Reviewerly/exHarmony huggingface dataset:
Implementing Dead Letter Exchanges in MQTT and Proposing a Broker Failure Algorithm Utilizing Blockchain DNS

The Message Queuing Telemetry Transport (MQTT) protocol is used in the Internet of Things (IoT) and Edge Computing for its lightweight and efficient data exchange capabilities. However, challenges like lack of fault tolerance on the broker end and limitations in storing unacknowledged messages in Quality of Service (QoS) higher than 0 have emerged. This paper proposes leveraging Blockchain technology to enhance fault tolerance in dead letter exchanges at QoS levels 1 and 2 and enable more efficient and secure data management through block storage of unacknowledged messages to address these issues. The proposed approach suggests using Blockchain to implement dynamic load balancing through Blockchain Naming Services, which involves hosting brokers under decentralized domain names on the Blockchain, monitored by a load balancer. In case of a broker failure, the load balancer can seamlessly migrate topics from the failed broker to the next available broker, ensuring uninterrupted communication between publishers and subscribers. This approach aims to improve the performance and reliability of the MQTT protocol while preserving its lightweight and minimal overhead nature. By leveraging Blockchain technology, this paper offers innovative solutions to overcome the limitations of MQTT and enhance its capabilities as a reliable and efficient data exchange protocol for IoT and Edge Computing applications. The findings of this research contribute to the growing body of knowledge on integrating Blockchain with MQTT and provide insights into how Blockchain can be used to enhance the fault tolerance and scalability of IoT protocols.
Novel Two Dimensional Chaotic Map for Secure Image Communication

In present era of fast multimedia communication, there are many loop holes where the security is compromised. We have developed a new two-dimensional chaotic map for secure multimedia communication in this research. The generated two dimensional triangle function combined discrete chaotic map (2D-TFCDM) is rigorously studied and tested. The various randomness test applied on the novel map are: National Institute of Standards and Technology (NIST) test suite, attractor plot, sensitivity test, bifurcation diagram and Lyapunov exponent. Generated map is utilized in image encryption, where it is combined with hash functions for better security. The generated map passes all the tests and demonstrates that the novel discrete system is extremely chaotic and can be used for cryptography applications. Simulation results obtained from the proposed methodology is compared with the existing techniques that show the extreme chaotic nature of the proposed system.
Hybrid Methods For Classification Of Breast Cancer Using Machine Learning Techniques

Over 10% of population in the world is affected by breast cancer and it is prominent to predict and classify the type and seriousness of the disease. Considering the risks involved, early an accurate diagnosis of breast cancer plays an important role in its treatment. This paper aims to help the healthcare system worldwide by providing with a dependable method for the prediction of breast cancer. Various ML methods are proposed to find the accuracy and precision of the disease from mammography images and efforts to improve levels of accuracy has been taken. In this paper, 5 ML methods are compared to find the accuracy of Breast Cancer and further voting methods have been used to select the best possible combinations to find precision, F-score, recall and accuracy to find the type of cancer in both men and women. From the results, it is evident that algorithms that are based on distance have better results compared to methods based on entropy of the dataset for the applied dataset.
College Enquiry Chatbot

Today's widespread use of smartphones is proof that technology is always evolving. Nowadays, artificial intelligence is crucial to numerous industries, including manufacturing, human resources, and customer service. There are numerous chatbots that help people discover solutions to their questions. As a result, we are developing an AI-powered chatbot that can address all questions about colleges. It serves as an intelligence tool with an emphasis on higher education. This artificially intelligent machine will respond to queries from users regarding matters related to higher education. The information is kept in the chatbot's database so that it can be used to spot trends and decide how to respond to questions. The chatbot for college inquiries was developed using an NLP system that evaluates questions and comprehends messages. People react to each other differently based on emotions and attitude. Because chatbots must go by rules just like humans do, they will communicate with customers in a courteous and correct manner. Students can ask the chatbot any questions at any time of the day or night, and they will receive a prompt and accurate response. A chatbot can respond to thousands of users simultaneously. Chatbot can work 24 ×7 without getting tired. It has minimal errors, which increases productivity.
Statistical Comparison of the Running Times of Global Posteriori Inference Algorithms in Algebraic Bayesian Networks

Global posteriori inference is an important problem in the theory of algebraic Bayesian networks, which are a subclass of probabilistic graphical models. So far, two algorithms have been presented that allow global posteriori inference. These algorithms use different global algebraic Bayesian network structures – secondary and tertiary. Thus, the task of comparing these algorithms is defined, and, in particular, it is necessary to perform a statistical analysis of their running times, which is the purpose of this paper. In addition, the stochastic generation algorithm of acyclic algebraic Bayesian networks was defined in the framework of the article, which allowed to generate data for the experiment. As a result, it was found that the algorithm using a tertiary structure works 1.15–2 times faster on algebraic Bayesian networks, where there are 5–150 knowledge patterns. At the same time, the difference in running time is noticeable the more atoms in the knowledge pattern models and their intersections.
Adversarial Attacks against Neural Networks using Projected Gradient Descent with Line Search Algorithm

The aim of the research is to investigate the security challenges posed by the deployment of neural networks, with a focus on evasion attacks. The research uses the MNIST dataset as a representative example and employs the projected gradient descent with the line search (PGDLS) algorithm to craft adversarial examples that can deceive the network into making incorrect predictions. In this research, we demonstrate that neural networks are vulnerable to such attacks and investigate PGDLS ability to craft adversarial examples. The research also aims to provide insights into the security challenges posed by evasion attacks and to contribute to the ongoing research into the security of neural networks. The ultimate goal of the research is to raise awareness of the security risks associated with the deployment of neural networks and to provide valuable information for the development of robust and secure models. In order to prevent neural networks from malfunctioning or being misused in real-world scenarios, ongoing research is essential to enhance their security.
Image Inpainting Based On Sparse Representation with Dictionary Learning and Construction

The practice of imperceptibly altering images has a long history in artistic production. In the field of image processing, there have been significant advancements in studying image deblurring, denoising, and inpainting, contributing to picture enhancement and restoration. These studies have garnered widespread recognition due to substantial improvements in recent years. One intriguing area of research in picture restoration is the reconstruction of missing regions, which has various real-world applications. This paper focuses on removing extraneous text material from images through an image inpainting task. The K-SVD approach is utilized to acquire sparse representation dictionaries from the given image. Random and DCT dictionaries are constructed for image processing, and through the application of the K-SVD technique, the dictionaries are trained to adapt effectively to image patches with different properties.
A Steganographic Method Using a Secure and Effective Pattern-Based Approach

Information is now widely available thanks to the development of the Internet and communication technologies. By itself, the abundance of knowledge creates a risk. The risk of intentional interference and eavesdropping in electronic communication is rising. In this day and age, privacy and security are top priorities. Two techniques have been developed in this context: cryptography and steganography. Steganography hides the presence of the message, whereas cryptography scrambles it to make it illegible. Steganographic techniques might be frequency-domain or spatial-domain (changing the pixel values to conceal data) (modifying the image's frequency coefficients). The vulnerability of spatial domain techniques to steganalysis is greater than that of frequency-domain methods. In this project, we provide an original pattern-based steganography method with an enhanced Caesar cypher for cryptography. This method chooses four patterns to conceal once the data is first encrypted using the extended Caesar cypher, the message in the picture. An additional degree of protection is added by securing the secret message with an AES-encrypted password. This technique is unnoticeable to visual assaults, histogram attacks, and statistical attacks since the changes it produces are not evident in the final image. This method offers fresh insight into a steganographic process that is more reliable, effective, and adaptable.
ROI Extraction and Nuclei Classification of Pap Smear Images for Cervical Cancer Detection

Automated nuclei classification methods are increasingly important in improving the accuracy and efficiency of cervical cancer screening. These methods utilize artificial intelligence algorithms such as machine learning and deep learning to identify and classify cellular nuclei from Pap smear images. However, the implementation of these methods presents various challenges such as the need for high-quality images, manual annotation of training datasets, and the management of changes in lighting conditions. This paper proposes a novel automated nuclear classification method that employs an enhanced Alexnet model to overcome the aforementioned challenges. In addition, this paper presents a Region of Interest (ROI) Extraction method to improve the accuracy of Pap smear image classification and reduce the computational burden of the proposed AlexNet model. The proposed system is evaluated using a publicly available dataset, and the results indicate a classification accuracy of 95.78%, precision of 94.86%, recall of 96.86%, F1-score of 95.82%, and specificity of 94.82%. These outcomes suggest that the system can effectively automate the process of nuclei segmentation and accurately identify and classify cellular nuclei from Pap smear images with minimal human intervention. The findings demonstrate that the proposed system is an effective approach to automated nuclear classification.
High-Performance Authentication and Efficient Block Creation: Ethereum

At present, there is rapid growth in storing data online. Online data storage enables users to access their information anywhere and anytime. However, this can lead to some security issues, which can be handled today using Blockchain. Numerous researchers have worked on various blockchain application domains, including supply chain management, the medical industry, governance, etc. In addition to these applications, many issues still need research, such as security, privacy, performance, scalability, etc. As part of our research, we are focusing on a blockchain-based medical application field. The concept is based on the Internet of Things (IoT) and Blockchain, where we employ wearable devices to read user data like blood pressure, heartbeat, and temperature. However, anyone can wear this wearable device which comes up with the problem that it is optional that the data we are storing comes from a valid user. Secondly, we need fast access to data stored in Blockchain, but as the block creation takes 10-12 seconds, we suffer from some delay in response time. In our work, we proposed solutions to these two problems by using authentication while storing data and reduction in the Block creation time.
Keep it simple: A Novel Algorithmic Based Chess Scoresheet Writing using Computer Vision

Chess players use chess scoresheet to record the moves they and their opponent make during a game. Maintaining a scoresheet during the game in few time formats is mandatory. It serves several purposes for the players and the organization hosting the tournament. It assists players in learning from their mistakes and helps organizations resolve disputes between players. In a nutshell, the scoresheet is a proof. Handwritten score sheets have numerous drawbacks, necessitating the use of technology to automate them. Various researchers have worked on it, but it is done with either piece recognition or board recognition using Convolutional Neural Networks (CNN) algorithms. It frequently necessitates a large amount of computational power, generates memory overhead, and has numerous drawbacks. It only works for digital chess images (online chess games) or physical chess board images (offline chess games), not both. This paper proposes a novel algorithmic-based module called Keep it Simple (KIS). This KIS writes the scoresheet using the input binary matrix obtained from the digital image of the chess board. The proposed algorithm requires less computational power and eliminates all the bottlenecks of existing methods. Moreover, it works for all chessboard images, regardless of color, size, board type, or game mode (online or offline).
CYBER WEB: An Extension for Scanning Websites

Technological advancements are hiking at faster paces than light. With every second we travel around the sun while constantly spinning in space, with no absolute end, the technology we make is increasing. These technological expansions are very hard to contain or control. With the beauty and complexity of the human brain, we are successfully making greater technological discoveries and inventions, to provide the human population with the best of the better. Cyber security has to keep up with the day-to-day life of the developing technology. As new updates roll out for websites, applications, appliances, devices, etc., almost every minute, security gets harder and harder to provide. The discrepancy between technological advancements and security, requires cyber security to strengthen for data protection. Websites dominate the internet. However, as the updates increase, there may be an increase in security concerns. This study provides intensive research into cyber security tools, to scan the risk, threats and vulnerabilities of the web applications.
Data Security Optimization Using New Hybrid Cryptographic System

Every communication system's main issue is data security. For any business, educational institution, or defense force, communication has become a crucial instrument. Data transfer must be done safely and securely. Many cryptographic methods have been suggested and put into use as of late. Based on the quantity of keys employed, those algorithms are divided into asymmetric and symmetric algorithms. Although numerous algorithms are employed to protect data, they occasionally breach its security. At now, the goal is to combine a number of safe methods to create a highly secure data transmission environment. The RSA asymmetric method, the SHA256 hashing technique, and the symmetric cryptographic algorithms AES and Twofish will all be merged. These methods enable us to guarantee the secrecy, authentication, and data integrity of three cryptography fundamentals. For text encryption, a number of security analysts are utilized, including differential analysis, histogram, information entropy, secret key size and key sensitivity. The suggested technique offers a high degree of security, a rise in key size, and a better encryption scheme according to the findings of numerical simulation for text encryption.
Effective K-means clustering algorithm for efficient data mining

With the advancement of technology and the increase of data, Data mining needs to be more efficient. Therefore, data mining along with machine learning and statistics gets effective to face today's scenario. Machine learning provides an easy way to accomplish tasks and statics provides a standard computational skill to understand, manage and analyze data. The work surrounds the enhancement of the K-means clustering algorithm to make it more effective and to create accurate clusters over large data sets. There are some flaws in the classical K-means algorithm the major is that it cannot work over large and dense data points. This paper aims to improve the k-means algorithm so that it can create more smooth and more accurate clusters over highly dense data points. To accomplish this window density method is being used to create more homogeneous clusters. This will make the K-means algorithm suitable for all shapes and sizes of clusters. This will also enable handling noise effects, spotting outliers, and discovering clusters other than hyper ellipsoids. We have introduced the K-means algorithm with window density and derived that the results are more accurate than the original K-means algorithm as we have evaluated the outcomes through purity measure.
Enhanced Unimodal Continuous Authentication Architecture on Smartphones for User Identification through Behavioral Biometrics

Nowadays intruders are smart and intelligent to initiate various attacks on the smartphone's entry-point au- thentication methods to access the user's stored information on the smartphone. Accessing users' confidential information rather than personal information by attackers can create a monetary or non-monetary gain. Thus, continuous authentication was proposed on smartphones to recognize the user from the entry point to the end of the session. Even though, we noticed that earlier works on unimodal-based continuous authentication were limited to specific applications on smartphones. This can create a vulnerable gap to access the users' information on the smartphone. Thus, we propose an enhanced unimodal-based continuous authentication architecture for user identification on the smartphone. The proposed approach instantaneously chooses the unimodal behavioral biometrics based on the foreground application of the device and performs the user identification through a machine learning model. The experimental results pro- posed approach show better results than the existing literature.
Video Segmentation using YOLOv5 for Surveillance

One of the most major issues associated with computer vision applications includes image and video segmentation. The objective of video segmentation is the extension of the image segmentation task from the image sector to the video sector. The problem is the simultaneous detection and segmentation of objects in image frames of videos. Many new methods typically develop complicated pipelines to handle the task of video segmentation. Previous methods generally first obtain an image frame or clip from the video, do the segmentation task and piece together those incomplete results by tracking or matching them with a very low accuracy of less. This paper proposes a new way of video segmentation using the YOLOv5 algorithm which puts forward the use of a neural network from beginning to end (the complete process from the beginning to the end) that makes predictions of bounding boxes and class probabilities concurrently. When provided with a video clip containing multiple frames of images as input from the user, the YOLOv5 algorithm will output the objects in each frame of the video in the same order as given by the user. Also, the YOLOv5 and the Mask R-CNN [12] algorithms are compared in terms of computational speed and the comparative implementation of both the algorithms show that the YOLOv5 is much faster than the Mask R-CNN algorithm.
Optimization of Data Memory and Safety in Clouds with Hashing Algorithm

Although cloud computing frameworks consume a significant amount of system resources, it is still difficult to consistently obtain precise responses to query requests. Many plans fail to address the issues raised by persistent inquiries due to a lack of space and extremely complicated progressive maintenance. Hash-based information structures are frequently utilized in the construction of records due to their rapid response times to queries and consistent scale that leans toward complexity. Sadly, information structures based on hashing take up little space and are likely to be able to handle hashing crashes. Hash tables frequently resolve crashes caused by hashing by employing open tending, binding, and mixed hashing. Cuckoo hashing, in contrast to standard hash tables, handles crashes in hashing by moving things around with simple “throwing out” actions.
Developing an image strengthening method for improving the contrast and colours of underwater photographs focused on haze removal

Underwater photos can be hindered by haze, which reduces visibility and makes it difficult to extract useful information. To address this, a crucial task in underwater image processing is removing haze, which can improve image quality and facilitate further analysis. This study introduces an image enhancement technique that combines haze removal with contrast and color enhancement to further enhance the visual quality of underwater photos. The proposed method first employs contrast enhancement and weight maps before applying white balance to the same input. Weight maps are again applied to the white-balanced image before a fusion technique is used to merge the results of the previous steps. Experimental findings demonstrate that this approach successfully enhances the overall visual quality, contrast, and colour of underwater photos.
Smart Attendance Using Face Recognition

Manual marking of attendance on a daily basis is a tedious and time-consuming process. Facial recognition is being used on large scale to tackle this problem. We have developed a similar system that recognizes different faces and marks the attendance of the individual along with sending the filled excel sheet of the attendance of all the students to the concerned faculty. This allows faster marking of the attendance for a large number of students.
Autonomous Obstacle and Object Detection for Visually Impaired With Audio Aid

The Object recognition is the most happening and challenging application in many areas like helping visually impaired people, autonomous cars or robots, security tracking etc. This application works efficiently under the most popular technique's like machine learning, deep learning which contain various algorithms to work on the motive to detect the different objects from a complex image. The application works under the best algorithm and used in the hardware component like raspberry pi, which can be attached to the blind stick and works as the visual assistant for the visually impaired people by detecting the objects and obstacles and gives the audio aid.