Reviewerly/exHarmony huggingface dataset:
The Fusion and Verification of 2D Human Skeleton and 3D Point Cloud based on RealSense

Recognizing the human body’s 3D coordinates is important for human-robot collaboration because the robot might need to deliver objects to human or avoid the collision. This study proposed a method to concatenate the extracted human skeletons from RGB sensor with depth information from the Intel RealSense D455 Depth Camera for accurately estimating 3D human coordinates. First, MediaPipe pose estimation was used to extract human body skeletons. By aligning the skeleton joints with the corresponding depth information from the depth camera, the overlapped depth information of each skeleton point can be obtained. The 3D skeleton point cloud can be generated by computing the coordinates of skeletons through RealSense SDK and OpenGL. To validate the extracted depth coordinates, the experiments were conducted at various distances of testing subjects. The experimental results showed that the measurement error is within 1.5% at distances of 1, 1.5, and 2 meters, establishing a reliable reference for the proposed approach.
Revolutionizing Industries Through IoT, Blockchain and AI Integration

The integration of the Artificial Intelligence (AI), Blockchain technology, and Internet of Things (IoT) has the potential to revolutionize a wide range of industries and applications. The Internet of Things (IoT) is a unified network of physical gadgets, automobiles, smart apartments, and other items that are built-in with sensors, software, and network connectivity to gather and share information. By integrating IoT with Blockchain technology and AI, it is possible to create decentralized, secure, and intelligent systems that can automate various processes and decision-making tasks. For example, in the supply chain industry, the integration of IoT, Blockchain, and AI can enable tracking real-time of goods, reduce the risk of fraud, and improve efficiency. In the healthcare sector, the integration of these technologies can enable the secure exchange of medical data, assist in the diagnosis and treatment of patients, and optimize drug development. In addition, the integration of IoT, Blockchain, and AI can also enable the creation of smart cities, where various urban systems such as transportation, energy, and waste management can be optimized through the use of intelligent and decentralized technologies. Overall, the integration of IoT, Blockchain, and AI has the potential to transform a wide range of industries and software's, creating new opportunities for innovation and improving the efficiency and security of systems.
Grade Classifcation of Breast Cancer using Deep-Learning

Breast cancer(Bcancer) in India has a high mortality rate, with one woman dying for every two newly diagnosed. The primary reasons for this are delayed diagnosis, advanced stages of the disease upon presentation, and inadequate facilities for early detection and treatment. Bcancer does not often show early symptoms, making it difficult to prevent. Early detection can significantly improve the prognosis and survival rate by allowing for timely clinical treatment. Automating some elements of Bcancer diagnosis can increase the number of women who are screened and detected early, ultimately reducing the number of late-detected cases. By combining traditional and computer-based diagnosis methods, pathologists' workload can be reduced, and performance can be improved. Identifying and classifying subtypes of Bcancer accurately is a crucial clinical task, and automation can save time and reduce errors.further,Two models have been created for classifying histopathological images into grade I (G1), grade II(G2), and grade III(G3) BCancer. One model is designed for binary classification, while the other model is designed for multi-class classification.Despite limited computational power and a small dataset, the results are satisfactory. The models can be improved for grade-wise classification of Bcancer. Deep networks have been designed with computational tricks such as ReLU, dropout, and batch normalization to improve performance. There are several algorithms to detect breast cancer, but there is currently no grade classification for breast cancer. In this paper, our focus is on classifying the different grades of breast cancer, which will help patients understand the severity of their condition. We classify G1 as the least dangerous stage, G2 as more dangerous, and G3 as the most dangerous stage.
YogMaster: Detection and Correction of Yoga Postures Using Augmented Reality

Yoga is becoming increasingly popular due to its positive effects on the body and mind. Although many individuals engage in yoga practices, it is important to use proper instruction from an experienced instructor to avoid potential health issues. The proposed solution aims to address this issue by developing a web-based application that uses a camera to capture the user's yoga poses. The TensorFlow MoveNet model then processes the captured images which identifies 17 key body points. This information is then fed to a Neural Network model for Classification which verifies the accuracy of the pose and if necessary, provides correction through a Correction model. The user will be alerted to any corrections through both visual and audio cues. The results demonstrate that the model has 99.47% accuracy.
Multispectral Satellite Image Dehazing and Pollution Estimation

Since the introduction of computer vision, the difficulty of image dehazing exist. Due to the presence of numerous air particles, which causes hazed photos, images taken during bad weather circumstances sometimes appear to be of inferior quality. To eliminate haze from the photos, this study uses a static technique called dark channel before. Ground based air pollution sensors give regular readings but have limited coverage. Satellites offer broad global coverage but poor spatial resolution and insufficient data on the vertical distribution of pollutants. It is not easy to estimate the concentrations of pollutants at the surface, where they are produced. In order to address this problem, a framework has been developed. This research work focuses on providing an approach incorporating the dehazing of a multispectral satellite picture and providing a pollution range of the region by estimating the percentage of nitrogen oxides.
Blockchain with Client Assistance with the Transaction Reason

A shared database divided across exclusive groups is a block chain. It offers to tie new applications together and deal with any repercussions involving open places. In any event, the square chains suffer from the harmful effects of excessive torpidity and low throughput. The gathering of more spectacular square chain-based apps is affected. This study suggests an additional strategy for the development of square chains: shifting naturally off-the-top tasks to the client-side to make the square chain-focus as quick as is reasonably possible. Here, the hash is created using SHA Calculation. The client is expected to receive the plan from the support bundle, which the association included. After receiving the approach, the client can increase their record for the executive branch. The association will provide the client with pricing information to aid the document, and the customer will canning pay the association's total fee. Following that section, the board forwarded the Record to the assistance group, who kindly adjusted the development date to the Client. The Document can be obtained by the customer following the Help pack's redesign. Here, AES is being used for security as a purpose to encrypt the substance. After customer interest waned, the back end of the blockchain security procedure was implemented.
Reopening, Repetition and Resetting: HCI and the Method of Hope

This paper attempts to (re)theorize the nature and role of hope in collaborative and interventionist CSCW and HCI projects. Building on work in anthropology, STS, CSCW and HCI, and extended reflection on the 3Dprintability project - a multinational collaboration dedicated to the 3D printing of prosthetics in sub-Saharan Africa and Southeast Asia - it advocates for a more pluralistic and action-oriented understanding of hope built around moments of collaborative reopening, repetition and resetting. It then argues for a more robust and open-ended HCI method of hope that can change the way interventionist projects are conceived, documented, evaluated, and communicated, both in the HCI literature and in more public-facing accounts.
Resisting Visual Hacking: A Novel Graphical Password Authentication System

Password-based authentication is a widely used method to ensure security and privacy in computer applications. However, the selection of weak passwords and insecure input methods by users is considered the “weakest link” in the authentication process. Instead of employing arbitrary alphanumeric sequences, individuals tend to opt for passphrases that are either mnemonic or abbreviated in nature. With the proliferation of web and mobile applications, users can access these applications at any time and from anywhere with various devices. Although this convenience is highly desirable, it also increases the likelihood of passwords being exposed to Visual Hacking. The forms of attack may be executed through either direct visual observation or the utilization of external recording devices in order to obtain the user's login information. In response to this issue, our team has devised an authentication system that centers on graphical passwords and is made to combat the threat of Visual Hacking. The system incorporates a three-layered security mechanism, comprising color and pattern matching functionalities in conjunction with password verification. This makes it highly challenging for potential attackers to deduce or limit the password even amidst numerous camera-based hacking attempts. Additionally, we have created and put to the test a prototype of the system to assess its user-friendliness. Our experimental results indicate that our system can resist Visual Hacking more effectively than previous authentication systems while still maintaining a high degree of usability and security.
Combination of Semantic Segmentation and Skeleton Estimation for Human Hands Detection

Recognizing the human hands’ positions is crucial during human-robot collaboration because the robot might need to deliver objects to humans or avoid a collision. The aim of this study is to accurately position human hands in the collected video on a real-time basis when a human operator processes the manufacturing tasks. Although the pose estimation methods such as MediaPipe or Detectron2 can recognize the skeletons of the human body, they cannot detect the human body contour, particularly for palms and fingers. Besides, the existing human hand positioning models such as BodyPix require substantial computational resources to achieve high accuracy. To tackle this issue, a hybrid framework was proposed to combine semantic segmentation and skeletal detection, obtaining pixel-level information in regions of interest (ROI) to achieve accuracy in a cost-efficient way. The experimental results show that the proposed hybrid model outperforms the existing lightweight model on the simulated data with maintaining stable resource usage. For future research, it is suggested to expand the application of various hybrid models and to conduct a further in-depth analysis of different human body parts. Through these extended investigations, it is anticipated that the accuracy and efficiency of human body part positioning can be further improved.
Resume Classification using Support Vector Machine

Recruitment is one of the company's most important and crucial tasks. A company needs to select the best person for the job who has all the skills to fulfil the duties. A resume is a reflection of a person's abilities. These days, companies receive an enormous number of applications for a job. Traditional recruiting methods such as analyzing and class manually analysing and classifying resumes are becoming consuming a lot of time. So, there is a need for automation of the resume analysis and classification phase. The automation should work in such a way that it will take resumes, job roles, and job descriptions as input and classify whether the resumes are suitable for the job or not. This can be done with the help of text analysis and classification algorithms such as the Support Vector Machine.
Tulu-Based Algorithmic Language Model

Tulu is an Indian language with many speakers but few usable data sources. The 3-source model uses verified data along with data from users to build an understanding of the Tulu language. It implements a modified Hidden Markov model to weigh Part Of Speech (POS) sequences to decide which sequences are yet to be understood or which sentences are more likely. Past attempts to build models for low-resource languages have mainly attempted to use similar languages which are high-resource, or else used rule-based translation. These have a moderate amount of success with regards to accuracy when compared against high-resource models. Efforts to combine the two methods have also been made. A combination of a limited database, user inputs, and community feedback is employed in the 3-source model to create a language model. This model can be replicable for other languages. The project has three intended outcomes: one, to develop a dataset for Tulu words and sentences in a usable format; two, to be able to generate and translate sentences which are accurate in terms of syntax and semantics; and three, to be able to infer results from user feedback and improve its understanding of Tulu. Possible applications of the system include preserving languages having small communities, document translation, language learning applications, and simplifying error correction in translation. The system provides a weighted average accuracy of 24.42 % using Word Error Rate (WER) as the evaluation metric.
Analysis for Designing a Symptom-Based Health Improvement System Using Machine Learning

Healthcare providers are under more and more pressure to save costs and improve patient outcomes. The creation of symptom-based health improvement systems that make use of machine learning algorithms is one such strategy for reaching goals. The proposed methods employ supervised and unsupervised machine learning techniques to discover patterns and associations between symptoms and probable medical diseases. Based on each patient's unique symptom profile, the system seeks to provide them with individualized advice. The system's ability to recognize probable medical problems based on symptom patterns has been tested using a huge dataset of patient records. The technology strives for high accuracy rates in recognizing probable health issues and gives patients personalized advice depending on their symptoms. Furthermore, it helps in reducing healthcare expenses by identifying potential health conditions at earlier stages, which can help prevent unnecessary treatments and hospitalizations, as well as by providing insights into symptom patterns and relationships between symptoms and potential health conditions, which can help healthcare providers make more informed decisions. This research analyzes those proposed solutions in brief and presents us with an insight into the number of works and research done on the topic of symptom-based health improvement systems.
Automatic Detection and Classification of Wild Animal Species Using YOLO Models

Using a camera trapping framework, which employs cameras activated by sensors to record a burst of pictures of animals in their habitat, it is feasible to monitor animals in the open. This paper explores the effectiveness of YOLOv3 and YOLOv5 object detection algorithms in automated animal tracking through the use of computer vision. A dataset of six animal classes was utilized to train and test both models. The results demonstrate that YOLOv5-m achieves an 81% detection accuracy rate, surpassing that of YOLOv3. In addition, the two models were evaluated based on detection speed and model size, with YOLOv5-m providing higher accuracy and comparable speed and model size to YOLOv3. Overall, these findings highlight the potential of computer vision and deep learning in automated animal tracking, and emphasize the importance of selecting appropriate algorithms for various use cases.
Real Time Object Detection based on RCNN Technique

This research provides a method for recognizing objects in real-time on any model-enabled device, regardless of the environment. In computer vision, object identification and training are thriving, all-encompassing, and elusive areas. CNN are used in this study to develop a multi-layered model for categorizing items into specified classes. Following object identification, the proposed algorithms assign a class label by assessing many photographs. These things are identified using more comprehensive feature maps. Recent advances in deep learning for image processing have made this possible. Such images might be found in the video frames used to train the model. Multi-scale feature maps are used for detection and filters, each of which has separate default boxes so that various aspect ratios can be accomodated. The training continues until there is a discernible reduction in the error rate. The performance of the trained model is used to evaluate test images. Proposed object recognition method employs a single-shot multi-box detector methodology and a faster region CNN architecture. This helps us to improve object detection method's computational efficiency and proposed model can identify and classify the given input image data more accurately.
Control of A Virtual System with Hand Gestures

In the field of Human-Computer Interaction (HCI), significant advancements have been made. These advancements aim to bridge the gap between the physical and digital realms by introducing various modules. Traditional techniques predominantly involve relocating the tracking ball or adding buttons to the mouse. However, our proposed solution offers novel mouse control options. Our solution incorporates hand tracking, marker motion tracking, and colored object tracking to facilitate movement. The primary focus of this research proposal revolves around mouse cursor movement, as well as click event-based object detection and marker identification. It demonstrates how computer vision technologies, using cameras, can perform all the functions that a mouse currently accomplishes (e.g., left/right click, double-click, scroll, etc.), including tasks like picture segmentation and gesture recognition. Gesture identification poses a challenging problem that necessitates a diverse range of techniques such as motion modeling, motion analysis, pattern recognition, and machine learning. For instance, the ability to click and move the mouse can be achieved through the recognition of colored objects. The fundamental objective of this approach is to utilize a webcam to construct a virtual device for human-computer interaction. A method for recognizing hand movements and their associated gestures has been developed, encompassing all relevant elements. Consequently, numerous mouse operations can be performed when motion is recognized.
Beyond Face Value: Identifying Irony and Sarcasm in Short Texts Through Multiclass Classification Techniques

This research study presents a comparative analysis of multiclass classifiers for detecting irony and sarcasm in short texts. With the increasing use of social media, it has become important to develop accurate and efficient algorithms for detecting irony and sarcasm, which are often expressed implicitly in text. This study has evaluated five multiclass classifiers, including Naive Bayes, linear classifier, XG Boost, KNN, and SGD, using different text representations, such as count vectors, word-level TF-IDF, and hash vectors. The results showed that the linear classifier using word-level TF-IDF achieved the highest accuracy of 74.39%, while the KNN classifier using hash vectors achieved the highest accuracy of 75.16%. However, all classifiers exhibited sensitivity to certain keywords and phrases, indicating the need for further research to improve the robustness of the classifiers. This study provides insights into the strengths and weaknesses of different multiclass classification approaches for detecting irony and sarcasm in short texts, which can guide future research in this field.
Fake Review Detection and Classification Using Improved Convolutional Neural Network on Amazon Dataset

Fake reviews are a growing concern for e-commerce websites and other online platforms. To tackle this issue, researchers have developed an advanced convolutional neural network system that can detect and classify fake reviews. This system has been specifically designed to analyze the Amazon review dataset, and by integrating various text pre-processing methods and an innovative attention mechanism, it has significantly improved its classification accuracy. In fact, the proposed model outperforms existing state-of-the-art techniques in terms of accuracy and F1-score, with an impressive accuracy of 0.97. These results demonstrate the effectiveness of the improved CNN model in accurately detecting and classifying fake reviews, and provide a valuable tool for maintaining the authenticity and trustworthiness of online reviews.
An Effective Machine Learning-based Segmentation and Feature Extraction Technique for Muscular-Disorder

Application of technology for the classification of sports and exercise, Yogasan identification is now common practice for numerous people in modern society especially when dealing with muscular disorders. Over the past three decades, research has focused on motivating and difficult problems related to the usage of computer vision for target recognition and identification. The skeleton parts of the head, neck, arms, hands, legs, and feet are designed and stored as a vector in this research to identify the human yoga asana pose. These skeleton elements are also responsible for head movements, hand postures, and standing postures. Every single interaction between the skeleton joints makes a pose, and every feature is described as a set of moveable joints. The weighted skeletonization-based image segmentation is proposed for the enhanced Yogasan recognition from the selected keyframes, the most relevant keyframes are selected using the Euclidean distance measure. The performance of the proposed method is analyzed by considering the features as well as the training percentage. The accuracy, precision, and sensitivity based on the features of the multi-SVM classifier are 89.020 %, 89.720 %, and 90.855 %, and the training percentage using the multi-SVM classifier is 94.268 %, 93.297 %, and 97.375 %.