Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Moving from Traditional to Smart University

The revolution of technologies and communications pushed for smarter applications and services. A smart university has become a target for educational institutions in many countries in the last few years. The smart-university provides many advantages in all aspects: academic, administrative, and research. Transformation to a smart university will save time, effort, and cost, in addition to increasing the quality of services, users' satisfaction, and sustainability. This research provided reviewing study for the most important standards, services, applications, and tools that must be present in smart universities. Then, the research proposed a framework to transform universities into smart ones. In recent years, the Kingdom of Saudi Arabia (KSA) is considered one of the leading countries in the field of digital transformation. So, in the next phase, we will try to apply the proposed framework in one of the KSA universities.
Intrusion Detection using Machine Learning Techniques: An exhaustive review

With the enormous growth of computers and data networks, as well as the vast number of relevant applications, cyber security has recently received a great deal of attention. The Internet has become more vulnerable to planned and extended cybercrimes. Consequently, the development of a powerful intrusion detection system to eliminate various cyberattacks in a network has become critical. Even though traditional security measures exist to detect and prevent cyberattacks, these measures are ineffective because cybercriminals are intelligent enough to circumvent all of them. Many cybersecurity applications employ machine learning (ML) techniques. This paper reviews the literature on intrusion detection using machine-learning techniques. This paper gives a brief overview of various machine learning methods and security datasets that are commonly used. Additionally, various metrics for evaluating the classification model's performance are discussed. This review paper looks at different machine learning methods for intrusion detection and the challenges that we can face during the application of machine learning.
Power Quality Enhancement In Multi-Microgrids Using Hybrid Energy Storage System

Energy storage is used to share reactive power in imbalanced multiple microgrids (MMG) and develop a new multi-function control system that enhances voltage and power quality. A power balancing regulator and reactive power compensation are both included in the multipurpose control system. As an alternative, either injecting or absorbing power through the energy storage system (ESS) minimizes the reactive power sharing defect among MMG. The proposed system is used to compensate for voltage regulation and balance three phase power of point common coupling connector. It is advised to combine the backtracking algorithm with reactive power compensation to increase system security. In addition to the algorithm's inherent anti-windup, a regulator for power balance is also made. The MATLAB real-time simulator was used to test the microgrid system using a single-phase photovoltaic system and a load connected to an unbalanced three-phase photovoltaic system.
Ensemble Filter-based Feature Selection Model for Cyber Attack Detection in Industrial Internet of Things

Industrial Internet of Things (IIoT) has been recently observed to be a more vulnerable target for different kinds of cyber-attacks. Most of the attack detection systems that are designed with help of Machine Learning (ML) models suffer from poor accuracy, high false detection rate, and a curse of dimensionality problems. To overcome these issues, an optimal selection of features is required to maximize both detection accuracy and computational efficiency. This paper has proposed an ensemble filter-based feature selection approach in which four well-known filter-based feature selection techniques such as ANOVA, Pearson Correlation Coefficient (PCC), Mutual Information (MI), and Chi-Square (CS) are used to reduce the irrelevant features. Then, in order to achieve two reduced feature sets, features are combined using a suitable design technique (union and intersection operation). To detect cyber-attacks, this reduced feature set is passed to four ML algorithms Decision Tree (DT), Random Forest (RF), XGBoost (XGB), and CaBoost(CTB) classifiers. Using the Edge-IIoT dataset, the effectiveness of the proposed model has been evaluated. Experimental results indicate that the proposed methodology achieved 97.84% and 99.61% accuracy in both the intersection and union feature sets, respectively.
Analysing Satellite Images using Segmentation with U-net and Focal Tversky Loss

Satellite images have proven to be a reliable source of data to monitor the earth for the collective benefit of mankind. These images include various features of earth like-roads, buildings, vegetation, and water bodies. Spatial and temporal analysis of these features can be used to predict the trends in urbanization. spatial urban analysis using satellite images obtained from specific satellites centers were used by unitizing U-net model to segment features in images using semantic segmentation. The images were annotated with pixel-wise semantic labels. Segmentation involves labelling every pixel into one of the features, which can then be used to calculate the actual area on the earth’s surface. A comparative study of different approaches using the U-net model for satellite image segmentation can be seen. When the resolution of the satellite images are known for every image, this approach can easily be used to calculate the ground area of the different features and thus perform spatial and temporal analysis on the features.
Non-Invasive Blood Glucose Measurement Using Ire Sensor

The world’s population of diabetic patients has been growing recently. The technique most usually employed to quantify blood glucose levels is invasive causing pain, uncomfortable, expensive, and raising the risk of the transmission of infectious infections. In this article, we suggested a non-invasive method for detecting blood sugar levels to have a safe and painless measurement. It is based on near-infrared spectroscopy at a wavelength of 940 nm, which was chosen because it is appropriate for measuring blood glucose. It consists of a transmitter and a receiver. The NIR light is sent to the finger and interacts with the blood glucose. The transmitted signal is detected by the photodetector. The photo detector’s output current is transformed into a voltage signal before being filtered and amplified. By examining the variation in received signal strength that is obtained after reflection, the concentration of glucose in the blood can be identified. By using a 2nd order polynomial regression equation based on the recorded voltage the glucose concentration is determined. The outcomes of the suggested system demonstrate the viability of a non-invasive, NIR-based approach for measuring blood glucose. The glucose value is also displayed via a mobile app. By which the user can use to determine their blood glucose level and continue their routine at the right time.
IoT Implementation in India: A Complex Decision Process using GRA/AHP

Internet-of-Things (IoT) has long been considered the future. The range of challenges and issues for implementing IoT in India encompasses catering to a vast number of heterogenous devices, architectures, various information exchange protocols and most importantly, awareness among the masses. The critical problem India faces is identifying the challenges and recognising the top-ranked issues to deal with rigorously in IoT adoption. This study focuses on a thorough identification of application areas, open issues and implementation as well as adoption challenges of IoT from an Indian perspective. This complex decision problem could be solved by applying Grey Relational Analysis (GRA) and Analytical Hierarchy Process (AHP). The study focuses on providing a GRA/AHP-based analysis and ranking of the challenges that India must address in the widespread implementation of IoT. A fuzzy-based decision process combined with GRA and AHP methods is also discussed to make robust and more reliable decisions.
Human in the Loop: What the Avatar XPrize Revealed About the Future of Telepresence Robots

It's still an achievement for autonomous robots to merely survive in the real world, which is a long way from any kind of useful generalized autonomy. Under some fairly specific constraints, autonomous robots are starting to find a few valuable niches in semistructured environments, like offices and hospitals and warehouses. But when it comes to the unstructured nature of disaster areas or human interaction, or really any situation that requires innovation and creativity, autonomous robots are often at a loss.
Computing Stock Market Price Behavior Using Machine Learning Approach

Stock markets are the most beneficial invention for creating wealth and are a key feature in any investment plan in developing countries to emphasize on economic resources including financial services. Investment in the stock market is getting popular among common people day by day. Every day almost 5000 Bombay Stock Exchange (BSE) listed trade companies offer appx 240 million stocks which make a total of 2000Cr+ Indian rupees in stock market investment. Small Businesses, Banks, and brokerage firms, all depend on the stock markets to make profits. Thus, analyzing the behaviour of the stock market will help improve the Indian economy and help people predict the upcoming financial risks. As the stock market is frequently fluctuating and contains randomness, it is hard to predict the behaviour using traditional forecasting methods. In this study, we have implemented various machine learning models (Linear Regression, Polynomial Regression &SVR) and have evaluated them based on different criteria to compare the efficiency of these models in predicting the stock market behaviour accurately. Appx. 25 years of BSE historical data is used for training and testing these models and two different ways of these models, firstly by predicting the stock market values and secondly,by predicting its behaviour.
A Systematic Study of Approaches used to Address the Long Tail Problem

E-commerce relies heavily on the Recommendation System. The primary purpose of the Recommendation System is to assist users in selecting appropriate items. Many Recommendation Systems only recommend popular items to users and ignore unpopular ones. These unpopular items have very few ratings. These unpopular items create a long tail in the Recommendation System. A recommendation system refers to this type of problem as the Long Tail Problem. These unpopular or tail items, if comes into the mainstream for recommendations to the users can increase the sales of items along with popular or head items, by providing users an opportunity for one-stop shopping for both their mainstream and niche tastes. This paper provides an overview of various techniques used to address the Long Tail problem of the Recommendation System. A comparative analysis of all the methods used to address the Long Tail Problem, their evaluation criteria, and their results is also included in the paper. Researchers and academicians will benefit from a comprehensive study of the Long Tail Problem in Recommendation Systems by better understanding existing work and developing future directions.
Evaluation of Cell Inconsistency in Lithium-Ion Battery Pack Using the Autoencoder Network Model

Cell inconsistency is a common problem in the charging and discharging of lithium-ion battery (LIB) packs that degrades the battery life. In situ, real-time data can be obtained from the battery energy storage system (BESS) of an electric boat through telemetry. This article examined the use of a 57-kWh BESS comprising six battery packs connected in series, each of which contained 16 LIB cells with a nominal capacity of 180 Ah. Because of cell inconsistency, the 96 cells had different voltages during the charging process. We compared the performance of four types of autoencoders (AEs): a fully connected (FC) model, convolutional neural network (CNN) model, long short-term memory (LSTM) model, and hybrid CNN–LSTM model. These AEs were employed to evaluate the cell inconsistency by minimizing the reconstruction error. The FC model exhibited the optimal performance among the four AEs in an abnormal battery condition. The LSTM model had the highest capability of distinguishing normal and abnormal cells. Nevertheless, the CNN–LSTM model, which combines the advantages of the CNN and LSTM models, is the most effective AE for the complex working conditions of electric vehicles.
A Review on Predicting Brain Stroke using Machine Learning

At present, healthcare is one of the biggest concerns in the world. Brain Stroke is the leading cause of death worldwide. Prediction and detection of the occurrences of a brain stroke at the early stages is a valuable work in the medical field. There are several factors which are responsible for the brain stroke such as BMI (Body Mass Index); Age; Sex; Family Background; Gender; smoking status; hypertension, etc. In healthcare, a lot of research work has been done on the prediction of heart disease, but relatively little attention is paid on predicting a brain stroke. The main aim of this study is to review different research articles published earlier and to choose the best machine learning techniques for the prediction of brain stroke for our future work. After reviewing the different machine learning methods utilized for stroke predictions and after taking into account the previously published studies, it has been discovered that death rate and functional results are the expected outcomes for the majority of the research work done. Support Vector Machine, Stacking, Decision Tree, Weighted Voting, Random Forest, Neural Networks and Naive Bayes were the most frequently employed methods.
Smart City Waste Management System using IOT

Dustbins are used for disposing the waste. When it gets overflowed, people used to throw the waste outside the dustbin until the garbage collector clears the dustbin. It makes the place surrounded by mosquitoes, bugs etc., and mainly affecting the environment and creates several unhygienic problems. To overcome this problem, we have proposed a system called “Smart City Waste Management System using IOT’’ to manage the waste. The sensors are placed in the top of normal garbage bins to measure the amount of trash and find the formation of any toxic gases. These measures make the sensors send the notifications in form of messages to waste management board. So that they can identify the location and garbage collector reach the place as soon as possible and clean the place. The proposed system is developed using Arduino, ultrasonic sensor, temperature sensor, gas sensor and RFID tag. It offers several benefits to the users such as Protect from bacteria and germs- motion sensor trash can protect you from harmful bacteria and germs and gives hygiene surroundings. Because of sensor technology, there is no need to touch the trash can for opening or closing.
Pattern Of Productivity In Gamification Elements Research: Bibliometric Distributions

Recently, there has been a lot of interest in the practice of integrating game elements into contexts that are not gaming-related. E-learning has incorporated gamified elements including prizes, medals, tests, grades, objectives, and contests. For this paper's bibliometric analysis, we found 838 documents from the years 2012 to 2022. The R programme "Bibliometric" was used to conduct the majority of the analysis for this material. The inquiry took into account the development of the journal itself, the most important documents, authors, and country pieces. Vos Viewer is used to examine the most popular keywords in citations. The analysis demonstrates that DOMNGUEZ A. 2013, COMPUT EDUC was the most pertinent article with 902 citations, Dichev C. was the most pertinent author, and this article also demonstrates that online learning, systematic literature review, education innovation, computer science, and game-like experiences were the most popular topics in this field. This research aims to provide researchers and academics with a comprehensive understanding of the subject at hand and demonstrate how concentrating on themes has led to productive lines of further research and investigation.
IoT Attack Detection Method based on Synthetic Minority Over-Sampling with Random Forest Technique

The primary objective of this study is to create a self-protection system for the Internet of Things (IoT) that relies on machine learning. One such resampling approach is the proposed IoT Attack Detection Method based on Synthetic Minority Over-sampling with Random Forest (IADMSMO-RF). To identify risks in the IoT ecosystem, we employ IADMSMO-RF, which improves network search computation. Assessment is performed on the Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD) dataset to see how effective the trial is in recognizing attacks on the IoT. For the success of the IoT, it is crucial to have a reliable system for detecting and protecting against cyberattacks. Compared to Synthetic Minority Oversampling Technique - Synthetic Minority Oversampling Technique (PPDT -SMOTE), the suggested technique outperforms it in attack detection and categorization. The proposed system has an accuracy of 97.46%, a false alarm rate of 0.2%, and a curve area of 98.76%.
Fuzzy Time Logic-Based Traffic Control System Using Hyper Dynamic Scheduling Graph Theory Approach for Traffic Environment

By day-to-day development in transports as in road ways, the traffic issues is more mitigation arriving the jamming. Specifically the Multi-lan signals posed complex nature for signal arriving. The traffic controller using fuzzy assumption rules for make effort in signaling system. Traffic signals not as intelligence based on the logical representation to sense to identify the signals Due to the Nerve traffic control system design time complexity is not mush enough to make much efficient. To propose a Fuzzy logic tragic control system based on Fuzzy Hyper Dynamic scheduling graph theory approach (FHDSG) with addition time state logic control. The proposed system is efficient, handling congestion and traffic flow in the intersection by estimation the arrival time. Each traffic slot is assigned by periodic management of the green light space. By controlling the severe traffic, Expected time computation is used to calculate the duration of the green light of the traffic signal. This paper has proposed the use of intelligent traffic light controllers, fuzzy logic depends the scheduling to assign the slot of waiting to release the traffic. It is implemented in order to provide intelligence of Max state scheduling to the fuzzy logic control system. The proposed sytem reduce the time complex nature as well provident much efficient traffic control system
Microvascular Free Flap Reconstruction of Thigh Defects After Tumor Resection in the Setting of Radiation

Background Soft tissue reconstruction of the thigh defects can usually be achieved with local options. Free tissue transfer may be indicated in very large defects with exposed vital structures and/or a history of radiation therapy where the healing potential of local option is poor. In this study, we evaluated our experience on microsurgical reconstruction of oncological and irradiated thigh defects to assess the risk factors for complications. Methods Institutional review board–approved retrospective case series study using electronic medical records from 1997 to 2020 was conducted. All patients with irradiated thigh defects derived from oncological resections who underwent microsurgical reconstruction were included. Patient demographics and clinical and surgical characteristics were recorded. Results Twenty free flaps were transferred in 20 patients. Mean age was 60 ± 11.8 years, and median follow-up time was 24.3 months (interquartile range [IQR], 71.4–9.2 months). The most common type of cancer was liposarcoma (n = 5). Neoadjuvant radiation therapy was performed in 60%. Most commonly used free flaps were latissimus dorsi muscle/musculocutaneous flap (n = 7) and anterolateral thigh flap (n = 7) Nine flaps were transferred immediately after resection. Overall, 70% of arterial anastomoses were end-to-end, whereas 30% were end-to-side. Deep femoral artery branches were chosen as the recipient artery in the 45%. Median length of hospital stay was 11 days (IQR, 16.0–8.3 days), and median time to start weight-bearing was 20 days (IQR, 49.0–9.5 days). All were successful except for 1 patient who required additional pedicled flap coverage. The overall major-complication rate was 25% (n = 5, hematoma = 2, venous congestion requiring emergent exploration surgery = 1, wound dehiscence = 1, surgical site infection = 1). Cancer recurred in 3 patients. One required amputation due to cancer recurrence. Age (hazard ratio [HR], 1.14; P = 0.0163), tumor volume (HR, 18.8; P = 0.0006), and resection volume (HR, 2.24; P = 0.0019) were statistically significantly associated with having a major complication. Conclusions Based on the data, microvascular reconstruction of irradiated post–oncological resection defects shows high flap survival rate and success. Given the large size of flap required, the complex nature and size of these wounds, and history of radiation, wound healing complications are common. Despite this, free flap reconstruction should be considered in irradiated thighs with large defects. Studies with larger cohort and longer follow-up are still required.
Morphology of Metapodiophalangeal Joints and Mobility of Finger and Toe in Bovids

Abstract. Bovid morphologies differ depending on body size and habitat. We hypothesized that bovids also vary their mobility of fingers and toes depending on body size and habitat. In this study, we compared the shape of the distal ends of the metapodial bones and the proximal facet of the proximal phalanges to examine the metapodiophalangeal joint gaps and simulated the three-dimensional motion of the bovid metapodiophalangeal joints during the opening and closing states through CT scans to investigate whether the mobility of the finger and toe is affected by body size and habitat. The results showed that species with gaps in the metapodiophalangeal joints had higher mobility and that species living in mountains had larger gaps. This suggests that bovids living in unstable terrains can move better on unstable substrates due to their increased mobility of digit. Similarly, our results indicate that smaller bovids are more likely to display variations in the mobility of digits than larger-sized bovids since greater body size reduces the range of mobility to avoid dislocation risk. Our results show that bovids have been able to be flexible to a range of body sizes and habitats by varying the size of their metapodiophalangeal joint gaps.
Creating Implicit Measure Stimulus Sets Using a Multi-Step Piloting Method

The effect of arbitrary stimulus selection is a persistent concern when employing implicit measures. The current study tests a data-driven multi-step procedure to create stimulus items using a combination of free-recall and survey data. Six sets of stimulus items were created, representing healthy food and high sugar items in children, adolescents, and adults. Selected items were highly representative of the target concepts, in frequent use, and of near equal length. Tests of the piloted items in two samples showed slightly higher implicit measure–behavior relations compared to a previously used measure, providing preliminary support for the value in empirically based stimulus selection. Further, the items reported as being the most associated with their target concepts differed notably from what one may expect from the guidelines or population consumption patterns, highlighting the importance of informed stimulus selection.