Reviewerly/exHarmony huggingface dataset:
Deepfake detection for preventing Audio and Video frauds using Advanced Deep Learning Techniques

Deepfakes, powered by generative adversarial networks (GANs), automate the creation of deceptive videos. To combat this menace, the study assembles an advanced model that fuses ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, renowned for their efficacy in handling visual and temporal aspects, for the detection of deepfakes in audio and video content. Pre-processing, facilitated by a Multi-Task Cascaded-Convolutional Neural Network (MTCNN), accurately extracts facial regions. The model undergoes rigorous evaluation across three key datasets which are, FaceForensics++(FF-DF), Celeb-DF, and Facebook Deepfake Detection Challenge (DFDC), affirming its real-world readiness and exceptional accuracy. The combined models consistently deliver highly precise results, maintaining their robustness against evolving deepfake technologies.
A Comprehensive Review of Frameworks for Achieving Interoperability in Multi-Cloud Environments

The objective of this review is to investigate and outline a thorough Multi-Cloud Interoperability Framework (MCIF) to solve the difficulties in attaining seamless interoperability in multi-cloud systems. In the context of multi-cloud, interoperability refers to the capacity of several cloud service providers to work together effortlessly, enabling customers to deploy applications across various cloud platforms without running into compatibility problems or having to make substantial performance sacrifices. The suggested MCIF includes all the crucial elements and features required to build a solid and flexible framework. A unified and successful cloud computing experience for consumers is ensured by the MCIF, which makes use of a shared cloud ontology to facilitate optimal communication and resource utilization across various cloud providers. The overview paper covers the stages necessary to accomplish interoperability in a multi-cloud context and emphasizes the importance of each component, offering helpful insights for researchers and practitioners looking to create practical solutions for multi-cloud interoperability.
Bilingual Translation Using a Novel Framework

A system of written and spoken communication that is used by the people living in a particular area or nation is termed as a language. It is composed of many sounds and symbols. A multitude of languages are spoken in India, one of which is Tulu. Given that many Indians, especially those in remote farming communities, lack English comprehension, literacy, or fluency, there's a need for a proficient language translation mechanism. The proposed model aims to develop an innovative system to translate from English to Tulu. This system will aid residents in surmounting communication obstacles they face during every day routines. The anticipated system receives English speech as an input and generates Tulu speech and text as the output. The model integrates bilingual transformation. Various speech-to-text and text-to-speech algorithms are employed to make this possible. In addition to the translation feature, the project also comprises a dictionary filled with commonly used Tulu words.
Hearing Aiding System for Impaired

Hearing loss can be a significant challenge for individuals, affecting communication and quality of life. This project aims to address this problem by creating a device that converts sound signals into vibrations that can be sensed by deaf individuals. It uses an electric motor to produce vibrations and converted sound signals into electrical signals using a microphone and amplifier. The electrical signals were used to drive the motor, which produced vibrations that could be felt by the user. The vibrations were transmitted through a surface that could be touched or held by the user. It evaluates the effectiveness of our device in converting sound signals into vibrations and found that it was able to convey sound information effectively. This also evaluates the usability and acceptability of the device among deaf individuals, who found it to be easy to use and effective in conveying sound information. This project demonstrates the potential for a device that converts sound signals into vibrations to improve communication and quality of life for individuals who are deaf or hard of hearing. Further research could explore potential improvements to the device, such as the use of different types of motors or surfaces, as well as its potential applications in different contexts and populations.
Integrating ML Algorithms in Graph Database for Link Prediction

The task of link prediction is commonly performed in graph analysis, where the aim is to predict potential connections between nodes in a graph that do not currently exist. This paper focuses on examining how machine learning techniques can be utilized for predicting links in graph datasets, with a specific emphasis on WN18 and KG20C. The proposed approach combines traditional graph-based features with features that are learned through deep learning techniques, specifically embeddings. We use a combination of graph embedding techniques, such as node2vec, and machine learning models, such as Decision trees, Random Forests, Naive Bayes, XGBoost, and Artificial Neural Network to predict missing edges in the graphs. The performance of this approach is assessed through the evaluation on the WN18 and KG20C benchmark datasets, where it is compared to other advanced methods. We argue that, due to the semantic nature of these datasets, natural language processing (NLP) techniques could potentially be more effective for link prediction. We highlight the benefits of using NLP- based techniques, including word embeddings, node embeddings, and transformer models, for predicting links on these datasets. Furthermore, we discuss some of the challenges associated with integrating NLP and graph-based methods and offer potential solutions. Ultimately, we conclude by summarizing our findings and emphasizing the importance of combining diverse ML and NLP techniques for predicting links in complex graph datasets, such as WN18 and KG20C.
Artifact Based Deepfake Detection Methods

One of the biggest innovations of Artificial Intelligence (AI) is the ability to generate manipulated or synthesized media (images, videos). When these generated media is created in to look like real and original people then it is called a Deepfake. Deepfakes have gained attention due to their potential to create convincing and misleading content that can be difficult to distinguish from authentic media. While they have garnered positive value in entertainment sector but have seen as a biggest threat regarding its ethical and societal implications particularly in the context of misinformation, identity theft, privacy invasion, and defamation. Rigorous research has been done since its beginning to prevent and detect media forgery and many detection techniques have been developed and discussed in literature. There is no clear winner in identification of DeepFakes however the artefact-based approaches seem to be much superior in the literature. In order to find the lean and identify best detection technique, an evaluation of existing techniques is needed which is the main purpose of our paper. This compares the video forgery detection techniques in terms of convergence, accuracy and equal error rate (EER).
Investigating the Use of Artificial Intelligence for Secure and Transparent Record Keeping in Education

Numerous businesses, particularly education, are being rapidly transformed by artificial intelligence (AI). AI is being utilised in school management to enhance student results, the learning experience, and administrative chores. This study intends to investigate the use of AI in educational management, comprising advantages and disadvantages. The study effort examines the literature on AI in school management using a systematic review method. According to the research, AI has several benefits, such as increased student engagement, personalised instruction, and cost effectiveness. But AI also presents several difficulties, including ethical issues, possible biases, and the necessity to reskill the workforce. The study finds that AI may significantly enhance educational management, but its application must be done so carefully and cautiously.
Real-time System for Translating American Sign Language to Text Using Robust Techniques

Sign language serves as a vital medium of communication for the Deaf and dumb people. We present a real-time system to translate ASL into text, addressing the communication gap between deaf and dumb people and normal people. Our system leverages computer vision and machine learning techniques to accurately recognize ASL gestures. In this study, we introduce a convolutional neural network (CNN) and (LSTM) approaches designed to identify hand gestures associated with human actions as captured through images from cameras. By utilizing the hand's spatial positioning and orientation, we construct the training and testing datasets for the CNN-LSTM model, we were used 4680 skeleton images of the ASL alphabet, from A to Z as a dataset. The hand image undergoes an initial filtering process followed by classification, where a classifier determines the classification of the hand gesture. Subsequently, the refined images are harnessed to train the CNN-LSTM model. Our real-time system provides accurate and reliable results in real-time 99.9%, in addition to generating new datasets that are processed and can be trained on any model.
WARM WOMB: A Wireless Real-time Monitoring System for Neonatal Care in Resource-Limited Settings

To ensure the well-being of preterm babies and help them adjust to the outside environment, a environment similar to a womb has to be provided. The neonatal intensive care unit (NICU) is designed specifically for monitoring and caring for the health conditions of premature babies. However, in rural areas, many hospitals lack NICU facilities, which means parents often have to travel to multi-specialty hospitals in case of emergencies. Unfortunately, the cost of NICU care in these hospitals is often prohibitively high. To address these challenges, we propose a solution called Wireless Artificial Real-time Monitoring WOMB (WARM WOMB). This innovative system aims to provide effective care for low-risk premature babies in abnormal conditions. It features a compact and cost-effective infant incubator that helps maintain the baby's temperature at levels similar to that of the mother's womb. Additionally, the WARM WOMB system incorporates advanced monitoring capabilities. The hardware of this proposed system has been tested under various conditions to analyze its functionality. By integrating temperature regulation, vital sign monitoring, weight tracking, and real-time video capturing, the proposed WARM WOMB design offers a comprehensive solution for neonatal care. Its primary focus is on addressing the specific needs of premature babies and enhancing their overall care.
Detecting Deepfakes using CNN and LSTM

Deepfake, a face-swapping method that has been abused recently, has caused a great deal of public worry. Effective countermeasures are required because several deepfake videos, sometimes known as "deepfakes," have already been created and posted online. Therefore, Deepfake detection is going to be a promising defense against deep fakes. It is quite easy to imagine cases in which these convincing tampered videos are exploited to foment political unrest, extort money, or stage terrorist attacks. In this study, a pipeline is suggested for identifying and classifying deepfake videos. Our system uses convolutional neural networks (CNNs) to retrieve frame-level characteristics. Then, a recurrent neural network (RNN-LSTM) is built using these features to determine if a video has been altered or not. We study, analyze and compare various methodologies and ours on a vast cluster of deep fake videos from various video sources. We present how our system can complete this task effectively with comparative results while utilizing a straight forward architecture.
A Multimodal Approach Utilizing the IOMT to Address both the Pandemic and its Aftermath

Many IOT in the healthcare industry offers improved medical facilities for patients, benefiting doctors' offices and hospitals as well. The Internet of Medical Things (IOMT) plays a crucial role in enhancing the accuracy, reliability, and efficiency of electronic devices within the healthcare sector. Researchers are advancing a digital healthcare system by connecting existing medical resources and healthcare services. While IOT is impacting various industries, our focus is on its research contributions to the healthcare sector. The proposed system integrates multiple medical equipment, such as sensors and web or mobile-based applications, enabling communication across a network. This system facilitates the monitoring and storage of patient health data and medical information during the pandemic situation and after math. Additionally, using machine learning and data analysis, the proposed system aids in predicting the severity of the pandemic in different zones, offering potential solutions. The system seeks to improve outcomes for post-COVID-19 patients by using a multimodal approach to track their health.
Weather Forecasting using Application Programming Interface

Weather forecasting is crucial for planning daily activities, making informed decisions, and ensuring the safety of individuals and communities. The project begins by selecting a weather data provider, such as openweathermap or weather stack, and obtaining the necessary API key. Python libraries, including requests and json parsing, are utilized for making api requests and extracting weather related information. Details such as temperature, humidity, wind speed, and meteorological conditions are included in the retrieved data. The user is provided with a thorough weather report containing the created weather forecasts, which provide insightful information about the current state of affairs and potential future trends. Users can interact with the application by inputting their desired locations to obtain location specific weather information. Python offers a range of machine learning and statistical modelling libraries, including Scikit-Learn, TensorFlow, and PyTorch, which are used to build weather forecasting models. These models can be as simple as linear regression or as complex as deep neural networks. They use historical weather data to learn patterns and relationships between different variables.
The Best ML Classifier(s): An empirical study on the learning of imbalanced and resampled credit card data

Machine Learning-based fraud detection systems are more effective at detecting financial fraud. Credit card fraud detection is one of them. The datasets used in learning by the machine learning algorithms faced many difficulties due to class-imbalance issues. The aim of this research is to discuss the latest developments in credit card fraud detection and also assess the predictive performance of eight popular machine learning classifiers, such as Logistic Regression, K-Nearest Neighbour, Decision Tree, Random Forest, AdaBoost, Bagging, CatBoost, and LightGBM, on learning from an imbalanced credit card fraud detection dataset. Here, the synthetic minority oversampling technique with the edited nearest neighbor, i.e., SMOTE-ENN, has been used to deal with class imbalance issues. The performance evaluation of each classifier is performed on learning in two situations using a raw dataset and a synthetically resampled, class-balanced dataset. When learning from a raw dataset, Random Forest and CatBoost outperformed all the classifiers. The RF attained the highest recall, G-mean, and AUC as 0.785, 0.886, and 0.892, respectively, while CatBoost attained the highest precision and F1-score as 0.921 and 0.843, respectively. In contrast, when learning from a resampled class-balanced dataset, Random Forest, AdaBoost, and Bagging attained the highest precision of 0.999, while K-Nearest Neighbor, Random Forest, and AdaBoost attained the highest recall of 1.000. On other performance metrics, all classifiers performed well except Logistic Regression and Decision Tree. The findings illustrate that the Random Forest classifier is more effective in imbalanced data as well as resampled data learning.
CNN-based Human Emotion Recognition from Facial Images in Controlled-Uncontrolled Environment

Emotions play a crucial role in shaping human thoughts, behavior, and feelings. Consequently, the task of recognizing emotions from facial images has attained significant attention because of its applications across various areas. Traditional algorithms have often proven inadequate in meeting real-time human needs, leading to the emergence of machine learning and deep learning algorithms as more effective alternatives. Existing researchers of computer vision and affective computing have primarily concentrated on enhancing the classification accuracy of benchmark datasets. In line with these advancements, the main focus of the study is to evolve a specialized Neural Network based model which is competent in accurately classifying seven distinct human facial emotions. The model is meticulously trained and rigorously tested using both laboratory-controlled with an accuracy of 98% and 53% over uncontrolled environment images without using any sort of pre-processing and also ensuring model robustness across different scenarios.
Decoding Digits: Advancements in hand written Digit Recognition with Machine Learning Algorithms

One of the most challenging besides fascinating fields of image processing and pattern recognition has been the recognition of handwritten characters and numbers. This paper's primary goal is to illustrate and reflect the work that has been done in the area of hand-written digit recognition. areas of pattern recognition and image processing is a highly challenging task. It is difficult to extract features from and segment handwritten numerical script because the numbers in this identification challenge are not precisely written or scripted since they vary in size or shape. In the suggested work, segmentation is accomplished by the use of vertical and horizontal projections. Convex envelope technique is used for feature extraction, whereas SVM is employed in recognition and and classification.
Handwritten Character Recognition: A Comprehensive Survey

Handwritten character recognition (HCR) plays a vital role in numerous domains, including document digitization, automatic text recognition, signature verification, and postal automation. Over the years, significant advancements have been made in the field of HCR, driven by the ever-growing demand for accurate and efficient recognition systems. This paper presents a comprehensive survey of the techniques, methodologies, and recent advancements in handwritten character recognition. We discuss various approaches, including feature extraction, classification algorithms, deep learning techniques, and dataset creation. Additionally, we analyze the challenges and open research areas in HCR and highlight potential future directions.
Blockchain in Higher Education: Key Research Themes and Future Directions

This research paper explores the applications of blockchain technology in higher education, focusing on areas where it can bring transformative changes. The study addresses research gaps by conducting a comprehensive bibliometric analysis to understand the overall research landscape, specific applications, and challenges of blockchain in higher education. The findings provide insights into publication trends, influential contributors, and emerging research directions. The study highlights the potential of blockchain in areas such as credentials and certificates, record-keeping, micro-credentials, payments, research, and data management. This research contributes to advancing knowledge by guiding future research directions and facilitating the integration of blockchain technology in higher education systems.
Analysis of Mental and Physical Health for Personality Development using Fuzzy AHP and Fuzzy Topsis

The complex interactions between mental and physical health highlight the demand for a thorough and accurate method of health analysis. In order to provide a solid framework for assessing both mental and physical health indices, this study introduces a thorough technique that integrates the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP). This study gives a new perspective on the use of fuzzy TOPSIS in the area of health evaluation in order to provide a comprehensive framework for evaluating both mental and physical health aspects. Fuzzy TOPSIS and Fuzzy AHP used together to examine physical and mental well-being give a complete picture of a person's overall wellbeing. By including both physical and mental well-being difficulties, this method provides a full understanding of health status and its dynamic nature, underlining the importance of treating both dimensions in healthcare planning. This paper provides an overview of the key components of the technique, including data preparation, criterion weighting, and decision-making procedures. In order to rank the elements, we are taking into consideration criteria in this study that are based on both MH and PH. This method holds the potential to enhance healthcare decision-making, leading to more effective healthcare interventions and improved overall health outcomes by providing a flexible and comprehensive framework for evaluating and prioritizing both mental and physical health criteria.
Spatio-Temporal Convolutional Neural Networks for Deepfake Detection: An Empirical Study

As the creation of deepfakes becomes more prevalent and sophisticated, the need for accurate and robust detection methods intensifies. This paper presents a comprehensive empirical study on the efficacy of S patio-Temporal Convolutional Neural Networks (ST-CNNs) for deepfake detection. It explores how the rich spatio-temporal information contained within video frames can be exploited by ST-CNNs to distinguish between genuine and manipulated content. The study is underpinned by a robust testing framework, wherein a range of deepfake generation techniques are used to evaluate the detection model. It further investigates the effect of various layers and architectural elements on detection performance. The results demonstrate that ST-CNNs, by leveraging spatio-temporal correlations, can offer superior deepfake detection performance compared to the conventional CNN models. This work can guide the development of more efficient and effective deepfake detection strategies by providing empirical insights into the utilization of ST-CNNs.
VidSum - Video Summarization using Deep Learning

The aim of video summarization is to create a short summary video which captures the essence of the original video and contains all the important events of the original video. This is helpful because, now we don't have to go through the entire video and are able to get a gist of it from just a short summary video. Current Supervised learning video summarization methods, use Convolutional Neural Networks and some supervised learning techniques use Recurrent Neural Networks in addition to them. We propose VidSum, an architecture for Video Summarization using Deep Learning. We combine Long Short Term Memory (LSTM) Networks with Convolutional Neural Networks to solve the problem of Video Summarization. Our deep learning model is able to find the temporal importance of video frames and is able to generate video summaries which are temporally coherent and contain the important parts of a video clip. In our testing, our model outperforms other models on the famous TVSum and SumMe datasets for the task of Video Summarization.