Reviewerly/exHarmony huggingface dataset:
An Improved Real-Time Sign Language Recognition using Transfer Learning

For decades, many researchers have been working on finding efficient solutions to implement sign language recognition systems through various technologies. Sign languages are visual languages that communicate through the hand, facial expressions, as well as body movements. Sign language is the bridge that connects us to the world of those who have impaired hearing or verbal ability. It allows them to understand the world around them through visual descriptions and, as a result, contribute to society. Thus, It strikes our mind to bridge the gap between the hearing impaired and normal people & make communication easier. Many researchers faced many challenges such as long training times to train a deep neural network model such as CNN or RNN, huge datasets of millions of images, high computational power requirements, the usage of expensive sensory devices, etc. Hence, the proposed method in this paper uses a technique known as “Transfer learning” where we use a high-accuracy pre-trained model and fine-tune its final layer parameters according to our task that is, Sign Language recognition which significantly reduces the training time and requires a small set of data of less than 20 images per gesture. Thus in this paper, we created a custom dataset generated using the system webcam, labeled data using the LabelImg tool, used Tensorflow Object detection API and downloaded a pre-trained model named “SSD MobinetNet V2” and applied Transfer Learning. Challenges faced in the project were issues with the correct labeling of images, a few installation errors, and lighting and background issues in a few detections. Overall, the method proposed is highly efficient in saving a lot of training time, providing high accuracy, and using fewer images for training.
Spam based Email Identification and Detection using Machine Learning Techniques

E-mail is a popular means of communication through the internet and is widely used by everyone in their daily lives. E-mail usage is increasing at an exponential rate, and this increase is leading to an increase in spam mail. Spam email is an email that contains unnecessary and sometimes malicious data and is sent in large quantities. Because of spam emails, there is a possibility of fraud using this type of email. People who are not aware of these kinds of frauds may get into trouble due to these spam emails. So, it is necessary to identify those types of messages through the use of techniques and methodologies of machine learning. Machine learning will be used and applied in this research to select the best algorithm that correctly detects spam with highest accuracy.
Cyber Security and Frameworks: A Study of Cyber Attacks and Methods of Prevention of Cyber Attacks

Cyber Security is an essential aspect for businesses as the threats that cybercriminal activities are increasing daily, and the importance of cyber security is also overgrowing. There is thorough planning needed for robust and resilient implementation of security measures in cyber systems. This research proposal is also being conducted for information about cyber security and the megastructure of the current frameworks. The study also observes various cyber threats and the activities of cybercriminals who can steal essential data from any system. Robust cyber security needs to be implemented for a resilient system that absorbs shocks while withstanding risks such as data breaches and theft. Furthermore, an analysis of this cyber security is done by way of a literature review from secondary sources with the evaluation of the importance and advantages of the significance. Furthermore, the types of cyber securities are being explained and discussed, bringing about the discussion on the significance of models of cyber security. A brief description of those cyber security models is also included with this research proposal for discussion. The analysis of cyber security, as a project, can be precious for studying the cyber security systems
A Secure and Efficient Product Analysis for Trust based E-Commerce Application

In current situation, e-commerce is considered essential and a growing business in day today life. People are buying the product based on deals and offers provided in the ecommerce application. Customer does not know about the quality and authenticity of the product, and sometimes the seller does not meet the customer requirements. To avoid this situation, a Secure Trust Monitoring KNN (STRUMKNN) ecommerce model has been implemented in this research. This model consists of a customer trust reputation system based on feedback comments and rating system given by each client. This model helps customer to find the good seller in ecommerce sites based on trust weight to find the product quality and product delivery. In the proposed model, two techniques such as word separation method and sentiment analysis are employed for providing trust profile to the customer. Here, based on the key pair of words either positive or negative given by a customer in the comments section, the suggestion is given to the buyer to order the product on trust profile. Sentiment analysis helps to identify if the customer ratings given by the user are accurate or not, based on the ratings and trust. The experimental results prove that the sentimental assessment used in the suggested model provides customers a successful way of finding the product and increases the confidence of e-commerce application users.
Blockchain based Mechanism for Cloud Security

As cloud-based data storage becomes increasingly prevalent, data breache risks and compromises of data grows. Data can be altered, and malevolent actors could take advantage of this. Clients require cloud services for different variety of applications. Cloud Service Providers ensure that the client data is correct and trustworthy. To address the issue, this paper proposes a solution that utilizes encryption and data verification to enhance data integrity and reduce vulnerability through the use of blockchain technology. Specifically, the proposed approach involves encrypting data and storing it on the Ethereum blockchain, allowing clients to quickly determine whether their data has been tampered with before the cloud service provider becomes aware of any potential security breaches. Even in cases where the data has been compromised, the proposed system enables clients to auto-verify data integrity, providing greater peace of mind and enhanced security. Overall, the proposed solution represents a promising approach for enhancing the security and integrity of data stored in cloud, and could be of significant value to individuals and organizations alike. This paper outlines the standards of data security and its ground-level implementation. The suggested system relies on a back-end service that uses a private cryptocurrency and does not require any advanced setup procedures. A wallet setup for the selected coin by the client is needed.
Using Machine Learning to Identify Instances of Cyberbullying on Social Media

Bullying has moved online as a result of the technological revolution, which was previously limited to physical boundaries. One type of cyber bullying is ridicule or insult. According to the report the cyber bullying on social media is getting worse. Insulting words change over time, and the same word can mean different things depending on the situation. A comment cannot be considered bullying simply because it contains such a word. Therefore, simple keyword spotting methods are insufficient for labelling comments. Lexical databases like Word Net, which provide synonyms and homonyms for words, have been utilized in other languages to address this issue. It is difficult to identify a word as bullying because there is no English-language lexical database. As a result, the proposed work solved the problem by following the rules. Outliers were removed from the collection of tweets containing profane language, and the remaining tweets were pre-processed. Five feature extraction rules were used to find insults in the text. The Support Vector Machine (SVM), K-nearest neighbor (KNN), and Naive Bayes algorithms were then utilized. With F1-score of 91 percent, the findings demonstrate that SVM along with an RBF kernel performs better. The fact that this research focuses on English-language cyberbully detection is novel and has not been done before.
Detection of RBCs, WBCs, Platelets Count in Blood Sample by using Deep Learning

A whole blood cell count is an essential check in scientific diagnosis to assess common fitness conditions. Blood cells are traditionally counted manually using a cell counting chamber (hemocytometer) in conjunction with various laboratory compounds and solvent (chemical) compounds, which is time-consuming and very dull to experiment with. In this work, a machine learning system mastering the method of automated computerized counting of blood cells is proposed. By using machine learning and deep learning techniques, the blood cells and their counts can be identified with the best accuracy compared to the other existing techniques. The CNN is used for the image classification. One of the best techniques for achieving the best accuracy in the least amount of time for the blood cells dataset is VGG-16 technique. The proposed system is a combination of the CNN and VGG-16 methods. The learnt models are generalized for the training and testing of the different datasets. In general, this computer-aided system of detecting the blood cells is more useful for practical applications. According to the results of this research, the accuracy of counting blood cells is in the range of 90 to 95 percent.
RECS Methodology for Secured Data Storage and Retrieval in Cloud

Different web management strategies are used in the cloud computing deployment to address various issues. Here, information security and protection has developed into a significant issue that restricts a lot of cloud applications. In tandem with the growth of cloud storage’s benefits, the drawbacks of cloud data have also grown. This has an impact on the cloud-based information servers that are used to store data. further used for exchanging jointly massive amounts of data using DFS. The cloud computing foundation’s information exchange processes are defenseless against several assaults, for which multiple cryptographic approaches were unable to provide a workable defense. Reliable Efficient Cloud Storage Model is a novel approach. With the Blowfish approach, the suggested algorithm is applied to this model. When compared to the current technique, experimental results showed that the RECS approach is effective in storing and transmitting the encrypted data.
Comparison of Various Face Recognition Algorithms in ML/DS

Face recognition software is a piece of computer hardware that employs several algorithms to recognize faces in digital photos, identify people, and then confirm the authenticity of the acquired images by comparing them to facial images that have already been saved in a database. The field of computer vision includes the essential problem of facial recognition, which has been extensively researched by several researchers. This topic is crucial, particularly for specific applications like surveillance technology. This survey article’s main objective is to compare the various facial recognition algorithms.
NLP based Analysis and Detection of Unethical Text

In the present scenario, the internet has enabled everyone to express their opinion. As every coin has two sides, it paved a way for increased negativity and intolerance. In this context, unethical text includes offensive comments and hate speech targeting a group or an individual based on their characteristics such as race, religion or gender and that may threaten social peace. So, the unethical text should be eliminated before it reaches a user where identifying an immoral text plays a pivotal role. Various methods are used in identifying a text, one such method, widely known as Natural Language Processing (NLP) is used to perform text analysis in the process of detecting the unethical text. NLP enables us to perform tokenization, embedding matrix, padding and identification of semantic relationships, which helps to analyse the text data efficiently. Different algorithms were used to detect the unethical text, this study has used ISTM and Bi-LS TM to identify whether the user’s statement is offensive or not. The better and accurate model is considered and a web page is built on it. It predicts the offensiveness of text present in user input. In this way, the proposed model detects the unethical text on the web, which can be used to eliminate unfiltered comments. The best model is achieved by using the Bi-ISTM with an accuracy of 86.4%.
Application of Blockchain Technology in the Healthcare System

Healthcare is a sector that generates a large of amount data. This hefty data contains medical data related to patients and electronic health records of a patient, this data is becoming more vulnerable to Cybercriminals in the dark web. All this data is being exposed in the dark web which results in the vulnerability of healthcare industry. In order to protect the data, there a lot of techniques and methodologies that are chosen, which turned out be more expensive in terms of maintaining them. In the healthcare sector, where it has greatly The adoption of blockchain technology is going through a conceptual transformation in terms of increased effectiveness, access control, technological innovation, privacy protection, and security of data management processes transformation. In blockchain technology, out of three architectures, the private network, which ensures closed network and enable better security will be selected. The patient can access data with the help of private key.
Secured and Structured Data Storing and Retrieval in Cloud Computing by using TPA

The issue of security for the data that users store in the cloud is very important. The cloud service provider may provide some data security, but there may be serious issues regarding data security between the user and the service provider. An auditor hired by a third party can resolve such problems. This study has examined various approaches to enable cloud data storage security. However, when a Cloud Service Provider possesses both the encryption and decryption keys, there is a risk to data security and privacy. User data may be transmitted by CSP without the user’s permission. For auditing, this study has introduced a Third-Party Auditor. At the user’s end, the data will be encrypted before being sent encrypted over the network to TPA. Before storing the data in the cloud, TPA will verify it. There are a lot of cloud computing users who access and modify data, so they need a dependable service provider that can protect their data completely. As a result, the client’s data will be subjected to an audit by the TPA to ensure its integrity. As a result, the user will have a more comprehensive view of data privacy. This study offers a solution for users, who require data privacy and security.
Facial Landmark-based Cursor Control and Speechto-Text System for Paralyzed Individuals

Computers are crucial in today’s world, but disabled individuals, particularly those who are paralyzed, may face challenges in using them. Paralysis can limit movement to just the eyes, head, and voice, making computer usability difficult without others assistance or help. Few techniques have been developed to improve cursor control systems, such as voice control and gesture control. However, some users have found these methods to be inconvenient or challenging to use. A new model has been proposed that eliminates the need for assistance. It identifies specific facial characteristics, or landmarks, that allow for the control of cursor movement and clicking events without requiring direct physical contact with the user. The system uses eye gaze and facial movements to achieve this. An Integrated Speech-to-Text system is also incorporated into the cursor control system to enable textual interpretation. The system features a user-friendly interface that makes it easy to use and provides a better user experience.
Blockchain based E-Voting Application – A Survey

Voting is a crucial component of daily living in the modern world. Everyone belongs to social groups, which might vary in size, and in the majority of them, voting has a significant impact on key decisions. Technology advancements have simplified the voting process. Present voting system has lot of issues concerning to transparency, privacy etc., To solve these issues, both the polling and counting of ballots can be done using blockchain technology. In-depth information is provided on the development of the BlockVote electronic voting system. Application of Ethereum technology in e-voting leads to scalability issues. POW (proof of work) mining concept leads to inefficient time management operation. This study has proposed a Hyperledger fabric framework for the implementation of e-voting application. The Hyperledger Fabric network is a middleware program and an interface that make up the system. Both of the smart contracts: EVotingContract and CodesContract are a part of the Hyperledger Fabric blockchain. The connection between the blockchain network and the user interface is made possible by a gateway application.
Towards Minimal Training for Low Resource Languages

The advancement of research in the field of natural language processing has made peoples’ daily lives much easier, with numerous applications at the disposal. The traditional methods like the Hidden Markov Model, the CRF classifier, the Naive Bayes classifier, and others are being replaced by neural networks in recent times. However, most of these methods work considerably well only with huge amounts of training data and, hence are not suitable for languages that are poor in terms of trainable resources. The challenge is to make the system work considerably well with minimal training. This paper presents research work to understand the effect of training size for part of speech tagging, which is one of the preliminary tasks for any NLP application. Experiments are conducted to understand the training size required for standard techniques to perform with high accuracy. The results of the experiments conducted for English and Assamese are presented in this paper.
Twitter Sentiment Analysis

This study focuses on real-time Twitter sentiment analysis using Streamlit, TextBlob, and Tweepy, in order to gather and analyze data from Twitter and understand the sentiment of a particular topic, brand, or event. The study presents an efficient and scalable method of collecting tweets in real-time and analyzing their sentiment using TextBlob, a Python library for processing textual data. The results of the analysis are presented in an easy-to-understand format through a web-based dashboard built with Streamlit, allowing users to track the sentiment of a topic over time. The analyzed data, i.e. positive, neutral, or negative sentiment, is represented in graphical format on the dashboard, providing users with a visual representation of sentiment trends. The study also demonstrates the usefulness of such analysis for businesses, marketers, and researchers in understanding customer sentiment, identifying trends, and improving decision-making.
Enhancing Cloud Storage Security with Intrusion Detection System using CNN and Grey Wolf Optimization Algorithm

Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. This research article proposes a novel approach for enhancing the security of cloud storage systems by developing an Intrusion Detection System (IDS) that utilizes feature extraction, pre-processing, and advanced analysis tools. Specifically, the proposed IDS leverage a Convolutional Neural Network (CNN) model and a grey wolf optimization algorithm to detect potential cyber-attacks on cloud storage. The research work focuses on extracting relevant features from the data and performing preprocessing to remove any irrelevant or noisy data points. The CNN model is then trained using the pre-processed data, while the grey wolf optimization algorithm is engaged to optimize the runtime performance of the IDS. The outcome of the proposed approach demonstrates its ability to detect various types of cyber-attacks with high accuracy and efficiency, which can help to improve the security of cloud storage systems. By measuring the effectiveness and efficiency of the algorithm, it can be determined whether GWO is a suitable optimization algorithm for intrusion detection in cloud security.
A Survey on Movie Recommendation System by using Machine Learning Algorithm

People’s desires, trends, and interests change as the world changes. Similarly, in the realm of cinema, individuals prefer to watch movies based on their interests. Many digital cinema service vendors have developed, and their aim is to keep every member entertained to grow their company and popularity. To develop their firm, the content provider should offer movies that their customers would enjoy, so that they will keep watching their upcoming films. Customers are likely to extend the web-based movie network operator application on a regular basis if this is done. The goal of this work is to create a Machine Learning algorithm based on XGBoost using collaborative filtering-based movie system that will suggest cinema to all users depending on their choices and evaluations. In order to achieve this, information screening is utilized to recommend films based on genres across movies, and a filter is applied to compute genre depending on the subscriber and offer film data. To boost performance, the suggested system employs the latest learning approach, XGBoost. The findings demonstrate that the proposed method is successful for movie suggestion, and it reduces Root Mean Square Error.
Autonomous Vehicle Image Classification using Deep Learning

High-performance computer systems, Deep learning algorithms, and sensors work together to enable autonomous driving. First, information about the surroundings around the vehicle is gathered via sensors like lidar, radar, and cameras. Then, deep learning algorithms evaluate this data to determine how the car should react, such as with steering or braking. These computations are done in real-time using high-performance computer devices There is ongoing research towards the creation of autonomous vehicle image categorization systems, however the systems confront several difficulties. In recent years, several approaches—including the application of deep learning algorithms and the incorporation of sensor fusion technology have been suggested to solve these problems. The precise and effective categorization of photographs taken in actual surroundings continues to present difficulties. The requirement to analyse massive volumes of data in real-time, which necessitates powerful computation and effective algorithms, is one of the key obstacles. In this paper we include the idea of detecting vehicles and things on the road, such as vehicles and lane detection, which can be used for autonomous vehicles. The main objective to achieve this our proposed system includes a popular algorithm YOLO (You Only Look Once) YOLOV3. By including the concept of detecting objects using YOLOV3, better results can be obtained by increasing the grid size to $26*26$. This extended grid size helps to detect the smaller objects.