Reviewerly/exHarmony huggingface dataset:
Machine Learning-based Depression Prediction using Social Media Feeds

In today's environment, young people frequently use social media platforms to communicate emotions. They post about their feelings on social media, which can help us understand how they feel at the time. As a reaction to the critical need for early detection tools, this research study uses sentiment analysis techniques to examine user contributions to social networks to help detect potential depression at an early stage. The research describes different methods for predicting sadness from user posts. The dataset is vectorised using count vectoriser and TF-IDFvectorizer, and features like post sentiment is retrieved. In our project, the model is divided into training and test datasets and trained using the Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and K-Nearest Neighbors machine learning techniques. The measures that are assessed are recall and accuracy. The Instagram API is applied to mine Instagram posts to create the dataset for the model. Each comment will undergo pre processing; each word will be processed through a lexicon to determine if it is positive or negative. This research study presents a new feature vector for classifying the texts as positive or negative. Each comment generates a score value from the lexicon to signify the degree of positivity, negativity, and other factors. A CSV file containing around 6,300 posts has been preprocessed. The distinctive characters and extraneous characters are eliminated using regular expressions. The data quality is then enhanced using stop words, Lemmatization, and tokenization. The best method for this approach yields an accuracy of 90.19% and a recall of 89.85% utilizing a decision tree model using a count vectorizer.
Research on Facial and Mask Recognition

In the period when the COVID-19 is raging, in order to prevent and control the epidemic, it has become normal for people to wear masks. Masks mask the face, greatly reducing the number of facial features available for recognition, and the accuracy of the original face recognition algorithm decreases. This is also an important discussion at present. Yolov3 target detection trains a large number of data sets to recognize the wearing of masks but cannot recognize the identity of the detected person. Therefore, this paper proposes a study on the simultaneous recognition of face and mask, using biometrics to locate eyes with different eyes spacing according to key point information and use Euclid algorithm to calculate. Through data matching, the face wearing masks can be accurately identified.
Multi-Object Segmentation on Public Roads

This study explores the challenges of multiple object segmentation on Indian roads, which presents unique challenges, such as unstructured roads, opposite lane driving, and animals on the road. Despite the extensive research done on object detection and segmentation, only a few studies have explored the effectiveness of popular models such as mask R-CNN, yolo, Vit for Indian dataset. The unstructured nature of Indian roads along with high occlusion and varying lighting, different shape and colors of the vehicle presents a unique challenge to the task of object segmentation on Indian roads. This study explores the uses of two object segmentation models, Mask Scoring R-CNN [7] and Cascade Mask R-CNN [8] for multi object segmentation on Indian roads using the Indian Driving Dataset (IDD). The paper compares the results of these models to benchmark models and addresses the significant differences between the IDD and other datasets by proposing an adaptation of these models to accommodate to the Indian traffic context thus addressing the significant domain gap between the IDD and other datasets.
Solar Panels Efficiency Improvement Through Orientation Monitoring by Non Contact Method using TMR Sensor

The need for renewable resources has become cru-cial as a result of the escalating rate of energy consumption, the finite supply of fossil fuels, and the growing problem of pollution. Solar energy, in particular, has gained significant traction among renewable resources due to the abundant solar potential present around the world. This paper presents a novel technique of position monitoring and solar tracking of a PV panel with the help of non-contact Tunnel Magneto Resistance (TMR) sensor. Sun tracking of the Solar PV system enhances the energy production and in case of Solar thermal applications like parabolic trough and dishes it is essential. The tracking of sun and system orientation uses different types of sensors and actuators. The sensor size, precision, cost, and the gathered information from the sensor is import. In this research Tunnel Magneto Resisters (TMRs) sensor is used to determine the PV system one axis orientation. The training data sets are created through tests at a curved surface with different measured radial distances. The validation results showed the Bx, By Bz axis error values as 0.1, 0.3 and 0.5 percent maximum in the position measurement.
An Efficient Ensemble approach for Fake Reviews Detection

People prefer to buy items and services online to save time in the current age of growing e-commerce. these internet purchases are heavily impacted by the reviews or opinions of people who have already purchased them. customers provide comments to businesses on how to improve product quality, develop, and monitor business strategies in order to boost sales and profits. customers may also use these comments to choose the proper items with less effort and time spent. giving fake review is the practice of fraudulent people who wants to promote or degrade products or services for illegitimate monetary gain. in this research paper, we present an ensemble machine-learning model to identify whether a review is fraudulent or authentic. to achieve this objective, amazon reviews dataset is used. the proposed ensemble model outperformed as compared to other individual classifiers. random forest provides 99% accuracy which is better than other algorithms.
Comparison of Deepfakes Detection Techniques

Detection of fake audio and video is a challenging problem. Deepfake is popularly used for creating fake audio and video content using deep learning. Deepfakes, artificially created audiovisual interpretations can be used to degrade the reputation of a renowned person, hate-speech, or affect public belief. The development of novel methods for identifying various deep fake video types has received a significant amount of research throughout the years. In this research, we present a thorough comparative analysis of current state-of-the-art deepfake detection methods. The primary goal of our research is to identify the factors that contribute to the performance degradation of deepfake detection models currently being used when tested against a comprehensive dataset.
EMS-RTC: LSTM-based Adaptive Video Streaming for Smart Ambulance

A smart ambulance continuously shares the patient's vital signs data and video feeds from the ambulance so that the doctors stationed in the hospital can work with the paramedics in the ambulance, performing an early assessment of the patient or even providing treatment guidelines. One prerequisite to the seamless cooperation between the doctor and the paramedics is high-quality video streaming. Video streaming with bad image quality or high latency would introduce significant difficulty in providing accurate diagnoses of the patient. As the video needs to be delivered from a moving ambulance, the streaming performance is influenced by the fast variation of the service quality of the mobile network. Compared to static scenarios, additional channel impairments (e.g., the shadowing effect) is introduced in the mobile scenarios. In this case, it is crucial that the video streaming bitrate can quickly react to the changes of the service quality and is adjusted accordingly, such that the video quality leverages full available bandwidth, but does not exceed what the channel can support. To this end, we propose EMS-RTC, a real-time video streaming platform specialized for the need for ambulance services. EMS-RTC trains a classifier to infer the optimal bitrate based on features from a range of signal- and network-related metrics, collected in real-world driving scenarios. We compare EMS-RTC to a state-of-the-art adaptive bitrate algorithm (i.e., Google congestion control). Evaluation results collected from real-world driving scenarios show that EMS-RTC reduces the total time duration of stall events by a factor of 3.85, at the cost of a neglectable reduction of image quality.
Intrusion Detection using Camera and Alert Management

The ability of a closed circuit television camera to detect and raise an alert is the future of surveillance systems. The traditional way to provide security to any area is by placing a CCTV camera but this way is not sufficient for handling the frequently occurring illegal activities. In order to update these traditional methods, this research study has analysed the techniques that automate the system using image processing algorithms such as Sum of Absolute Difference (SAD). The technology used for implementation is OpenCV (Open source computer vision) with Histogram of Gradients for performing human detection from images and video streams. The image that indicates the difference between the first stage and the time-stamped image where the intruder may be identified is acquired as the result. The accuracy is measured using the Mean Squared Error (MSE) of the image pixels.
Research On Virtual Restoration Display Of Chinese Paintings Based On Augmented Reality

In response to the current problem that a large number of broken Chinese paintings cannot be exhibited, an AR restoration method is proposed that allows visitors to experience the restoration process and appreciate the restored works when they visit. Firstly, the damaged Chinese paintings are digitally restored; then the ORB(oriented fast and rotated brief) algorithm extracts more uniform feature points in terms of quantity and distribution through linear transformation image enhancement and improved adaptive thresholding to ensure the accurate overlay of AR virtual content through feature point matching; finally, the color of virtual content is corrected through histogram matching before the virtual-real overlay to ensure the consistency of AR virtual-real fusion. Finally, the color of the virtual content is corrected by histogram matching before the virtual-real superposition to ensure the consistency of AR virtual-real fusion. The experiments show that the method can effectively solve the problem that the broken paintings and calligraphy cannot be exhibited directly and can increase the participation and immersion of visitors.
A Survey on Recommendation System for Future Researchers Using Classifiers

Recommendation system has revolutionized how users and websites communicate, becoming more and more vital. There are numerous uses for recommender systems in a variety of industries, including the economics, education, and science. The amount of digital information is growing swiftly due to the quick growth of information technology. Using big data analysis techniques, researchers use search engines like Google and Bing to find and filter material such as movies, music, and articles. To assist these academics, digital libraries like PubMed, DBLP, and arXiv offer sophisticated search interfaces; however, users must initiate the search process themselves. Therefore, taking into account the general trends of communities and specific interests, a tool is very helpful to actively discover and recommend the most intriguing research articles. The problem of locating the most relevant research publications for a specific academic is looked into. Existing solutions are unable to adequately address the suggestions of new publications since there is a dearth of previous data. As a result, this study on recommendation algorithms include algorithms for collaborative filtering. Additionally, a hybrid recommendation algorithm is created to incorporate real-time smileys, ratings, reviews, and temporal considerations.
Blockchain based E-Voting system with Facial Recognition

Traditional voting systems can be difficult for cer-tain groups of people to access, such as those with disabilities or those living in remote areas. A blockchain-based digital voting system can be designed to be more accessible, allowing more people to participate in the electoral process. Unlike the existing voting systems prone to errors and discrepancies, leading to a lack of trust in the electoral process, Blockchain-based e-voting systems aim to eliminate fraud and increase online voting security, transparency, and efficiency. The proposed system comprises three main sections: admin, voter, and candidate. The admin section authorizes the entry of voter and candidate information into a database. Voter registration involves the collection of facial features, while candidate registration requires basic information to create a database. During the voting process, a live camera is used to identify voters based on their facial features. The voter's eligibility is verified using the Random Forest classification method, and if eligible, a unique voter ID block is added to the Blockchain. The voter ID block is hashed with SHA-512, making it impossible to tamper with the immutable records on the Blockchain. The effectiveness of the Random Forest classification method is also evaluated.
Product Prediction using Sentiment Analysis and Linear Regression

An important research issue is how to carry out rapid and accurate business marketing by learning how to identify successful clients, reduce the cost of business competition, and understand customer feelings and buying preferences. This research work discusses the methodologyfor the sake of proper analysis of data that relate to client- provided evaluations and surveys for products sold over a period of time in the Amazon commercial centre. The data sets are analysed using sentiment analysis and linear regression analysis. It is discovered that the ratings like best performer rating or the star rating is optimistic and correlated with the reviews that are completely dependent on the analysis of sentiment provided by the classification algorithms like Naive Bayesian classification algorithm. Multiple regression analysis is used to determine the relationship between three indexes: helpful rating, star ratings and reviews. An optimistic correlation within the three indexes is found, and the reputation of the product in the online market improves eventually. It is proposed that positive reviews indicate a potentially successful or failing product depending up on the optimistic reviews that has been received star ratings. Finally, candidate words from the Amazon sentiment dictionary are chosen.
Intelligent System of Badminton Serve Action Based on YOLOv5 and OpenPose

In badminton matches, players from both sides hit the ball very fast. In peacetime training, coaches and players often need to watch videos repeatedly to capture action information, which leads to the slow efficiency of correcting action. In view of the above problems, this paper proposes a method of using YOLOv5 technology for target detection of players and using OpenPose technology to establish a comparative scoring model of serving motion. First, the standard action images of badminton players were shot in professional venues, and the key nodes of all players were marked to initially establish the exclusive data set of badminton matches. Secondly, players with data similarity concentration or fuzzy data are intercepted and trained for many times to obtain a large number of accurate data sets and improve the recognition degree of the model to occluded or fuzzy targets. Finally, through the comparison of the figure service action and standard service action, score from the perspective of difference, judge whether the player's action is standard, so as to guide each player's action. The experimental results show that the badminton analysis model has a more accurate detection effect on the players, greatly shortens the training time, and improves the training efficiency of badminton players.
Robot planning and control method based on improved time elastic band algorithm

For the planning and control system of differential mobile robots, the traditional time elastic band (TEB) algorithm has a long obstacle detection time and cannot complete dynamic planning in complex environments. At the same time, the control quantity calculated by TEB based on the trajectory point sequence difference has a jump, which results in poor control effect and unstable steering. By simplifying complex obstacles with RANSAC (Random Sample Consensus) algorithm, the collision detection time can be reduced and the algorithm computation can be reduced. At the same time, based on the optimal preview strategy, a nonlinear proportional function of tracking error is defined to recalculate the robot control quantity, so as to reduce the jump and make the operation control more stable. Finally, a robot navigation system based on ROS is built for experiments. The experimental results verify the effectiveness of the method and improve the real-time performance and reliability of differential robot planning and control.
A dual BIOS safe startup method based on national devices

Because the information technology is developed rapidly, storage computing devices have been widely used in the field of information. When more and more important data is stored in storage computing devices, the reliability and security of storage computing devices for data has become an important issue. But in the use of storage computing devices, there is a probability that the storage computing devices cannot be started the system due to the abnormal operation of BIOS, which will be a potential security risk of causing data loss. Aiming at this problem, a dual-BIOS safe startup method applied to the nationwide production of devices has been designed. Specifically, the storage computing device contains two BIOS with two mirror data backups in each BIOS. When one mirror data of the primary BIOS is damaged, the device will automatically switch to another mirror data or switch to the mirror data of the secondary BIOS. Simultaneously, a warning signal will be sent to the system when the device system is started successfully. Then the failed BIOS will be automatically troubleshot and updated. The security startup method designed in this paper can not only improve the reliability of storage computing devices and data security, but also enable BIOS to have self-recovery function.
Method of Clarity Evaluation of Shoes in the Video Based on Multi-feature Fusion

Regarding the difficulty in analyzing shoes with varying clarity in videos, a multi-feature fusion based clarity evaluation method is proposed and studied. First, shoe images are detected and cropped from videos and then operators including HSV, GLCM and DWT are used to extract the color and texture features. Then the extracted features are fused with features from deep convolutional network to form the clarity score of the corresponding shoe image. Experiments show that the output of the proposed method is highly consistent with human subjective feelings. Thus, the proposed method can be used as a data filtering preprocess method to rule out vague shoe images, offering efficient and reliable digital support for subsequent shoe sample identification.
Visual Recognition for ZELDA Content Generation via Generative Adversarial Network

In video games, procedural content generation has a strong history. Current procedural content generation strategies, such as search-based, solver-based, rule-based, and language-based techniques, have been used to create levels, maps, character models, and surfaces in games. There has been a research area dedicated to game content generation. More recently, Generative tasks have been in charge of a wide range of content creations that are relevant to games. Although some front-line Generative Adversarial Networks (GANs) are used independently, others are used in conjunction with more traditional techniques or an intel-ligent environment. GANs model suffers from a problem known as mode collapse where duplicate content is generated. In this ar-ticle, we have applied a simple Generative Adversarial Network, Deep convolutional Generative Adversarial Network(DCGAN), and Wasserstein Generative Adversarial Network(WGAN) to the ZELDA data set for levels of content generation and conclude the results of the basis of visual recognition. Results show that WGAN generates visually good content.
Real time DNN-based Face Mask Detection System using MobileNetV2 and ResNet50

Coronavirus has changed the entire world. Studies indicate that to minimize the spread of the virus it is advisable to use masks for maximizing safety and keeping the community safe by slowing down the spread of the coronavirus. However, it becomes tedious and un-feasible to manually check each and every person who wears a mask or not. In that regard, technology presents digital and innovative solutions for this complex problem. This research study proposes a novel face mask detection algorithm. For face mask detection, Real Face Mask Detection dataset which consists of 4095 images in which mask images are 2165 and without mask 1930 images are taken to train and test the model using various pre-trained algorithms like (VGG19) besides proposing 2 different algorithms. The other models compared and tested are ResNet50, and MobileNetV2. These models are trained and tested in a Google Collaboratory environment with the help of TensorFlow and Keras software. A comparative study is made between these algorithms to decide which is the one that is the most suitable algorithm for the environment based on different parameters. The final model is applied to random images to check the accuracy of the model.
Integration of Blockchain Technology for Enhanced Wireless Networks

The purpose of the study is to identify the usefulness of block-chain technology in wireless communication. To evaluate the role of block-chain and illustrate the significant factors that is affecting wireless communication through the block-chain. Secondary data and thematic analysis have been done to reach the aim of the research. Block chain and knowledge sharing are the key components in the digital era that allow heightened personal security from several aspects. Wireless communication caters to the need of block chain technologies where instance transparency, as well as security, is included. The study ensures that training boot camps are of immense relevance and significance while the future of mobile technologies is concerned. The worldwide spread of block chain technology has become the growing concern in the digital era which has been significant in wireless technologies
Visual Distance Fraudulent Detection in Exam Hall using YOLO Detector

Today's situation is risky because safety and monitoring must come first. It is defined as the action that is unusual in nature that deviates from the norm in a specific context. Exams are frequently monitored by invigilators manually or by video surveillance in exam halls all around the globe. Monitoring a test facility involves a lot of the personal and time. When using interdisciplinary approaches, the manual process of exam rooms is likely to be inaccurate. When created, an “Abnormal Behavior Detection Technique” would not only help identify hazardous actions, but also help reduce them. This study will cover a broad variety of strategies for outlier identification and classification, as well as their pros and downsides. The entire future of the any new unusual species uncovered in the exam auditorium would be purposeful after the experiment.