Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Atmospheric Constrained Uncombined Multi-GNSS PPP

The general objective of this study is to analyze the performance of Precise Point Positioning (PPP) convergence and initialization while stochastically constraining the atmosphere. One specific objective of this study is to review the performance of dualand triple-frequency PPP solutions using uncombined measurements. The research question to be answered is whether there is any significant benefit in constraining the atmosphere, specifically the ionospheric parameter, in dualand triple-frequency PPP processing? For thorough performance analysis in the uncombined PPP case, data from 70 globally distributed multi-GNSS stations were processed for the month of February, 2016. The uncombined triple-frequency PPP results with GIM ionospheric constraints showed reduced convergence time compared to unconstrained solutions. At the 68 percentile, a 60% and 78% reduction in convergence was observed for dualand triple-frequency PPP with ionospheric constraints, respectively. However, more than 50% improvement was observed for constrained triple-frequency uncombined GNSS PPP solution convergence and initialization in the first 5 minutes as compared to dual-frequency PPP solutions. It was concluded that by applying atmospheric constraints in an uncombined multi-GNSS approach, significant improvements were observed for both dualand triple-frequency PPP implementations.
MOODIFY: Tailored, Personal and Multifaceted AI Assistant for Young Adult Mental Health Issues

Mental health issues are prevalent among young adults aged 20-35, who face stresses related to studies, work, finances, and personal relationships. We present Moodify, an Android application to identify, analyze, and alleviate mental health problems at an early stage. The app has three key features: 1) An emotion detection module that divides the face into lines to extract and predict emotions, then recommends uplifting songs and podcasts based on the user’s current state. 2) An autonomous chatbot for users to privately discuss their feelings and receive suggested coping strategies without revealing their identity. 3) Anonymous group chat rooms where users can socialize and discuss common problems anonymously. Moodify aims to improve mental wellbeing by allowing users to monitor their emotional state through facial analysis, vent feelings safely to an AI, and connect with peers undergoing similar issues while maintaining privacy. Evaluations demonstrate the emotion detection module can identify facial expressions indicative of mood with 82% accuracy. The chatbot is effectively being designed for reducing negative sentiment through supportive conversations. Moodify represents a novel mobile approach to enhancing mental health through emotionally intelligent systems and secure social features tailored to young adults’ needs. The app empowers users to privately manage mental health issues by increasing self-awareness, providing personalized support, and facilitating anonymous peer discussions.
A Survey and Analysis of Textual Content Based on Exploratory Data Analysis Technique and Opinion Analysis

This research paper presents an Exploratory Data Analysis (EDA) technique, Artificial Intelligence (AI), and Natural Language Processing (NLP) based approach for the analysis of textual content, and opinion analysis. Although technically difficult, the task is very beneficial. Using the natural language processing method, we addressed the problem of detecting violent content in text documents in this work. Researchers have used a variety of techniques, including machine learning techniques such as Naïve Bayes, Lexicon-based approach, and Encoding techniques and we have used datasets to compare the data. Therefore, it provides an opportunity for research to explore and recommend sophisticated techniques. For this paper, we examined a unique dataset.
Motion Planning with Obstacle Avoidance for Wheeled Inverted Pendulum System Based on PMP Approach

This work is mainly focused on the optimal motion planning problem of a Wheeled Inverted Pendulum (WIP) system, which is one of the benchmark mechanical systems used for the study of nonholonomic systems. Designing motion planning control law while respecting the nonholonomic constraints possesses a challenging control problem. We address the problem where the given system has to reach any desired target configuration from an arbitrary initial configuration by finding an optimal path which takes into account the path curvature, control effort as well as obstacles in the environment. Throughout the course of motion, it is desirable to maintain the pendulum body at the upright position. To tackle this problem from a variational point of view, Pontryagin's Minimum Prin-ciple (PMP) is utilized for deriving the necessary optimality conditions which finds a collision free optimal path with a reduced control cost satisfying the path smoothness constraints defined via an objective function. Numerical simulations are carried out on the derived nonlinear WIP model to validate the proposed theoretical results.
Research on Identification Method of Electricity Consumption Behavior of Small and Medium-Sized Enterprises Based on Random Forest

With the increasing demand for data monitoring of micro, small and medium-sized enterprises and the rapid development of digital grid construction, power data resources have increased sharply, which contains rich information resources. However, in the data analysis of micro, small and medium-sized enterprises, due to industry differences, regional differences, difficulties in scale division and other reasons, there are usually problems such as the size and industry characteristics of micro, small and medium-sized enterprises are not accurate enough, and the data analysis error is large, which further affects the later decision-making and policy development. In order to solve the problem that the electricity consumption of micro, small and medium-sized enterprises is not accurate enough, this paper proposes a random forest-based identification method of electricity consumption of micro, small and medium-sized enterprises. Firstly, the power consumption data of small, medium and micro enterprises are collected and pre-processed to establish a decision tree model. Then, the random forest model is constructed, and the power consumption identification model is established by training the power consumption data of small, medium and micro enterprises. Finally, the simulation is carried out according to the actual operation data of small, medium and micro enterprises in a city in Northeast China. The results show that the method proposed in this paper can effectively identify the characteristics of small, medium and micro enterprises and ensure the accuracy of the solution, which is of great significance for improving the safe and stable operation and economy of small, medium and micro enterprises.
Lyman α scattering in the Earth's hydrogen geocorona: 1.

The interpretation that the nighttime Lyman α observed from rockets is solar Lyman α that has been transported to the night side by the earth's extensive hydrogen envelope (the ‘geocorona’) is considered in detail. The equations of radiation transfer are solved for several spherically symmetric models of the hydrogen distribution. The numerical results for the integrated Lyman α night glow are compared with the observations. Radiation transfer in models whose total hydrogen abundance is consistent with daytime absorption measurements is shown to be inadequate in accounting for the observed night glow features.
Deep Learning-Based Severity Detection in Alzheimer's Disease: A Comprehensive Study on Cognitive Impairment

Alzheimer's disease is a neurodegenerative disorder affecting millions worldwide, causing cognitive decline. Early detection is crucial for effective treatment, but manual diagnosis is error-prone and time-consuming. This study proposes a deep learning-based model for diagnosing and classifying Alzheimer's disease using VGG-16 CNN architectures. The model uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes MRI scans from individuals with Alzheimer's and healthy controls. The model uses a pre-trained VGG-16 convolutional neural network as the base, fine-tuning on the dataset, and data augmentation techniques. The trained model achieves a 96.75% accuracy on the testing dataset, distinguishing MRI scans between Alzheimer's patients and healthy controls. The VGG-16 architecture demonstrated superior performance during training and testing.
Cloud-Powered Personalization: An Advanced Recommendation System for E-Commerce Sites

This research study aims to enhance personalized recommendation systems by integrating cloud computing technology. The goal is to improve the precision and efficacy of recommendation algorithms by utilizing cloud infrastructure's scalability, flexibility, and computational capacity. The study covers the state of recommendation systems today, highlighting the challenges traditional models face in managing large datasets and adapting to changing user preferences. It also explores the design and workings of cloud-powered recommendation systems, emphasizing the benefits of shifting processing and storage to cloud-based infrastructure. The study also explores the use of machine learning algorithms in the cloud environment, highlighting how the cloud infrastructure supports real-time processing and ongoing learning, allowing recommendation systems to quickly adjust to changing user behavior. The study also investigates the consequences of integrating social interactions, user-generated content, and contextual data into the recommendation system. It also discusses security and privacy issues, suggesting anonymization methods, access restrictions, and encryption techniques to protect user privacy without compromising system functionality. The study presents experimental results comparing the performance of the proposed cloud-powered recommendation system against conventional on-premises models, assessing measures like response time, scalability, and accuracy.
Exposure on Edge Detection and Image Segmentation with Instinctive Classification of Brain Tumor Using Artificial Intelligence Methods

This research article proposes an exposure on edge detection on images followed by image segmentation and proceeds with the automatic classification of brain tumors by applying artificial intelligence functionality. The renowned medical image processing application consents to deliver the anticipated outcome in medical imaging technology. In medicine, image processing is computerized and divided into minuscule units called pixels. Following this, specific development has been decided to produce the desired outcome mage processing involves practical experiments with medical images with self-motivated study and analysis. MRI (Magnetic Resonance Image) noise removal is seen in medical images that are noteworthy and important for various image processing operations. The strategy suggested in this research study combines sophisticated bilateral filtering with the artificial intelligence method of neural network applications for pre-and post-processing procedures. The GLSZM method uses artificial intelligence techniques to classify linked images of the human brain, segment the image, and remove noise. The results will be shown as a segmented image with the classification of brain images using the automatic neural network method.
Descriptive Exam Evaluation through Semantic Mining and Analysis

Online examination evaluation methods are used as an alternative to the traditional examination’s way of evaluation methods. They offer a number of advantages while addressing issues associated with outdated conventional approaches. Online evaluation methods are mainly used for objective and short answers based exams but may not suitable for assessing the depth knowledge in candidates. So, candidate’s descriptive examinations are conducted to assess their depth of knowledge. Descriptive evaluation methods have been put into practice for quite some time but they greatly rely on keyword-matching techniques between the questions and answers. Due to this limitation, the semantic meanings of these answers are ignored. In current research, the deficiencies in prevalent systems have been addressed by calculating the semantic and syntactic similarity between answers. Moreover, the repetitive words and similarity between counteractive answers has also been considered. We have used SQuAD1 dataset in current research. Our model has achieved a value of 0.88 in terms of accuracy for while computing similarity among listed items.
HazOp Scenarios Modelling for Digital Factories

The Hazard Operability (HazOp) method aims to identify the potential hazards present in a plant in order to ensure its safety and prevent damage. The process involves identifying the causes of these hazards and their potential consequences and designing effective measures to minimise the probability of their occurrence. Inductive Logic Programming (ILP) is a technique used for supervised learning, where the aim is to construct a theory that explains a given set of observations. In [1], we proposed a learning technique based on ILP. It enables general diagnostic rules to be established to guide the operator in managing future malfunctions. In this paper, we carry on our work on Onto-Relational Learning by developing the SteamBoiler domain ontology using the RAMI top-level ontology and the HAZOP method. RAMI serves as a foundation for Industrie 4.0, offering a standardised interpretation of terminology. Further development and adaptation of each domain are executed at lower levels. The ontology developed is considered prior conceptual knowledge in ILP to induce supervision rules.
Exploring the Accuracy of Artificial Intelligence in Detecting Lies Through Micro-expression Analysis

Micro-expressions are the subtle and rapid movements of human facial expressions that could reveal a person’s true emotions, including emotions that people attempt to suppress, hide, or restrain. Many recent papers have researched facial expression recognition systems in video sequences using GRU models. However, they haven’t found a good relevance of micro-expression (ME) in detecting deceptive behaviors. In order to improve and contribute to the development of the system, we propose a micro-expression lie detection system with GRU’s hyperparameter optimization and explore its accuracy. FER-2013 is used for expression recognition learning and the dataset containing video clips of courtroom trials is used for deception detection learning. Several normalization techniques are done in the process. The CNN model with eight convolutional layers and three fully linked layers is used to train the facial expression recognition system. To improve its accuracy, multiple GRU parameter settings are employed. We used the model on the test dataset after training it. The outcome shows that it was 92.31% accurate. The confusion matrix predicts 12 out of 13 outcomes, with 100% accuracy on the deceptive class and 85% accuracy on the truthful class.
Backflow Power Optimization Method of Multi-DAB Converter Based on Master-slave Control

Under the conventional phase shift control, the dual active bridge (DAB) converter will create a significant amount of backflow power. Due to the various specifications of each module, there will be an unequal distribution of power in the input series output parallel system. This study suggests a power balancing technique based on master-slave independent control to address the aforementioned issues. This strategy assures the power balance of each DAB module and optimizes the backflow power of each module under various operating situations. Finally, an analysis and verification are performed on the input series output parallel (ISOP) system made up of three DAB converters.
Performance Investigation of Five-Phase Open-End Winding Induction Motor Drive

This paper presents a comparative analysis of the performance of a five-phase Open-End Winding Induction Motor (OEWIM) drive, a five-phase Induction Motor (IM) drive, and a three-phase IM drive. The comparison focuses on the transient response of speed-torque characteristics, phase voltage, and its total harmonic distortion (THD). A simulation study is carried out to evaluate the performance of the induction motors for the entire range of modulation from 0.2 to 0.8, and the results are presented. The three-phase IM and five-phase IM are supplied by a 3-phase 2-level inverter and a five-phase 2-level inverter, respectively, to generate the 2-level phase voltage. On the other hand, both ends of the five-phase OEWIM are powered by a 5-phase 2-level inverter to generate a 3-level phase voltage. Space Vector PWM technique is employed to generate the switching pulses for both inverters. The performance evaluation is carried out in MATLAB/Simulink environment.
YOLOv7 and EfficientNet Based Program For Detecting and Classifying Fish in Aquaculture Environment

The potential for aquaculture to supply the world’s demand for seafood is enormous. Effective fish monitoring and management in aquaculture, on the other hand, continues to be a serious challenge. This report proposed a study to develop a fish detection and classification system. We used YOLOv7 to detect fish and EfficientNet to classify the fish species. We trained and tested the models on augmented images of 9 different species of fish with 1000 images in each class. The proposed approach has a mAP score of 0.994 while the accuracy of the model was 0.9919 on the train set and 0.8139 on the test set. Our result shows that the combination of YOLOv7 and EfficientNet is applicable for efficient fish identification and categorization in the aquaculture environment.
Tunable Sparing of Disks in a Cloud Data Center

Disk Drive failure tolerance and optimum utilization of disks for storage are two of the most important challenges in the Cloud based data center. Addressing both these problems simultaneously can be tricky. To make the system failure tolerant, more storage needs to be added; this results in redundancy of storage. Providing a safety net for the disk is important to make sure that the disk can still operate. Therefore, we propose a novel drive sparing mechanism which not only provides failure tolerance by maintaining the optimal number of spare drives in the storage system, but also deals with over-provisioned spare. The method first identifies the critical disks that may fail in the RAID group based on the Venn-ABERS framework which helps us to know about the conditional probability of the disk failure, and then implements a variable sparing mechanism where a few disks act as dual-purpose disks. We demonstrate variable sparing using two cases, first when minimum spare pool is sufficient, and second, when minimum spare pool is not sufficient for failure tolerance. By proactively addressing potential failure points, the drives are able to extend their lifespan and enhance reliability when the variable sparing is utilized. In this paper, we explain the mechanism of variable sparing of disks and share results of an evaluation experiment using an open-source dataset by utilizing our prototype system.
Learning to Automatically Solve Algebraic Word Problems

We introduce an approach that automatically learns to solve algebra word problems. Our algorithm, a pioneering result for this task, reasons across sentence boundaries. It constructs and solves a system of linear equations while simultaneously recovering an alignment of the variables and numbers in these equations to the problem text. The learning algorithm incorporates varied supervision, utilizing either full equations or just the final answers.To solve these problems, our approach necessitates reasoning across sentence boundaries. It aims to identify a system of equations that concisely models the semantic relationships described in the problem text. This work represents, to the best of our knowledge, the first successful learning result for this challenging task.
Ciphertext Policy-Attribute Based Encryption for Fine-grained Access Control in Blockchain

Blockchain technology has become increasingly popular in recent years to implement a wide range of services, including trade finance, supply chain management, cloud computing, cryptocurrency, and IoT (Internet of Things). Blockchain technology is classified into two types: permissioned blockchain and permissionless blockchain. In the permissionless blockchain, there is no central authority, whereas in the permissioned blockchain, a single organization has authority over the network. However, permissioned blockchain platforms such as hyperledger fabric (HLF) face challenges such as private data leakage, compromised endorsers, and fine-grained data access control. This paper proposes ciphertext-policy attribute-based encryption (CP-ABE) based access control for blockchain. The proposed scheme uses a more efficient and expressive cryptographic approach that does not rely on pairings, but instead uses elliptic curve cryptography (ECC) and a linear secret sharing scheme (LSSS) with a more flexible access structure. The selection of endorsers for the hyperledger fabric has been made by the CP ABE policies defined, thus keeping endorser’s identities a secret. The proposed scheme ensures data confidentiality and lowers overhead compared to existing schemes.
The Utilisation of Machine Learning Algorithm Support Vector Machine (SVM) for Improving the Adaptive Assessment

In today's digital age, the convergence of science education and machine learning technologies has the potential to revolutionize the teaching and learning processes. This research, conducted by a dedicated science teacher, explores the dynamic intersection of machine learning and science education, with a specific focus on data-driven approaches to enrich the educational experience for students. The study centers on a group of 36 high school students from the Nazarbayev Intellectual School in Shymkent, Kazakhstan, aiming to assess the effectiveness of a novel approach to science instruction. It leverages the principles of multiple intelligence theory to tailor science curriculum and teaching methods to the unique cognitive profiles of individual students. The research outcomes demonstrate significant enhancements in students' computational abilities as a direct consequence of integrating machine learning into science education. By combining the power of data analysis and educational adaptation, this research contributes to the ever-evolving landscape of education and technology. This study is poised to serve as a cornerstone for educators, researchers, and policymakers interested in the evolving nexus of technology and education. It not only underscores the remarkable potential of machine learning in the realm of science education but also suggests a broader transformation of pedagogical methodologies, empowering students to excel in science while nurturing their diverse cognitive strengths.