Reviewerly/exHarmony huggingface dataset:
Latent Dirichlet Allocation Model for plurilingual Language Opinion Classification in Social media

The increased use of social media in public discourse has allowed sociologists to engage in exciting new areas of research. As a result, public arguments based on political, economic or philosophical perspectives have a greater impact on society. However, this open environment has also led to the emergence of shaming opinions, which involve derogatory language used to humiliate or demean others. Detecting and classifying shaming opinions in multiple languages is crucial for maintaining a respectful and safe online community. In this study, we propose a novel approach for shaming opinion classification in multi-lingual languages using the Latent Dirichlet Allocation (LDA) algorithm. A novel multilingual text dataset is employed for multi-class categorization in the proposed system. The LDA algorithm is employed to model topics within the text, enabling the identification of latent semantic structures associated with shaming opinions. By analyzing the distribution of topics and their corresponding linguistic features, we build a robust classifier capable of discerning shaming opinions from non-shaming ones in different languages. In order to improve the recognition and tracking of shaming opinions or hate comments, datasets have been analyzed and discussed in a variety of categorization scenarios.
Safeguarding Public Spaces: Unveiling Wallet Snatching through Edge Impulse Technology

In contemporary society, public space security and safety are of utmost significance. The theft of wallets, a frequent type of street crime, puts people’s personal items at risk and may result in financial loss and psychological misery. By utilizing Edge Impulse technology to identify and expose wallet-snatching incidents in public areas, this article offers a fresh solution to the problem. To develop a reliable and effective wallet-snatching detection solution, the suggested system blends machine learning techniques with the strength of the Edge Impulse platform. This study used Spiking Neural Networks (SNNs) which are inspired by the biological neural networks found in the brain. Edge Impulse offers a thorough framework for gathering, preprocessing, and examining data, enabling the creation of extremely precise machine learning models. The system can accurately discriminate between legitimate interactions with wallets and suspicious snatching attempts by training these models on a dataset that includes both normal and snatching events. The efficiency of the suggested method is 95% demonstrated by experimental findings, which show high accuracy and low false positive rates in recognizing wallet snatching instances. Increasing public safety, giving people a sense of security in public places, and discouraging prospective wallet-snatching criminals are all goals of this research.
Pictoquizezz: Engaging Cognitive Learning

In the era of virtual interactions, finding innovative ways to engage participants in activities such as quizzes can be exciting. One captivating approach is hosting a virtual quiz without relying on keyboards and a mouse but solely utilizing webcams, active internet, and hand gestures. The virtual quiz utilizes computer vision algorithms to detect and track hand gestures through participants' webcams. By mapping specific gestures to different quiz actions, participants can select answers and interact with the quiz interface without the need for traditional input devices. Participants are presented with a series of questions displayed on the screen, and they can indicate their chosen answer by making specific hand gestures. Monitor the participants' webcam feeds attentively to identify their hand gestures. Provide immediate feedback on the accuracy of their responses, indicating whether they are correct or incorrect. This feedback mechanism ensures that the quiz serves as an educational tool, promoting a continuous learning experience. The use of AI in virtual quizzes can enhance the experience by providing personalized learning time-saving quiz generation, instant feedback, and adaptable difficulty. By leveraging webcam technology and hand gestures, this virtual quiz offers a novel and immersive approach to quiz participation. The virtual quiz employs computer vision algorithms to identify and monitor hand movements using participants' webcams. This innovative concept aims to create a fun and engaging experience for participants, encouraging active participation and knowledge retention through gesture-based interactions.
Feature Based Training for Crime Detection using Deep Learning Techniques

Today’s surveillance systems produce a massive amount of video data every day, making it challenging for computer vision professionals to analyze it. Since they happen sporadically and with a low likelihood in actual surveillance, manually searching for odd events in these enormous video feeds is a difficult task. The public is better protected by anomaly detection using deep learning, which has more reliable detection capabilities and minimizes human labor. The framework for effective features-based intelligent anomaly detection presented in this research can function in video surveillance networks with a low time complexity. In the given framework, we take a set of frames in sequence and extract spatiotemporal properties from them by sending each frame to a pretrained Convolutional Neural Network (CNN) model that has already been trained. The collected features from the series of frames are useful for identifying unusual events. Then the Bi- directional Long Short-Term Memory (Bi- LSTM) model is used to classify anomalous and normal occurrences in various surveillance scenes once we pass in the feature maps that were retrieved.
Education Revolution using Virtual Reality

The development of a Web 3.0 education platform that leverages Virtual Reality (VR) videos, cryptocurrency payments, and Artificial Intelligence (AI) chatbots enhances the learning experience. The paper aims at providing interactive learning for users using a decentralized platform. This Web application runs on the latest concepts like Blockchain and Machine Learning. The AI-supported chatbot uses the latest OpenAI concept to answer users’ queries. The platform allows users to buy courses using cryptocurrencies through the Metamask wallet and also enables material authors to earn profits built on the count of views their material collects. The AI chatbot, powered by OpenAI Application Programming Interface, provides free support to users by answering their queries related to studies. Upon payment, users receive a token code via email that they can use to access the video content through a mobile app with a VR box. The platform aims to democratize education by making it accessible and affordable for learners worldwide. The use of blockchain technology and virtual reality in education is still in its early stages, and this platform has the potential to revolutionize the education sector and make learning more accessible, engaging, and personalized.
Investigating Supervised Machine Learning Methodologies for Preventing Phishing Attacks on SCADA Server

A high number of industries utilize supervisory control and data acquisition system for controlling and monitoring day-to-day operations related to the organization’s domain with digital methods and accuracy to boost the overall infrastructure. These organizations are extensively involved in sending and receiving crucial data from multiple nodes. These systems can become the target of Phishing Attacks that is used to steal the private data of the user. Attackers steal the user's important credentials like user login and password involved in these organizations. The SCADA has seen an increase in Phishing activities over the past ten years as well. To overcome these phishing attacks machine learning techniques can be trained in a prior manner for detecting these attacks in the organization. Machine Learning algorithms can examine the details of each packet by analysing traffic patterns. In this research paper, the proposed model works on preventing SCADA server attacks using artificial intelligence-enabled machines at an advanced stage. The more related three machine learning algorithms are explored for the classification of data packet and these are decision tree, k-nearest neighbors and linear regression for detecting Phishing Attacks on the SCADA system. The dataset included in the experimental setup is the interpretation of data packet traffic features. This paper demonstrates the promise of these algorithms for more research in this field and offers interesting details on the application of machine learning in spotting dishonest conduct inside SCADA phishing attempts.
Design and Implementation of a Comprehensive Intelligent E-Learning Systems Integrated Web App Solution For Dyslexic Students

The prevalent learning disorder, dyslexia impair a person's capacity for reading, writing, and spelling. But with the aid of successful interventions like multisensory techniques, phonics training, and assistive technology, persons with dyslexia can improve their reading and writing skills and excel in school and in daily life. This research paper "Dyslexi Duo" is a comprehensive solution for dyslexic students in all age group and includes 3 major sections. A Super Pad for the age group >18 to help them read digital documents like pdf with ease. An Interactive & Educational game -Hydra Hunt for the age group of 4-10 to learn formation of words with letters using both visual & auditory techniques. This game incorporates 4 key elements: storyline, clear goals, multiple levels, and points. An attached mobile application "Alpha360" for the age group of 3-8, which is an interactive multimedia learning app using 3d viewing, TextToSpeech, SpeechToText technology & a multisensory approach. This entire application was tested & evaluated with dyslexic students of the corresponding age group. Overall, our application can be a one stop solution to help dyslexic students overcome theirchallenges at an earlier age and achieve academic success.
Robust Attack Identification Strategy to Prevent Document Image Forgeries by using Enhanced Learning Methodology

Forgery in images is a long-standing issue that spans back to ancient times. Throughout history, images have been employed as evidence or documentation of events. In the present day, the accessibility of image preprocessing tools and affordable hardware has facilitated the creation of manipulated images with ease, often driven by ulterior motives such as disseminating false information or pursuing personal gains unlawfully. In recent years, the spotlight has turned to Artificial Neural Networks (ANNs), sparking significant interest in the realm of image forgery identification. Nevertheless, prevailing image forgery techniques rooted in ANNs are often specialized, geared towards identifying distinct types of forgeries. Consequently, a pressing need arises for an approach adept at efficiently and accurately uncovering unfamiliar forms of image manipulation. This research fills that gap by presenting a robust deep learning-based system designed specifically to spot fakes while working with double image compression. The model's training hinges upon discerning disparities between an original image and its recompressed iterations. To enhance the model's performance, the Ant Lion Optimization (ALO) technique is employed to fine-tune the features of the input data. Noteworthy for its efficiency, the proposed model stands out as a lightweight solution. Empirical results underscore its prowess, showcasing swifter effectiveness compared to prevailing state-of-the-art technique. Encouragingly, the model achieves an impressive overall validation accuracy of 95.87%, reinforcing its efficacy in identifying image forgeries.
Fake Product Detection Using Blockchain Technology

The rise of counterfeit products poses a significant challenge for manufacturers, affecting their brand reputation, sales, and profitability. However, blockchain technology provides a solution by ensuring the identification and traceability of genuine products throughout the supply chain. Blockchain is a decentralized digital ledger that securely stores transactional data in interconnected databases. Leveraging blockchain for product authentication and counterfeit detection offers a tamper-proof system. One effective approach is to utilize Quick Response (QR) codes linked to the blockchain. QR code scanners verify product authenticity by comparing the scanned code with entries in the blockchain database. If a match is found, customers receive confirmation of the product's genuineness. If no match is detected, customers are promptly notified of a counterfeit product, allowing manufacturers to address the issue. Counterfeit products not only harm manufacturers but also jeopardize consumer safety, revenue, brand reputation, and trust. Establishing a reliable system to detect counterfeit products is crucial. Blockchain-based systems provide a decentralized and secure platform, ensuring product authenticity and mitigating counterfeiting risks. By leveraging blockchain and QR codes, manufacturers can protect their brands, boost consumer confidence, and maintain a secure supply chain.
Smart signal control by detecting traffic intensity using IR Sensors

The Population increase in the countries like India is increasing Day-by-Day. The Traffic in the roads of the city is also becoming worse in some cases. The people are wasting their valuable time on road and leaving many important works because of this heavy traffic. This waiting time in the Traffic causes time wastage, wastage of fuel, damage of environment. In so many cities people are complaining that they cannot reach their destination on time because of this heavy traffic situation on every places in the city and Accidents are also increasing on the roads due the Traffic conditions. The proposed study presents an approach to solve this traffic problem by Traffic Signal Automation by sensing traffic intensity by IR sensors using IoT (Internet of Things). So The Proposed system can control the traffic by adjusting the timings of the signals based up on the calculations of Traffic intensity on all the sides of the roads. It provides a better Traffic Signal control. These IR sensors are placed on the all sides of the roads to detect the data of the Traffic congestion. So this way we can control the traffic in many kind of situations.
Classifying mammographic images for predicting breast cancer using CNN

In the whole world, breast cancer is among the most common and dangerous diseases affecting women’s health. For the identification of breast cancer to be effective and for patient outcomes to be improved, it must be done efficiently and precisely. The development of computer-aided diagnostic systems utilizing deep learning approaches has gained popularity as massive mammographic imaging datasets have been more widely available. In the suggested study, convolutional neural networks (CNNs) are used to categorizes breast cancer in great detail. CNNs are a viable method used for classifying breast cancer since they have demonstrated great effectiveness in a variety of medical image recognition tasks. The proposed methodology covers the most recent innovations, approaches, and difficulties related to CNN-based breast cancer categorization and has been tested on various parameters and achieved results as good as 98 percent the receiver operating characteristic curve's (AUC-ROC) area under the curve.
Searching for a needle in a haystack: Small Traffic Sign Detection in Large Images

A crucial component of driver assistance systems and self-driving automobiles is the identification of traffic signs. Traffic detection plays an important role in most sectors, like urban planning, transport management, and driving systems. The main issue is that in photographs taken from the road, traffic signs appear substantially smaller. Only 1% to 2% of the entire image area is covered by it. Therefore, it can be difficult to see a tiny traffic sign in an image with a wide background of items with similar shapes. As a result, to detect small traffic, it is suggested to use YOLOv3 network layers. This helps to raise the mean average precision and recall percentages. In order to get the best anchor set for the dataset, this work also provides an anchor box algorithm that will make use of bounding box dimension density. This lowers the average miss rate and false positive rate. This research paper provides a suitable approach to detecting minute traffic signals in a large image. The detection process goes through various steps, including managing the large image to an appropriate size to reduce the complexity and enhance the picture quality. Various algorithms are applied to it, like YOLO and CNN. This proposed approach is demonstrated by various experimental results. The suggested method is tested against Swedish and German traffic sign datasets.
Improving Action Recognition through Pose Estimation and Directed Graph Neural Networks

To improve the accurate performance and robustness of action recognition systems, this study attempts to merge the posture estimation and skeleton-based action recognition. While pose estimation provides a spatial representation of important places on the human body, skeleton-based action identification focuses on examining the time dynamics of human motions. By combining these two methods, we increase the performance of action recognition by utilizing the complementary data from both the spatial and temporal domains. Through empirical evaluation on benchmark dataset, this study shows the efficiency of alternative approaches for fusing posture estimation with action recognition. The outcomes show that this integrated method has the potential to attain cutting-edge performance in identifying human actions.
Recognition of Indian classical dance poses in multi head attention learning framework

The goal of this work is to develop a deep Indian classical dance classifier on online bharatanatyam videos to assist amateurish dance seekers. Previous learning models demonstrated that the global feature representations of dance poses in videos with unpredictable backgrounds have unreliable performance metrics.Therfore, tiny dance datasets with few dancers in controlled environments were used rather than recording from live performances. In this work, the random pixel distributions of the dancer in the online videos with cluttered background are being emphasized using multi frame multi head layer attention (MFMHLA) on deep ResNet features at different resolutions across deep layers. This results in a chronological enhancement of the pose at multiple resolutions across the depth. The experiments were conducted on our online bharatanatyam ICD, BOICDVD22 with 10 songs. The results conclude that the presence of MFMHLA has improved pose feature representations of online dance videos burdened with deformations.
Protecting the Secrecy of the Video Using Advanced Data Hiding

We propose a advanced video hiding technique to protect the data which need to be secure so that the piracy and the security of the data is maintained without falling into wrong hands. The message or data such as text , audio , video and files are embedded into a video .The video is of Audio Video Interleave (AVI) format and the data is embedded using h.264 algorithm , slicing and macro block technique. The video is secured using encryption and decryption using Advanced Encryption Standard (AES) algorithm. The sender secures the video using a key , the receiver can retrieve the data only when the key is entered thus by protecting the data. This method is used to transfer information securely by hiding the data in a video, the third party doesn't know that there is a data hidden in the video , they look just like a normal video without any traces of the data hidden inside it.
Exploring Active Machine Learning Techniques to Boost Classification Accuracy in Image and Text Models

This study provides a comprehensive analysis of active machine learning algorithms designed to improve the classification precision of image and text models. This study introduces the ALiPy (Active Learning in Python) library, which provides a platform for readily integrating active learning algorithms into classification models. The proposed method enhances model performance and reduces annotation costs by selecting informative cases for annotation using the query-by-committee (QBC) procedure. The technique’s efficacy is evaluated utilizing the AG News text dataset and the CIFAR-10 image dataset. Experiment results indicate that using active learning strategies significantly enhances classification accuracy in both domains. The ALiPy library can be used by academics and professionals to research and evaluate active learning methodologies. This research advances the implementation of active learning techniques and demonstrates how this can improve the classification accuracy of image and text models. The findings demonstrate the importance of employing active learning strategies to maximize data annotation efforts and enhance the functionality of machine learning models in real-world settings.
Experimental Evaluation of Robust Cyberbullying Detection over social media using Intelligent Learning Scheme

As the Internet has expanded, so too has the popularity of social media, which has become the preeminent means of communication in the modern day. While this improvement has helped people become more connected with one another, it has also led to some undesirable side effects. These include the emergence of harmful phenomena like cyberbullying, cybercrime, and online trolling. The negative impact of these issues cannot be underestimated, with instances of cyberbullying leading to severe mental and physical distress, particularly among vulnerable groups like women and children, sometimes even pushing them towards suicide. Similarly, the impact of online harassment has garnered significant attention due to its profound negative influence on social well-being. In this study, significant effort has been invested in reviewing prominent classification algorithms with a dual purpose: understanding their capabilities and limitations, and proposing an innovative approach to address the issue of cyberbullying. This approach employs Twitter datasets as a basis for detection. The proposed ensemble model constitutes a fusion of two distinct classifiers: the Decision Tree Classifier and the AdaBoost Classifier. The Proposed Model for cyberbullying identification demonstrates remarkable prowess. Its exceptional sensitivity score of 98.26% underscores its ability to accurately pinpoint positive cases, effectively identifying instances of cyberbullying.
Cloud Computing Pattern Identification with Machine Learning: A Study

Delivering needed computer resources over the Internet has made cloud computing an increasingly important requirement. Automated methods to find broad trends in cloud systems' usage and behaviour are becoming increasingly necessary as they get more complicated. The artificial intelligence subfield of machine learning provides an intriguing solution to this issue. The methods for detecting performance abnormalities, forecasting resource usage, and optimizing cloud system configurations are the main topics of this paper's review of recent work on machine learning for general pattern recognition in cloud computing. We also talk about the shortcomings and difficulties of the current methods and offer ideas for further research in this field.