Reviewerly/exHarmony huggingface dataset:
Multiple Replica Based Auditing of Outsourced Data: A Comprehensive Survey

Due to its wide range of services, the cloud has amassed a significant number of consumers in the current environment. Data owners can keep any kind of data on the cloud server, and users can access it whenever they need it. The simplicity of using data that has been outsourced comes with many difficulties. Outsourced data’s accuracy and confidentiality are at risk. Because cloud service providers occasionally lie, users have little faith in them. In order to evaluate and record the performance of their cloud vendor, a data owner conducts a cloud audit. Analyzing a cloud vendor’s compliance with a predetermined set of controls is the aim of the audit. This article explores numerous cloud auditing techniques, their classifications, advantages, and disadvantages. The main focus of the study is the exploration of Multiple replica-based studies and their performance evaluation. The article discusses the way ahead in the field of multiple replica-based auditing research.
Small Object Detection Using Depth based Optimized NMS

This paper introduces the method and algorithm for improvement of detection system based on CNN. due to some poor resolution and conflict background system faces problem of small object detection. So this paper proposed hybrid method for small object detection. Using improved loss function on IOU for bounding box regression and solve the positioning deviations by using ROI pooling operation and also use the multi-scale convoluted fussed features for more information. This algorithm gives good performance on small object like knife, cutter, wallet, screwdriver etc. Algorithm recall rate reach 85% and accuracy reach 80%. So, the detection is significantly better than other method and more effective to detect small object.
Medical care Robots and Their Utilizing

With recent development in Drone technology and the conditions of traffic in our country a drone to provide instant first aid to an onsite accident as a measure of being the first responded wifi benefit multiple people in an emergency, thus deploying a drone to provide relief is the ideal choice and such is explored in the paper submitted.
Cloud based architecture for Face Recognition in Django with Machine Learning

If we compare a face identification system to others like fingerprint, iris scanning, signature, etc., its applicability is simpler and its operating range is bigger than others. The face has a more pronounced structure and a larger surface area than other biometrics, and it can be collected without any physical touch.. We suggested combining Django and machine learning to create a cloud-based architecture for face recognition. The system recognises several faces in live captured images using a mix of techniques, including face identification with a histogram of oriented gradients (HOG), image augmentation, extraction of facial embedding, and classification of images using linear SVM. The local system camera is used to identify faces on individuals. Face detection, Face landmark, Face recognition is done first. The system is then updated to include a face classification technique that makes use of linear SVM. A database that we created with the help of 10 people is used to test the system. Within the desired parameters, the tested system performs acceptable for facial recognition. On the provided data, the linear SVM algorithm's accuracy is 99%. The system can also identify several faces in live photos that have been captured.
MedEHR-Electronic health Record using Blockchain

Blockchain technology is poised to significantly help the healthcare sector with its high degree of security, privacy, confidentiality and decentralization. In this research, the authors describe how blockchain technology can be used to transform existing medical data systems, and also present a framework that can be used to implement it. The goal of this framework is to provide secure storage of electronic records by defining detailed access rules for users of the proposed framework. The proposed system aims to leverage individual medical details, reduce the rate of duplicate testing, reduce treatment delays, and provide patients with sufficient information to make better decisions. When a patient visits a hospital, the hospital creates a patient record and stores it in a decentralized application [6] (DApp). A DApp is a blockchain-based platform for storing electronic medical records. Doctors can update patient records, and patients can access their medical records from her DApp using public keys. EHRs enable hospitals and doctors to track the information they need to comply with insurance companies and federal regulations. EHR acts as a central data source base where doctors’ orders for laboratory tests, x-rays, and other tests are stored.
A Framework for Sign Language to Speech Conversion Using Hand Gesture Recognition Method

The proposed work aims at designing a system which will be able to convert the signs or gestures into speech and text format that can be understood both by disabled as well as normal people. The people with disabilities face endless challenges and one the major challenge is not able to interact with ordinary people. Fulfilling the gap between disabled and ordinary people need to be given utmost priority. People who have this disease interact with one another in a variety of ways. There are several ways for them to communicate, and sign language is among the more famous choices. Individuals can communicate with sign language by using their bodies as their speech. One of the necessary and quickly expanding study topics is gesture recognition. Gesture Recognition can also be understood as a well-methodized language in which each symbol, sign or character is assigned with a specific meaning. We are aware that gesture recognition is the only means of communication for people with disabilities. Computers needs to be upgraded in such a way that they can read sign language, organize data, and eliminate the difference between normal people and people with disabilities. The proposed framework will not only be helpful for the disabled people but also for the technical and non-technical fields for verities of applications, this is because this system is much more efficient, accurate, cost-friendly, and easy to use. But, primary purpose of these documents is to translate the sign language of a mute person into proper speech and text.
U-Net Variants for Brain Tumor Segmentation: Performance and Limitations

Brain Tumor (BT) segmentation is a vital aspect of medical imaging essential for precise identification and subsequent treatment. Deep Learning (DL)-based methods, including U-Net and its variations, have demonstrated potential in BT segmentation. This paper reviews recent research on U-Net and its variants for BT segmentation, highlighting their strengths and limitations. The study also summarizes recent results from different BraTS datasets, which establish the effectiveness of U-Net in segmentation tasks. The high modularity and versatility of U-Net make it an indispensable tool in the medical imaging community, widely adopted for various segmentation tasks. The study's findings will benefit medical image analysis researchers, practitioners, and those interested in DL techniques for image segmentation.
Artificial Intelligence Based Dynamic Approach to Visualize the Graphs

Understanding graph algorithms for beginners is the biggest nightmare for them. To conquer that fear we have decided to build a website which will help them to understand graph algorithms in a fun and easy way. It is a visualizer website for Graph Algorithms, where you can see a detailed explanation and animations of important algorithm runtime and build intuition. Graph Algorithm Visualizer allows you to easily construct graphs and step-by-step observe execution of algorithms on them. It visualizes different types of graph algorithms such as Prim’s Algorithm, Kruskal Algorithm, DFS etc. Our aim is to build a project which will work for a teaching and learning tool. In learning mode, we are providing a detailed visualization of graph-based algorithms, such as Kruskal, Prim’s algorithm as well as each step will be verified against the algorithm and results will be provided at a time. We have confidence that our project will help many students to understand graph-based algorithms at a deeper level and improve their knowledge of graph algorithms. This model is especially built to help faculty to teach graph algorithms in a creative and fun way for students.
Securing Data in the Internet of Things (IoT) using Metamorphic Cryptography - A Survey

The Internet of Things (IoT) involves connecting a wide range of devices to the internet, allowing them to be controlled and monitored remotely. However, the transmission and storage of data in the IoT environment create security risks, including the possibility of unauthorized access or tampering. It is essential to use strong cryptographic and steganographic techniques to protect the confidentiality and integrity of this data. Cryptography involves using mathematical algorithms and protocols to encrypt and decrypt data to protect it from unauthorized access. Steganography is away to hide a message within another message or piece of data to keep it from being found. Their combined usage can help to secure IoT devices and ensure the confidentiality and integrity of the data transmitted and stored in them. This paper discusses the existing implementations of metamorphic cryptography to secure data in IoT devices.
Voice-Based Virtual-Controlled Intelligent Personal Assistants

Virtual assistants are improving and providing consumers with greater advantages. The comprehension and fulfilment of requests by virtual assistants will increase as voice recognition and natural language processing continue to grow. Virtual assistants are projected to be employed in more commercial activities as speech recognition technology advances. The main goal of developing personal assistant software (virtual assistant) is to use web-based semantic data sources, user-generated content, and knowledge from knowledge libraries. Basically, main objective of making this Voice-Based Virtual Assistant is to make life easier and having a personal assistant to everyone which can perform many tasks. As the end user interacts with a virtual assistant, the AI programming learns from the data provided and improves its ability to forecast the end user's needs. Virtual assistants are often used to do things like add tasks to a calendar, provide information that would normally be found in a website, and operate and monitor Smart Home devices like lighting and cameras and thermostats. Massive volumes of data are required to fuel virtual assistant technologies, which feed Artificial Intelligence (AI) platforms such as machine learning, natural language processing, and speech recognition. Speech recognition has a lengthy history and has seen several key advancements. On smartphones and wearable devices, speech recognition for dictation, search, and voice commands has become a standard feature. Design of a small, large vocabulary speech recognition system that can run quickly, accurately, and with minimum latency on mobile devices.
Chat Web App using Blockchain

As today’s world is the world of the technology and we all are depends on technology in one way or in another way.We all are using the apps which are secure we can’t say as our all the data is stored to the companies whose app we are using. As today’s app which we are using are not pretty secure and the administration can change the data or may misuse the data.So to protect the users data we are developing the app using blockchain. Blockchain apps are also known as the Decentralised apps which uses the peer-to-peer network and it ensures that due to central node failure no other network failure should be there. The Blockchain apps treats as immutable ledger that allow the chatting in a decentralized manner. A Decentralized apps for conversation and exchange of data is a need in today’s world because keeping data and information on a centralised server can be risky and it may be costly too. We can implement different ways to share data and to communicate to others using decentralised apps. By gathering the Blockchain and decentralised apps we can make a better and secure apps which will be secure a,easy to use and less costly.
Enhance the Quality of Recommendation System in E-Commerce Application

The recent trend of shopping has largely shifted towards online platforms. The future of online shopping to fulfil our needs looks promising since the internet is reaching to every corner of planet and various businesses are revolving around this. Online market provides customers a lot of options to choose from, this creates confusion among customers. To avoid this, platforms use something called recommendation systems which recommend products to customers based on their previous preferences and recent trends. Recent data also shows that enhancement in the recommendation system provides growth in business. So, recommendation system needs to be more accurate to fulfil both business and consumer needs. This paper discusses the enhancement of recommendations in E-Commerce platform using hybrid recommender system. Collaborative and Content based filtering has its own limitations, this paper provides a way to combine both and some other features-based filtering systems to get better results. Comparative study has also shown the better result for this method.
Warning System with Face Mask Detection Using CNN

The main motive of writing this paper is to create a facial mask detector that can find whether a person has worn a facemask properly or not. This paper is used to solve real-life problems that nowadays we must avoid. So it helps any person. After implementing this detector, we can easily classify people who are wearing a face mask and the people who are not wearing the mask and people who are incorrectly wearing facemasks. The proposed method of this paper uses the YOLO algorithm. This detector can be used for precautionary purposes during the spread of COVID-19. The aim of this research paper is to propose an alert system that detects individuals not wearing masks in public places using Convolutional Neural Networks (CNN). The proposed system uses a dataset of images to train the CNN model, which accurately identifies the presence or absence of a mask on a person's face. Live video feeds from cameras installed in public areas are monitored by the system, which sends alerts to authorities when it detects individuals not wearing masks. The effectiveness of the system is evaluated through experiments and comparison with other state-of-the-art face mask detection methods. This paper proposes a YOLO algorithm-based facial mask detector that can classify people who are wearing masks, those who are not, and those who are wearing masks incorrectly. The proposed method can be used for precautionary purposes during the spread of COVID-19 and has the potential to mitigate the spread of infectious diseases.
Text Generator using Natural Language Processing Methods

The field of NLP (Natural Language Processing) deals with the interaction between computers and human languages, focusing on the automated analysis of human language to extract useful information. For organisations that place a premium on efficiency, automation is the ultimate aim, and natural language processing is key to getting there. A machine can only completely comprehend humans when it talks with them in human language; it’s like speaking to someone in your mother tongue as opposed to someone you hardly understand or can barely speak. It is closed using natural language processing. . This paper will examine how a machine can truly comprehend human language while also delving into the specific algorithms that are employed. This essay will examine text conversions in-depth and go over how natural language processing proceeds. For those who are curious about the fundamentals of comprehending and mastering natural language processing, this review essay may be helpful. Natural language processing has a wide range of applications, including text processing, summarization, translation, voice and speech recognition, AI-powered user interfaces, expert systems, and more. However, Despite advances, NLP still faces challenges in accurately detecting emotions and expressions conveyed through non-verbal cues such as voice tone and body language. There are far too many difficulties in processing the complex linguistic system of humans. It will soon be necessary for everyone to communicate with a machine for both domestic and space-related tasks, making it crucial to comprehend how artificial intelligence works and how crucial NLP is to it.
Handwritten Text Recognition Using Machine Learning and Deep Learning

A new area of computer vision is character recognition. A common research topic is the growing use of digital and modern technologies in practically all industries and daily activities to store, transmit, and recognise handwritten characters for usage in digital formats. Any style of handwriting can be recognised by people. The handwritten transcription cannot be recognised by the machine. We require the computer to recognise the handwritten text because of this. A computer system may recognise and digitize handwritten input from sources including pictures, handwritten documents, and other sources of text by using handwritten character recognition. The development is based on a machine learning and artificial intelligence subfield known as deep learning. There are many different approaches and strategies used to construct handwritten character recognition systems. Yet, only few of them concentrate on neural networks. Compared to earlier methods, the use of neural networks for handwritten character recognition is more efficient. The Handwritten Character Recognition System is described in this system, along with its architecture, design, and testing procedures. The objective is to show how well neural networks recognise characters in handwritten text. In order to read handwritten notes from students and instructors, this system will report on the development of a handwritten character recognition system. This system turns a handwritten transcription's image into a digital text.
Obstruction Avoidance Framework for Indoor and Outdoor Navigation to Enhance Mobility of Visually Impaired Person

Blindness and visual impairments in computer vision are among the top ten disabilities affecting people, with India having the largest percentage of visually impaired people worldwide. An object detection and recognition framework is being created to aid in helping these people navigate and remain safe in their surroundings. This system uses a camera to capture an image and feed it as input to the software. The Single Shot MultiBox Detector (SSD) architecture is employed for object detection, utilizing deep neural networks to achieve accurate results. The input image is processed using the COCO datasets, which act as the system's training data and are predefined in the Tensor Flow framework. About 90% of objects in the actual world have features in these datasets, and depth estimation is used to calculate distance. The software then produces an audio output with the assistance of voice packages. Python is used to build the system because it has many built-in packages and libraries that make complicated code easier to understand. As a result, writing programmes with fewer lines of code is simpler.
Prevention of Website from Cross Site Scripting

The Web is an enormous part of many organizations and business activities. It holds hefty amount of confidential data which needed to be secured. Most Common vulnerability threat in Website hacking is Cross Site Scripting. Around 40% attacks on Websites globally occurred due to XSS (Cross Site Scripting). Cross Site Scripting targets both vulnerable and non-vulnerable website. To protect and prevent the website from XSS we must know the complexity and different ways of prevention. However, the research of finding the optimal way of detection of Cross Site Scripting is still in progress. XSS attacks can also be used to steal user information, such as cookies or session tokens, which can be used to gain access to the victim's account. XSS can also be used to inject malicious code into a page, which can be used to redirect the user into the malicious sites, or to execute malicious code on the user's machine. However, in this paper we will be focusing on XSS attacks and its types. In addition, this paper will introduce some techniques to prevent user's data from XSS attacks.
Speech Recognition Using Quantum Convolutional Neural Network

Quantum machine learning (QML) has emerged as an exciting new technology that explores challenging machine learning issues by relying on advances in quantum computing. A convolutional neural network is primarily used in the classical portion of the QNN-based Speech Recognition System, which is made up of both classical and quantum components. The quantum component is based on the equation’s quantum circuits with a certain learnable parameter. Speech recognition technology is one of the quickly evolving technologies, along with Siri on the iPhone, chatbots, Alexa on the Amazon Echo, etc… The research uses a quantum convolutional neural network (QCNN) to identify voice and convert it to text. The use of Quantum filters will reduce the input in to qubits and then it will be further moving in to the feature extraction and classification process. QCNN gives more accurate results and will increase the scalability by adding more layers in to the model.
Improving Prediction Accuracy in Drift Detection using LR in Comparing with Modified Light Gradient Boost Model

The goal of the proposed work is improving prediction accuracy in drift detection using Logistic Regression compared with modified light gradient boost model. The collection of 40 samples were taken by varying test and training data set size. These samples are divided into Two groups (Group 1 - Logistic Regression, Group 2 - Modified Light Gradient Boost) each having 20 samples and the accuracy was calculated to quantify the improving prediction accuracy in drift detection using LR and modified light gradient boost model. The G power is taken as 80%. The results for the simulation is 69% accuracy of LR, and the LGBM provides results with an accuracy of 98%. For the given dataset LR performs significantly less than the MLGBM in the prediction.
Comparison of Novel Optimized Random Forest Technique and Support Vector Machine for Fraudulent activities in credit card Detection with Improved Precision

The objective in the research work is to determine fraudulent activities in credit card using Novel Optimized Random Forest Technique (NORFT) algorithm with comparison of Support Vector Machine (SVM) algorithm by improving the precision. The study contains two groups, i.e Novel Optimized Random Forest Technique Algorithm is developed in the first group and Support Vector Machine is developed in the second group. To categorize data, a sample size of 181 per group was used with a g-power of 80%. The data was collected from various recent studies on the web, using a threshold of 0.05%, a confidence interval of 95%, and mean and standard deviation measurements. A new algorithm called NORFT was compared to the SVM, and NORFT was found to have significantly higher precision at 92.52%, compared to SVM Algorithm's precision of 62.82%. The significance value (p>0.05), which was determined to be p=0.352, indicated that there was no significant difference between the two algorithms. However, NORFT was found to be more accurate in predicting fraudulent activities in credit card and was able to improve precision levels compared to SVM.