Reviewerly/exHarmony huggingface dataset:
Investigative and Analysis Guidance in Walking Pattern for Orthopedic Subjects

A proper post-operative care should be provided to orthopaedic patients in order to recover quickly from surgery or leg fracture. If no proper care is given, the recovery period will be extended. The primary goal of the proposed strategy is to reduce leg strain while maintaining a normal gait. As a part of the proposed effort, a footwear equipped with a sensor unit will be developed to monitor and direct the patient by modifying the weight distribution pattern for the legs. If the patient tries to apply extra pressure to one of the legs, the sensor equipped footwear will send an alert. An arm band with a microcontroller, battery, and a Bluetooth module allowing autonomous and wireless operation is meant to provide notifications to patients. The proposed research findings are entirely focused on both legs equally as 50% to each, with an average variation of 1.08333%.
Analysis of Bio-Inspired Optimization Algorithms in Feature Selection for Facial Expressions Recognition

There are several popular methods for extracting facial expression features but finding the most significant features of a particular data sample and weeding out the rest that doesn't help us make better decisions has always been difficult. The Feature selection algorithm aims to solve this issue by removing irrelevant and repeated features. Feature selection involves including vital features or removing unimportant ones without changing them. Statistics methods traditionally used are ineffective as they increase the number of data samples and features to each sample. Feature selection techniques are used for several reasons [1]: (1) It eliminates unnecessary features from the model and reduces its complexity, (2) Accelerates the training process of ML algorithms, (3) Dimensional reduction reduces over-fitting, and (4) Builds a more accurate model. Recent advancements in swarm intelligence (SI) technologies have helped overcome these challenges. This research study compares various feature selection methods and then analyzes the meta-heuristic approaches based on swarm intelligence to solve the optimization challenges.
Multi Object Detection and Shadow Identification in Images: State-of-Art

When an image is classified, a class label is attached to it, and when an object is located, a bounding box is drawn around the object or objects in the image. There are two jobs involved in object detection: drawing a box around each object in the image and assigning it a name. Video surveillance, scene interpretation, and object recognition all benefit from shadow detection and removal. There may be major issues with object merging and object loss as well as object misinterpretation and form alternation in various visual applications such as segmentation, scene analysis, and tracking when shadows are ignored. It has been presented in the literature that shadow identification and removal can be accomplished using a variety of techniques. Object detection is a technology that uses moving images or digital images to recognise objects in the real world. The object is a member of a class that includes things like vehicles and people. There are several industries where object detection is critical to the safety and productivity of the business. Image recovery, safety approaches, research purposes, machine system assessments, computerised vehicle structures are only some of the many uses for object detection. A shadow is a visual phenomenon that occurs when an area of a scene is occluded by the primary source of light. Shadow detection and removal is a complex process that relies heavily on texture analysis and colour information. Machine learning-based object detection is a fundamental application of machine learning because of the powerful capabilities of feature learning and feature representation. It is a difficult problem to solve when it comes to pattern recognition and computer systems. This research presents a detailed review of shadow detection and removal techniques with object detection in images.
Enhancing the Resolution of Ancient Artworks using Generative Adversarial Networks

Inp ainting is a form of conservation that involves filling in damaged, deformed, or missing areas of an artwork in order to present a whole image. This method may be imp lemented in both traditional and digital forms of visual art, including chemistry-based photo prints, paintings done in oil or acrylic, sculptures, and digital video and still photos. The process of increasing the quality of a picture by filling in missing pixels using a digital image editor is known as “digital image inpainting.” Through the utilization of the Generative adversarial network, the focus of this investigation is to improve both the originality and the overall quality of the input picture. to provide a process for the long-term digital preservation and repair of historical works of art for future generations.
Data Support Algorithm for Hybrid Guiding Reform of Stable Cloud Classroom based on Dual Disaster Recovery System

This paper discusses about the utilization of data support algorithm for performing hybrid guiding reform on stable cloud classroom based on dual disaster recovery system. Data encryption is the core technology to ensure the security of big data storage. The cryptography system plays an important role in all aspects of data storage, use, and transmission. Data Guard meets the requirements of disaster recovery and backup under simple environmental requirements. It has good reliability, high security, and eliminates the hidden danger of single point of failure. Fully utilized with relatively little hardware resource investment, with very little impact on performance. Enterprises can grow demand according to the later stage of the business system. With this core integration, the designed model is then applied to the hybrid guiding reform of stable cloud classroom. The system is implemented and the performance is tested.
Performance Comparison of Convolution Neural Networks on Weather Dataset

Deep neural networks (DNNs) are quickly becoming a crucial enabling technology in a wide range of application fields. However, due to the long inference time and resource requirement, it is almost infeasible to implement DNNs on battery-powered resource-constrained embedded systems. Simply transferring computational load to the cloud is frequently discouraged due to security concerns, excessive delay, or a lack of connection. In the proposed work, four notable convolutional neural network-based models explicitly, VGG19, MobileNet V2, InceptionResNet V2, and Inception V3 have been considered. The proficiency of the considered models mentioned has been assessed on the custom weather dataset for multiclass classification. CNNs are genuinely strong for image characterization as the possibility of dimensionality decline suits the gigantic number of parameters in a picture.
Machine Learning based Recognition of Bangla Handwritten Characters

In this paper, we propose a method to recognize Bangla handwritten characters using CNN based machine learning techniques. Our proposed model takes input of single Bangla character images and recognizes it. We worked with 50 basic Bangla characters, 10 numeral digits and 13 special characters. A total of 24231 images from CMATERDB dataset have been used here. Our proposed model achieved 99.06% accuracy on alphabets, 99.75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy on digits and 99.15% accuracy on special characters. We look forward to upgrade this model to a complete Bangla handwritten speech recognition system in future.
A Comparative Survey on Parts of Speech Taggers for the Marathi Language

Natural Language Processing relies heavily on the POS tagger. The POS tagger is a useful tool for tagging each word in a phrase with parts of speech tags. NLP Applications performing various tasks use POS tagging as a crucial initial step. In terms of data tagging, the POS tagger for English is widely available, however, there is no similar tagger for Marathi. Marathi is a morphologically complex language with regional speech differences. Because of the ambiguity in the language, as well as its highly inflectional structure and free word order, establishing a successful POS tagger in Marathi is tough. This article provides a comprehensive overview of POS tagging for the Marathi language and its variations. Various POS Tagging models and approaches are investigated in this research. Tokenization in computer languages is similar to tagging in natural language processing. Choosing the appropriate tag for the scenario might be challenging for POS taggers. Research has been conducted to discover a solution to this conundrum.
Electronic System Design and OpenGL Simulation of Digital Painting Art Intelligence Platform

Electronic system design and OpenGL simulation of digital painting art intelligence platform is studied in the paper. A high-level hand-painted board is used for the drawing process of the CG digital painting. In the color control system, the CTL100WL hand-painted board is used for image input. To solve this problem, this paper uses CUDA to accelerate the extraction of image pixel data. In fact, analyzing the characteristics of the code stream data shows that the original data is aligned every 40 bits. With this core idea, the OpenGL is then integrated for the systematic optimization. Furthermore, the system design pattern is modified and tested.
Speech Recognition based Assistant Bee: Vision to the Impaired

The Internet and com puters are almost changing our lives every day and improving efficiency in all fields. There are millions of people who are visually impaired. According to research, people with visual impairments are 31% less likely than people without disabilities to use the computer. They are unable to use technology for ordinary tasks such as browsing the web, obtaining data, and executing various desktop functions. Assistant Bee software provides friendly assistance to the visually impaired to perform activities on desktop computers. The proposed model provides voice-based assistance to the visually impaired thus supporting the usage of the desktop computers in day-to-day acti vities. This Assistant Bee has been beta-tested through impaired people and it has proven to be efficient in providing user friendly voice based assistance to visually disabled.
Web Content Based Features for Malicious Web Page Detection Using Machine Learning

Malicious web pages are well-known threats in cyberspace. Malicious webpages and their URLs appear to be legitimate webpages in cyberspace and are used to steal personal information from internet users (victims) or to install malicious software on the victim's machine in order to gain further access and make the victims more vulnerable in cyberspace. Identifying such web pages in cyberspace is a difficult task for internet users. Researchers use different techniques with different sets of features to detect malicious web pages in cyberspace. Most of them extract vital features from URL text for malicious webpage classification due to their simplicity and risk-free nature. Some of them extract the limited number of features from the webpage content for better classification along with other features. Our proposed system considers features that are extracted only from the webpage content and use a machine learning model for classification. The accuracy of the existing and proposed systems is demonstrated experimentally. The result shows that the proposed system is more efficient.
Solar Energy Potential Assessment: Analytic Hierarchy Process Model for Selecting Solar Energy Sites in Vietnam

In recent years, the usage of solar energy has become one of the most popular immense renewable energy sources. It is a clean, environmentally friendly, and secure power source. Despite the availability of solar energy potential assessment since the early days of solar plant, the emergence of new proposals and the current debate on the development of an energy rating standard mean that this can still be considered as an open question. This paper presents an Analytic Hierarchy Process (AHP) model for selecting solar energy sites in Vietnam. Currently, the proposed factors affecting the decision to select the potential location of the solar power plant have not been unified, and this is still considered an open question for consultants and regulators. In this study, using AHP to determine the weight of factor to select solar energy site including Society, Technology, Environment, Economy and Policy in Vietnam. From the results, in the 5 factors of level 1 the technical factor has the largest weight, followed by the factors of Environment, Economy, and Society and finally is the Policy factor. The study also identified three key criteria affecting the decision to select the potential location of a solar power plant: Solar radiation intensity, Investment costs for internal infrastructure and the impact on the regional landscape. Furthermore, the results of this study will be the reference for further studies being able to elaborate on planning and supplementing of electricity development planning or appraising the plan to select the location of building a solar power plant.
Natural language inference model for the Vietnamese language with machine learning algorithms: a view from “Truyen Kieu”

Inference models in natural language predict whether a hypothesis sentence can be inferred from a premise sentence or not. Understanding the inference properties of natural language can elevate the representation of the semantics context. Many studies on this field are in English, but few are in Vietnamese. So, we introduce the Natural Language Inference corpus in Vietnamese using the Tale of Kieu, “Truyên Kiêu”, which Nguyên Du wrote. The dataset includes the premise of the poems in “Truyên Kiêu”; the hypothesis is that the conclusions are gathered from the “Kieu fortune-telling”. We make the following contributions: (i) provided the corpus for applying machine learning to semantic representation in the Vietnamese language; (ii) inference models in a natural language using machine learning method to predict the relationship between premise and hypothesis sentences. Finally, our research helps keep the traditional beauty of “Truyên Kiêu” in general and the culture of “Kieu fortune-telling” in particular.
Design and Develop Optimal Pathfinding Algorithm Applied in Book Accessing and Returning for Autonomous Mobile Robot in Library

This paper presents a visual optimal pathfinding algorithm that avoids obstacles on the way to the destination in a simulation environment, and the idea took inspiration from the optimization for solving the Traveling Salesman Problem. Firstly, using a webcam to scan the book information by QR code to know where the book's location is, what is the type of the books and use location information to send to robots for autonomous navigation duty. After that, the order list of books borrowed or returned is calculated and visualized using the K-means Clustering and Genetic algorithm. Then, to apply the optimal algorithms for a practical situation, Robot Operating System (ROS) is considered. In the ROS platform, to make sure the robots operate precisely, parameters of global, local cost maps and algorithms for global, local planners need to be adjusted. The robots will follow the order calculated path and announce when arriving at the book's location successfully. To track if the robots work correctly, a software for users to observe robot operation is created. The proposed algorithm works quite well in a simulation environment through implementing experiments. It is also a good foundation for further research into another autonomous field.
University Indoor Scene Classification using Transfer Learning

Image classification is used to classify images using a machine without any human intervention. This will help obtain all possible information of that particular capture just by feeding the image or by just clicking pictures from a device. The proposed study has created an extensive dataset of labs and its amenity by comprising 4502 images. Here there are 4 classes in the dataset: Apple Lab, Sophos Lab, Sophos Rack, and Virtual Reality Lab. The proposed work is based on transfer learning. Data augmentation was performed after the proposed model was passed through VGG 16, ResNet50, Inception V3, and Xception pre-trained models from which features were extracted automatically and categorized into 4 distinct classes. Size of the model, inference time, training accuracy, testing accuracy, recall, precision, F1 score, and FLOPs were evaluated while analyzing through pre-trained models. Best experimental results were obtained using Xception pre-trained model among all 4 pre-trained network models with 99.90% as training accuracy, 99.75% as testing accuracy, 99.35% as recall, 99.85% as precision, and 99.60 % as F1 score; having a model size of 83 MB.
Lymph Node Morbidity Diagnosis Using Multiclass Machine Learning Models

Lymphography, considered a corner stone in prognosis and diagnosis of lymphatic disorders continues to be a gold standard of reference in spite of the advancements in health technologies. However, analyzing the lymphatic characteristics implicitly curtails the diagnostic accuracy of few dreaded cancers such as lymphoma, malign lymph's etc., Thus to provide objective diagnosis computer aided diagnostic tools (CAD) play a prominent role. In this research, the role of robust machine learning classifiers in classifying lymphatic characteristics is proposed. The highest accuracy obtained by considering the prominent lymph characteristics is 85%. A good balance between specificity and sensitivity was obtained. The proposed system can be employed in a clinical scenario particularly in regions with poor medical infrastructures.
Security Access Assurance Related Technologies Survey

The security and reliability of power grid dispatching system is the basis of the stable development of the whole social economy. With the development of information, computer science and technology, communication technology, and network technology, using more advanced intelligent technology to improve the performance of security and reliability of power grid dispatching system has important research value and practical significance. In order to provide valuable references for relevant researchers and for the construction of future power system related applications. This paper summarizes the latest technical status of attribute encryption and hierarchical identity encryption methods, and introduces the access control method based on attribute and hierarchical identity encryption, the construction method of attribute encryption scheme, revocable CP-ABE scheme and its application in power grid data security access control. Combined with multi authorization center encryption, third-party trusted entity and optimized encryption algorithm, the parallel access control algorithm of hierarchical identity and attribute encryption and its application in power grid data security access control are introduced.
Research on the Evolution Path of Big Data Service Ecosystem based on Data Mining

The big data service ecosystem, as an emerging form of data service innovation spawned under the big data environment, explores the evolution path of the big data service ecosystem, and provides theoretical support and reference decision-making for the construction and development of my country's big data service ecosystem. First, combine the theoretical ideas of the service ecosystem, analyze the connotation and characteristics of the big data service ecosystem, and build the general structure of the big data service ecosystem; secondly, use data mining theory to build big data mining, and analyze the big data service ecosystem on this basis System evolution path; finally, conduct empirical research through the big data service ecosystem. Research shows that the evolution of the big data service ecosystem has accelerated by 6.5%.
Transforming education through mobile learning to improve students' communication skills

This study aims to investigate the effectiveness of teaching materials based on mobile learning on the respiratory system theme in improving the communication skills of junior high school students. Teaching materials developed using the ADDIE model. This study involved 42 students of 8th grade which were divided into two classes with an experimental class using teaching materials based on mobile learning developed and control class using biology book IGCSE which commonly used in schools. The instruments used are the assessment of the quality of teaching materials, comprehension test sheets, and communication skills test. based on the results of data analysis; 1) quality test and understanding test of teaching materials based on mobile learning are categorized as good and suitable to be used as independent teaching materials, 2) teaching materials based on mobile learning can improve students' communication skills with N-Gain 0.35 included in the category moderate, and 3) test of effect size of teaching materials based on mobile learning are effective in improving students' communication skills compared to biology book.
Short-term Solar Power Prediction using Long Short-Term Memory in Solar Plant with Deep Learning Machine

Solar power is a clean energy source that has developed quickly with considerable attention. Solar energy is required more accurate predictions, which could be integrated into the power grid. Therefore, this project attempts to improve short-term solar power prediction's accuracy, utilizing the long short-term memory (LSTM) in a deep learning machine. The collected data is acquired from the solar system installed in Kaohsiung city, Taiwan. The historical sequential weather parameter and the collected data from the battery module are utilized as input features for the predicting model. To acquire the optimum performance, hyperparameter optimization is employed to construct the best sequential historical data of the LSTM model. The experiment results are compared with a recurrent neural network (RNN), indicating that the LSTM could predict short-term solar power better.
Analysis Framework of Comparative Literature Wisdom Model based on Cyclic Text Recognition Algorithm

In the recent and latest study, the existing models regarding text recognition with the combination of deep neural network is general focused on the end connected end models, that is, it does not require explicit text cutting, but converts direct text recognition into a sequence learning problem. Hence, the analysis framework of comparative literature wisdom model based on the cyclic text recognition algorithm is studied in the paper. Recurrent neural network has the specia functionality enhancement feature of meetwing with the sequence scenarios, but RNN will have gradient disappearance as the time interval increases. Therefore, the designed text model is then considered as the solution. Through the systematic simulation, the accuracy performance is validated.
Reinforcement Learning-based Adaptive Fuzzy Controller for Coupled-Tank Liquid System Optimized by Advanced Jaya Algorithm

This paper proposes a new reinforcement learning-based Fuzzy controller optimized with an advanced Jaya algorithm used as a robust control approach applied in coupled-tank liquid system. First, the parameters of fuzzy controller are optimally identified using Jaya algorithm based on reinforcement learning. Second, an adaptive fuzzy-based sliding mode surface is innovatively designed to guarantee that the closed-loop system is asymptotically stable using Lyapunov stability principle. The proposed algorithm is applied to accurately and robustly control the liquid level of the coupled-tank system. The comparison results with inverse fuzzy controllers and adaptive fuzzy controllers are fully presented to demonstrate that the proposed controller ensures an efficient and robust approach to effectively control the highly nonlinear uncertain systems.