Reviewerly/exHarmony huggingface dataset:
The prediction of Digital Political Commercial Evaluation Using Improved Linear Regression

Analysis of digital advertisements for politicians with an age factor using linear regression algorithms versus decision trees to determine improved accuracy. The study gathered 116 samples from various internet sources using a 0.05 percent threshold, a confidence level of 95 percent interval, a mean and standard deviation, and an 80 percent G-power. Because the Linear Regression algorithm has already achieved an accuracy of 95.25 percent in forecasting political advertisement analysis, the DT approach must achieve greater accuracy in this research. In this study, the DT algorithm was used to analyzed political ads, yielding a 95.00 percent accuracy with a significant value of 0.007 (p0.05) for two-tailed tests with a confidence level of 95 intervals. The DT approach outperforms the Linear Regression algorithm within the constraints of this experiment.
Sentiment Analysis on User Feedback of a Social Media Platform

The paper proposes a methodology for implementing Sentiment Analysis on user feedback of a social media website named “DevelopersBay”. This study has performed a sentiment analysis of user feedback received on the website by using basic machine learning method and BERT model of deep learning. The process of sentiment analysis utilizes algorithms to evaluate the tone of feedback, indicating whether it is positive, negative, or neutral. This information is then used to enhance the quality of the services or products provided by the organization. In our research we found the deep learning method to be more accurate in determining the sentiment of the feedback received on the website. The results obtained from sentiment analysis help us improve our website to meet the needs of our users.
Electric Bike Security: Biometric &amp; GPS Integration for Intrusion Detection

The proliferation of the e-bike industry has highlighted the need for a robust and secure tracking system. The traditional mechanical key-based vehicle security systems have limitations that include vulnerability to theft, limited access control, lack of personalization, and real-time tracking. This research paper outlines the creation and deployment of a sophisticated GPS/GSM-based vehicle tracking system that incorporates biometric authentication for access control, addressing the aforementioned challenges. The system leverages the Global Positioning System (GPS) to unceasingly monitor the real-time location of vehicles and facilitates the integration of Global Navigation Satellite System (GNSS) technology, in the form of a NEO-6M module, with a Global System for Mobile Communications (GSM) network, expedited by a SIM900A module integrated with TCP/IP stack. The core of the system is the ATMEGA16 Microcontroller, which serves as the central processing unit, managing the incoming data from the GPS (latitude, longitude and time) and GSM (phone number, signal strength and mobile network provider) modules and governing the system’s overall functionality. The biometric module is powered by an ATMega328p chip incorporating fingerprint recognition technology, which adds an additional layer of security by permitting only authorized access. The proposed solution provides a cost-effective and secure means of monitoring that can be integrated into existing fleet management systems and used in various applications. Presently, the system is being utilized as a part of an electric bicycle kit and has been tested in controlled environments, demonstrating its reliability and efficiency.
Optimized Feed Forward Neural Network for Fake and Clone Account Detection in Online Social Networks

Online Social Network (OSN) is becoming popular among peoples to communicate, share and disseminate the personal, social and technical information among the other user in the Social media. Despite of its popularity, many challenges has been exhibiting on usage of the network in terms of security breaches. Especially fake account and clone attack has been propagating in the network on obtaining or misusing of others information’s which leads to privacy threats to the user of the account. In order to mitigate and eliminate those issues, machine learning based technique is employed to detect and prevent the fake account and clone attack propagation in the network. In this paper, a new optimized feed forward neural network is employed to detect the fake account. Initially data is preprocessed to remove the missing values and non-available values. In this, outlier data of the data stream is removed by employing the K-means clustering, it reduces the training time of the user classification and prediction process. The prediction of fake account in the data streams of the social network data is carried out using neural network learning model mentioned as feed forward neural network. The neural network considers the inputs on the data streams as identifying the dominant attributes on employing the principle component analysis for feature extraction process and genetic algorithm for optimal feature selection. The objective function of the neural network is optimized to reduce the misclassification rate of the user classifier. This proposed approach is able to enhance the accuracy of the user classification on comparing with existing technique on its evaluation with twitter dataset. The whole process is realized and evaluated with respect to accuracy, precision and recall.
VIRTUSOFT- The Virtual System

Many modules are developed to assist the physical world interact with the digital world. Here we present a very unique approach for Human Computer Interaction (HCI) where, we control Cursor movement, Keyboard typing with alternative input hardware collectively switch, and a Camera method presented integrates tricks operation like Clicking and Saving the photos in gallery are visiting be performed with the numerous hand gestures by using webcam as data device employing a real-time web camera. During this “VIRTUSOFT” Our method is to use a web camera and computer vision technology, like image segmentation, background subtraction and color tracking to manage mouse tasks (left clicking, right clicking, double clicking and scrolling actions) which we show how it can perform everything as current hardware devices can. For the thing recognition and tracking the color pointer has been used and implement the module with none physical contact with the system. Click events of the mouse are achieved by detecting the quantity of recommendations on the pictures.
Empirical Study of Deep Learning based Yoga Asana Identification and its Alternatives

This study focuses on the application of yoga, an ancient form of exercise that has been practiced for thousands of years. In recent times, yoga has gained popularity as a form of exercise and as a result, there is a growing demand for proper instruction. Many doctors recommended Yoga for the quick recovery of injuries as well as the best tool for combating mental-health issues such as depression, anxiety, and post-traumatic stress disorder. Yoga is the exercise that is widely accepted for its health benefits. Usually, there is a disconnect between what people are doing and what the ideal asana is. It is very important to perform yoga postures correctly to avoid variety of health problems such as joint pain, disc misalignment, shoulder pain, and so on. According to the researchers’ study report, nearly 87% of musculoskeletal pain or worsening injuries, and more than 10% said yoga caused pain in their hands, wrists, shoulders, and elbows. Our paper attempts to bridge the gap between what is the ideal posture and how to help people adopt a healthy lifestyle. So, in this research 12 Yoga asanas have been taken and classified them using neural network-based transfer learning models. Also, four transfer learning models named Baseline CNN Model, VGG16, VGG19, MobileNet and Xception models are applied. Out of all the models performed for Yoga asana classification MobileNet has leveraged a better accuracy of 91.2%. This study has employed deep learning methodology, and the proposed technique has achieved a high recognition rate. In addition, it was also discovered that the proposed technique has 80% more medicinal value.
Using Image Classification-based RBC Extraction for Malarial Parasite Identification in Peripheral Blood Smear

Malaria is a dangerous, irresistible, and perilous mosquito-borne blood disease brought about by Plasmodium parasites. The traditional and most standard approach to diagnosing malaria is by outwardly analyzing blood smears through conventional microscope for parasite-contaminated red platelets under the conventional microscope by qualified specialists. This technique consumes more time and the conclusion relies upon the experience and the information provided by the individual performing the assessment. A red platelet is viewed as contaminated if something like one parasite can be identified inside its inside. White platelets and free-drifting parasites are not thought of. The present status of the craftsmanship includes manual counting by a research facility expert or other person, who can recognize staining antiquities from actual nuclei, white platelets, and (contingent upon explicit prerequisites) life cycle and types of malarial parasites. In any case, the commonsense presentation has not been sufficient up until this point. This gives us all the inspiration to make malaria location and finding quick, simple, and proficient. The main aim of this study is to assemble a model to recognize cells from images of various cells in slender blood smear on standard magnifying lens slides and characterize them as either contaminated or uninfected with right on time and compelling testing utilizing image processing. This image processing method focusing on automated screening of malarial parasite contamination in microscopic image of thin blood stain.
Semi Autonomous Robot for Domestic purpose

Today, technology is rapidly advancing to meet the growing needs of humanity, with a particular focus on robots. Robotics refers to the creation of machines that can perform tasks or actions typically done by humans, either autonomously or via remote control. One of the major advantages of robots is their ability to operate in hazardous environments that are unsafe for humans. As a result, scientists are continually seeking to improve robots by developing new controllers and designs to make them more efficient and reliable. Our mini project centers around categorizing home robots based on the specific task they are designed to perform. The categorization is based on the object of operation. Our system features a semi-humanoid robot prepared with Google Assistant to interact with humans and carry out tasks according to our commands. The robot has the ability to move in different directions, halt, grip and release objects, and move its arms vertically. Furthermore, it includes a built-in vacuum cleaner feature that permits the robot to tidy floors.
Study of Image Forgery Detection using Scale Invariant Feature Transform

Images are a crucial source of information and are also used as a legal references and proofs sometimes, but since the availability of it has made easy due to internet hence it is prone to many manipulations and tampering. Image forging refers to the process of changing an original image in order to produce a false image for any fraudulent or other purpose. The most common form of forging is called copy-move forgery, and it consists of clipping or duplicating parts of the original picture and placing them elsewhere on the same image. Forgery detection techniques come in a variety of forms, including block-based and key-point based ones, just as there are various ways that forgeries are carried out. Through comparison with the original picture, the technique suggested in this article aids in identifying image forgeries. Using the Scale Invariant Feature Transform (SIFT) method, separate the invariant features from the original and modified images. The advantages of SIFT over other algorithms is that it helps in reducing computational expenses, increases accuracy, and provides better results.
Role of Computer in Digital Forensics

The report is built around how computers are used in digital forensics. Today, as digitalization spreads quickly, all businesses and people are utilising technology to improve how they conduct business. For that reason, we require a digital system that enables us to complete a variety of jobs more quickly. We also utilise social networking sites for amusement There is nothing wrong with this, but we should exercise caution when using social media and our systems because crime rates are rising as digitalization grows. In order to avoid cyber attacks, software should be installed. When an attack is conducted, we should be aware of the various methods that are available. For instance, in 2020, the use of computers will be too beneficial for the Covid-19 positive forensics test.
NLP based Text Summarization of Fintech RFPs

In today’s day and age, where huge quantities of textual data are generated every second, it has become difficult to keep ourselves abreast with new information. Documents in the financial sector tell a quantitative story. The qualitative or language content that accompanies financial statements is an essential component of the information set used by financial market participants for monitoring and stewardship. This necessitates the development of efficient technological methods for leveraging the existence of these massive amounts of textual data. Reading financial documents such as annual reports is extremely time-consuming and thus corporations have to allocate precious human capital to understand, analyze and derive insight from these reports. As a result, automatic summarization techniques can make this task simpler by enabling access to a smaller but informative portion of a given document. In this work, a system utilizing NLP techniques for summarizing financial documents based on queries provided by the user, is presented. This system overcomes the challenges posed by existing section-based summarization models. As machine learning methods have proven to be effective on downstream tasks such as text summarization, the intent of this work is to take advantage of these methods for effective extractive summarization and generate human-like summaries. The proposed model categorizes phrases according to their relevance by leveraging the strength of unsupervised clustering approaches, and it gets a ROUGE-1 score of 46%.
Exploring Facial Landmark Detection Techniques for Attention Detection in Human-Computer Interactions

High-dimensional facial landmark detection is an essential component of the Attention-detection mechanism, which involves analyzing and interpreting the intricate details of the human face. Despite significant progress in recent years, facial landmark detection still faces challenges such as variations in pose, expression, and illumination, as well as occlusions and noisy images. This review paper summarizes recent advancements in facial landmark detection techniques, including convolutional neural networks, deep learning algorithms, and more. We also discuss the limitations and challenges of current facial landmark detection methods and potential future directions for research. Overall, this review provides insights into the current state of the art in facial landmark detection and the challenges that still need to be addressed.
Briefing of Textual Information using Text Rank

The world is very much advanced with the abundant growth of technology and the way we communicate is rapidly changing with it Lot of information is being generated day by day. It is important to extract the useful information from it Text Summarization is one of the solutions to it Different statistical methods like TF-IDF, TF are used for extracting the summary. These statistical methods focus mainly on most frequently occurred words and less on importance of sentences. There are many limitations of TF-IDF method like it is based on Mg of words model, so it does not capture position in text, semantics, co-occurrences in different documents. The other limitation is it assigns low values to words that are relatively important So, we proposed a system that uses a graph-based approach which extracts the significant sentences from the given text. The proposed system provides extractive summary, abstractive summary and keywords for single document. The keywords extracted from the summary helps in understanding the main idea of the document The proposed model helps users in providing summary with important points and saves user time.
Large video processing using GPU programming

Currently, video cameras are used in many applications, from security to recognition mechanism, all based on the analysis of images and videos, these records are made in most cases for continuous periods of recording, which is why they have large video storage weight on hard drives, in this paper, we describe a method to analyze images of high weight, using the graphic processing units known as GPUs, which can be found in desktop computers and laptops, the model uses as a test mechanism, the MATLAB tool, as a language and development environment, the method presented has the advantages of being applied and replicated, depending on the needs and applications to be programmed.
Comparative Study on Forecasting Techniques by using Cloud Services

Cloud computing is predominantly regarded as a unique energy-efficient way of resource consumption for the IOT. The cloud computing technology offers businesses and users of cloud applications with remote computational resources. Both technologies extend a broad range of new opportunities for applications that are adaptable when combined with the Internet of Things. Modern weather forecasting methods focus heavily on anticipating cloud expansion predicting the load on cloud computing services becomes a research focus to arrange the resources strategically and economically for software and hardware of cloud computing. These often consume less energy than conventional on-premises systems because of dedicated servers for cloud platforms, better capacity utilization, and scale economies due to the use of bigger, more energy-efficient datacenters. Misleading cloud forecasts can result in a significant amount of ambiguity in meteorological data considering the essential function that clouds play in the planet’s climate system and Insufficient high-resolution datasets with extensive historical records internationally, few studies have attempted to use machine learning to address this difficult challenge and hence this article is written based on the forecasting methods and techniques that are used in previous references and the techniques.
Defending against Cache-based Side-Channel Attack using Virtual Machine Migration in Cloud

The Cloud services are gaining importance in today’s world. Many recent researchers have discovered unique vulnerabilities in securing privacy and integrity of the cloud-based systems. While cloud-computing is offering many advantages over the on-premises computing conditions in multiple aspects, like accessibility, availability, cost-efficiency, and scalability, it also introduces a wide range of new difficulties related to security. Previous research have shown that there are security threats in almost every component of cloud computing architecture such as infrastructure, storage, and application. The attacker has a clear set of targets that are interrelated to the Virtual Machines. He extracts confidential information from the user by implementing their own attack procedures of Virtual Machine on the physical servers. The Cloud platform provides underlaying infrastructure to the middle layer of the software system to the application that are developed on the top of the systems. This paper majorly concentrates on a specific task that is related to cache-based side channel attacks which targets the virtualization level, from the bottom of the software stack to the top. Experimental analysis along with graphical and mathematical analysis have been performed. The existing methodologies along with designed methodology are studied, and the comparative analysis is made.
Deep Fake Detection using Advance ConvNets2D

Even a few decades ago, it would have been impossible to foresee the widespread use of deep learning to tackle such complex issues. It has many positive applications but it can also be misused to cause harm to the society. One such challenge is deep fakes, and now more than ever, when anyone with a smartphone app can create a fake image or video, it is crucial to take measures to authenticate the content online. Visual and auditory “deepfakes” may be generated with the aid of neural networks. Deepfake mimics machine learning models with the help of Generative Adversarial Networks. Many well-known people have been affected by the rapid spread of false news stories on social media platforms like Facebook, YouTube, Twitter, etc. Artificial Neural Network (ANN) based deep fakes may appear identical to authentic images or videos before being moderated but they always have spatial and temporal signatures. A neural network has been trained to specialize in Deep fake detection can quickly identify these signs. To determine whether or not a video is deep fake, Residual Network and InceptionResNetV2 models are employed in this research.
Birds Egg Recognition using Artificial Neural Network

A bird of one species laying its eggs in the nest of a bird of another species without providing parental care is known as brood parasitism. Finding a classification for bird eggs is the issue. This study constructs a dataset by using the data augmentation technique. One of the most important tasks in agriculture is brood parasitism. While brood parasitism, any other species’ eggs might be mixed in. The birds are unable to identify their young. The possibility of a hybrid exists. Most people are unable to identify the bird’s egg, even if there may be identical children from the same parent. Using digital photographs, the eggs will be classified according to four characteristics: colour, spottiness, form, and pattern. After that, these variables were utilized to a multiclass egg classification technique for supervised machine learning. The results are more accurate because of the machine learning technique. Artificial Neural network of the machine learning techniques the model will be presented to obtain the desired results; each algorithm gives different features and measurements. This study recommends machine learning to classify birds’ egg automatically. The use of python and associated libraries was required throughout the classification process.
Game Automation System Based on Computer Vision

Games are an activity with some set of rules. Usually, games are played for entertainment or fun and sometimes used for education purpose. Now a day’s games are also considering as profession. Games are essential for healthy development in early childhood as many of the games helps in improving knowledge and skills of an individual. In recent times online games gain popularity that leads to decrease physical activity of a player in this scenario players loosing physical gaming experience. There will be huge requirement of a system that physically involves the players in online games. This study introduces a real-time hand tracking system for portable devices that analyzes a player’s hand skeleton using computer vision techniques this process of gesture recognition techniques has been used to understand human gesture and body language. This System acts as a bridge between human and machines and enables player to gain physical experience in online games. The classical approach is based on the computer vision methods, where the image frames obtained from the web are subjected to filtering, detection and recognition. However, Media-Pipe is multimodal framework provided by google that helps in training model to achieve high performance.