical Taxonomy of Load Balancing in Cloud Computing

A multi-variant, multi-constrain problem called load unbalancing reduces the effectiveness and performance of computer resources. Overloading and under loading are two undesired aspects of load unbalancing that are addressed by load balancing solutions. To the extent possible, various load balancing approaches currently in use are not organised in a comprehensive, thorough, systematic, or hierarchical manner.Furthermore, the literature neither studies nor considers the causes of load unbalancing. This study provides a comprehensive analysis various load-balancing strategy. Build efficient load balancing algorithms in the future, the benefits and drawbacks of current systems are emphasised, and significant problems are addressed.
Enhancing Infants Early Communication through Sign Language Recognition System using Raspberry Pi and Convolutional Neural Network

Early communication among infants is very helpful especially on the growth and mental ability of the child. Infants' early communication, as they have limited verbal skills and may struggle to communicate their needs and feelings effectively to their caregivers. While sign language has been shown to be an effective way to bridge this communication gap, caregivers may not have the necessary skills or knowledge to understand or teach signs to their infants. To address this problem, this research aims to develop and evaluate an automated sign language recognition system that can enhance early communication between infants and caregivers, thus improving infants' overall language development and quality of life. Using the internet of things and the models of machine learning algorithms for early recognition of sign language gesture results better understanding of mental acquisition of infants. This research explores the feasibility of using an Internet of Things (IoT) device, specifically a Raspberry Pi, combined with deep learning techniques to recognize baby sign language. The study uses a deep learning model to recognize sign language gestures and evaluates the impact of various variables, such as lighting conditions and background, on the accuracy of the model. The results demonstrate that the system is capable of accurately recognizing baby sign language gestures with an overall accuracy of 94.6%. This research has important implications for early intervention for hearing-impaired infants, as it allows for real-time monitoring and tracking of a baby’s development of sign language skills. The study recommends further exploration of using IoT devices and deep learning techniques for recognizing and monitoring baby sign language, as well as potential applications for assistive technologies and other areas of human communication.
Empowering Tourists with Context-Aware Recommendations using GAN

A technology-based solution known as a tourism recommendation system makes suggestions to visitors based on their preferences, prior travels, and experiences. These systems gather data from a variety of sources, including web searches, user reviews, travel history, etc. When there is a lack of data about some users or items, current technologies suffer from sparsity, which can lead to inaccurate recommendations. Another challenge that tourism faces is the diversity issue, which occurs when similarities are valued over preferences. GAN and context-aware recommendation systems are two prominent techniques that are combined in our system. The system's objective is to give personalized recommendations to travelers based on multiple contextual elements. The system creates new recommendations based on the knowledge it has gained by using GANs to identify patterns and connections between various elements of a tourist's setting and their preferences. Synthetic data is generated to supplement the original dataset, which can aid in overcoming the cold-start and sparsity issues. It can also aid in the development of more scalable recommendation systems.