Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on IoT: Applications and Security Risks

The Internet of Things (IoT) is one of the networking concepts that is expanding the fastest and has a variety of applications. IoT has enabled various industries, such as smart electricity systems, intelligent traffic, and smart homes and buildings, to become more sophisticated in the real world. Several previously manual and resource-intensive operations can now be automated thanks to the IoT. However, techniques for detecting and preventing security breaches need to be developed and put into practice for the successful adoption of IoT in several businesses. Major IoT application domains are identified, vulnerabilities are analyzed, and techniques for reducing security risks are suggested in this research. This study uses a mixed-method approach, beginning with a thorough assessment of the literature to identify the most well-known applications for IoT across sectors like industrial, smart cities, healthcare, and smart homes. Current weaknesses related to IoT networks and devices are also examined. Researchers and manufacturers will be able to assess and restrict the vulnerability range on IoT devices with the aid of this}study.
Quad-Radiator MIMO with asymmetric feed line for (n77/n78/n79/WLAN) 5G-Wireless Applications

The present article confers compact quad port antenna using effective technique for sub-6 GHz 5G band that involves the broad bandwidth. The goal of this work is not only to attain wider bandwidth but also it offers a high data rate with nearly omnidirectional type radiation pattern and the unique design approach. In order to improve necessary mutual coupling, the quad patch elements are arranged in face-to-face method with adequate spacing between antenna elements of MIMO (Multiple Input Multiple Output) system. The enhanced structure consists of inverted T-shape partial ground and square type patch with stub elements on both sides to enhance impedance matching. Also, the role of twin stub and rectangular notch on top right corner of patch is discussed. The asymmetrical feed line structure is incorporated for the operation of Quad port antenna with a structure of dimension (W × L) 31 × 40 mm2. With this compact size, the impedance bandwidth (-10 dB) of 2.27 to 5.34 GHz is attained. The analysis of antenna performance parameters like correlation coefficient, diversity gain and isolation which is required consistently in today’s wireless era have been considered. Additionally, the paper presents a highly effective n77/n78/n79 band antenna design method that allows wideband coverage, appropriate correlation and high isolation in wireless 5G transmissions.
Voice Based Authentication – An Alternative to OTPs

The traditional method of authentication using a one-time password does not provide enough security for web pages or applications that are a gateway to sensitive information/data. Sensitive data breaches have become a common thing in today’s world and have caused great losses to individuals, organizations and nations as well. With advancement in technology, access to OTP has become a piece of cake and sensitive data has become vulnerable. Given the gravity of the situation, a more secure way of authentication has become the need of the hour, therefore voice-based authentication is a better successor to the traditional method and is more secure. In this paper we have collected the voice sample and processed it using the MFCC method to extract the voice print and then used Gaussian Mixture Model to find similarity between the stored voice and the live voice.
Understanding and Managing Employee Attrition: Strategies for Retaining Talent

In this study, we address the issue of employee attrition by employing a synergistic approach that amalgamates Power BI analytics and advanced machine learning techniques. Our methodology includes a meticulous analysis of historical employee data using Power BI, uncovering subtle attrition trends, influential variables, and pertinent demographic insights. Subsequently, we engineer a predictive machine learning model that forecasts forthcoming attrition patterns and proficiently classifies employees at high risk of departure. The model’s precision is meticulously refined through rigorous feature engineering and judicious algorithm selection like Decision Trees, Gaussian NB, Random Forest, XGBoost. To actualize these insights, we develop a user-friendly Flask web application that seamlessly integrates the analytical and predictive processes. This intuitive application empowers users to input employee details and promptly receive accurate attrition predictions. Our innovative amalgamation of analytics, machine learning, and user-centric web deployment establishes a comprehensive and efficient solution. By arming organizations with actionable insights and preemptive strategies, we significantly contribute to ameliorating employee retention, fostering engagement, and augmenting overall organizational efficacy.
Cyber-Attack Detection in Smart Grids Using Machine Learning Approach

To meet the increasing demand of reliable energy, now-a-days power grids rely on synchronous phasor systems, Advanced Metering infrastructure (AMI) and industrial automation control systems. The critical cyber physical infrastructure and high-speed networks in smart grid has made it susceptible to cyber-attacks. As a centrally controlled infrastructure smart grid captures and processes data with the help of several devices such as smart meters, sensors and virtual machines. Such a huge amount of data if not handled properly can trigger massive blackouts and infrastructural damage to the smart grid. To assess the state of the power system using conventional methods, phasor quantity measurements are used. The observed and estimated findings are compared with a limit for cyber-attack detection. However, modern smart grids, which are mostly built on supervised learning algorithms, frequently use machine learning (ML) to address cyber security challenges. In this context, the main focus of this project is to differentiate between natural fault, cyber-attack and no fault or normal operating condition. The results of various ML algorithms are compared and best possible model is suggested as an enhancement to existing available methods.
S5ELBP: Supervised SAR Image Classification using SVM from SSELBP Texture Features

Synthetic Aperture Radar (SAR) and Optical sensors are very popular depending on their strengths and application needs. The ocean surface will be covered with clouds most of the time and optical sensors cannot capture the information as they cannot operate in dust and cloudy conditions. So, SAR sensors come into existence to capture the atmospheric phenomena in those regions. There is a wide range of texture extraction techniques such as traditional-based wavelet analysis, statistical analysis, local pixel-based approaches, and dimensional reduction approaches. However, these approaches have limitations in boundary extraction, high-dimensional feature extraction, and uniformity representation. In recent times, deep learning (DL) approaches to extract features which varies based on the applications, image interpretation, and so on. At the same time, DL approaches are limited in transparency, computation requirements, and hyperparameter tuning. In contrast, the proposed S5ELBP i.e., Supervised SAR image classification using the integration of Support Vector Machine (SVM) and Scale Selective Extended Local Binary Pattern (SSELBP) extraction techniques shall overcome various challenges of existing traditional and DL approaches such as adaptive thresholding, less computational resources, and noise robustness. Hence, the objective of the proposed methodology is to extract the image features from the Scale Selective Extended Local Binary Pattern (SSELBP). Also, the SSELBP features are integrated with SVM to show the strength of the proposed approach in SAR image classification. Additionally, the performance of the present work is enhanced by comparing the extracted results with various classifiers. Finally, the work is concluded by recommending the scope of future work.
Interpretation and Performance Machine Learning Model for Detection of Brain Stroke Based on Classifier

Several diseases, such as Brain Stroke, are one of the causes of death, one that can be utilized in disease detection, especially Brain Stroke, is Machine Learning (ML), several models that can be used in predicting the likelihood of a patient having a brain stroke are one of them; Logistic Regression, SVM, XGBoost, Naïve Bayes, Extra Tree, CatBoost Classification, LightGBM, Random Forest, Decision Tree, Gradient Boosting, k-NN. Some studies are still little using model interpretation and Model Performance using Mean Cross-Validation Scores. The Study aims to examine the accuracy-based classification performance of various ML models and interpret the model using SelectKBest to fill this little research gap. The results show that CatBoost has the best accuracy of 94.3% and SVM best model performance with Score (Mean-Cross Validation Score) of 0.951, and the "hypertension" and "heart_disease" features are the most influential features based on the K Score (SelectKBest), where the results are "72,928" and "74,706".
Corneal Endothelial Images Segmentation Using Deep Learning Model

The objective assessment of corneal endothelial pathology is frequently carried out using manual and semi-automated software tools. These software applications enable the quantification and analysis of corneal endothelial cells. However, this approach is subjective and not widely adopted by ophthalmologists. Developing methods for the automatic assessment of the size and structural organization of endothelial cells represents a significant challenge in the field of ophthalmology. Therefore, this article introduced a cell segmentation method using a convolutional neural network based on the U-Net architecture. More specifically, the network is trained to discriminate pixels at the borders between cells. The main contribution of this study lies in modifying the original U-Net by reducing the number of parameters in width and using average pooling, aiming to enhance the efficiency of corneal endothelial cell segmentation and analysis. The proposed method was evaluated on a dataset consisting of corneal endothelial cell images. The accuracy values are around 93.99% and the sensitivity has a high value (99.33%) compared to other architectures.
Sentiment Analysis of opinions about Electric Vehicles Using Twitter data

The proverbial road to hybrid vehicle inclusion has been consolidated over the last decade as a logical consequence of metrics such as a sustained trickle of new models, faster and more copious chargers, and technological breakthroughs. Every day, millions of people use networking sites to voice their opinions and insights about umpteen topics. Assessing marketplace perspectives using social media data can be an exquisite means for organizations today to elevate their marketing goals and reciprocate consumer feedback to gain a foothold in the market. For this paper, we focused on Twitter to get the public opinion on EVs. The data-set was created after extracting tweets from related hashtags with the Tweepy library and pre-processing them. Features vectors were extracted using TF-TDF vectorizer and machine learning algorithms such as Textblob, VADER, LSTM and BERT were used for the classification of sentiments. Finally, we analyze the results and graphically represent people’s opinions about Electric Vehicles in India.
A transformer-based approach to automate disease prediction from patient descriptions

Deep learning techniques have revolutionized healthcare by enabling automated disease diagnosis and treatment. However, medical diagnosis can still be time-consuming, invasive, and costly. In this paper, we propose a transformer-based approach for disease prediction using textual symptom descriptions, aiming to provide automated and accurate diagnoses. A pre-trained transformer model is applied on a dataset of symptom descriptions and disease labels for fine-tuning. The proposed approach leverages the model’s ability to extract contextual information and long-term dependencies from text, enhancing performance in disease prediction. The dataset, collected from online sources in collaboration with healthcare professionals, includes multiple languages (English, Hindi, and Hinglish). Experimental results demonstrate the effectiveness of the proposed approach, which has the potential to significantly reduce the time and cost of medical diagnosis. In English, DiagnoAI exhibited outstanding performance with a recall of 0.966, while for Hindi, it achieved an accuracy of 96.29 with a recall of 0.972, precision of 0.961, and F1-score of 0.968, demonstrating its robust diagnostic capabilities across multiple languages.
Predictive Analytics for Stock Market Trends using Machine Learning

Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately.
Data Driven Approach for Inverse Kinematics in 2D and 3D

Kinematics Analysis of Robotics is one of the most important tasks to compute. Inverse Kinematics, a field of Kinematic Analysis is considered to be difficult to obtain. The difficulty faced is due to the solution set generated by solving the Inverse Kinematics. Unlike Forward Kinematics, Inverse Kinematics provides a set of solutions and not a single solution. It provides a set of solutions as we can reach a place in space through many points. Despite having many methods to solve the problem, there is always been a doubt of which method outperforms all the other methods. It is seen that each system behaves differently while solving inverse kinematics. In this paper we provide insights on how systems with 7 Degrees of Freedom and 8 Degrees of Freedom behaves while solving the Inverse Kinematics in 2 Dimensional plane and 3 Dimensional space. The problem at hand is solved by using different methods that already exist and a comparison is done in the methods with the results obtained from the study.
Advancements in Crown Sheath Rot Detection using DenseNet121 and UNet in Rice Plant Leaf Analysis

This article aims to investigate the leaf spot disease in rice, a condition that has been observed to cause substantial reductions in crop yield, ranging from 10% to 50%. The disease is characterized by the appearance of circular lesions on the leaves of rice plants, which gradually expand and cover the entire leaf surface, leading to the eventual death of the affected leaves. In order to achieve automation in the detection and severity classification of the disease under investigation, a range of deep learning models were utilized. These models encompassed Faster DenseNet121 and UNet. These methods proposed in this study was trained and tested using a dataset consisting of 1120 images. The findings of the study indicate that the DenseNet121 approach that yielded the highest level of success demonstrated a noteworthy classification accuracy rate of 95.23%. The results obtained from the integration of image processing techniques with deep learning models demonstrated superior performance compared to the analysis solely based on deep learning models. The integration described in this study presents a novel approach to enhance the efficiency of diagnosing leaf spot disease on rice. By leveraging advanced technology, this integration aims to reduce the time required for diagnosis, minimize the occurrence of human errors, and facilitate the timely application of suitable pesticides to mitigate the detrimental effects of the disease.
Enhancing Hexapod Robot Locomotion Control Through PID Controller Optimization Using Genetic Algorithm

Hexapod robots are highly stable due to their six legs and are commonly used in challenging terrain. Precise control of the joint angles of the legs is crucial for smooth locomotion and stability. In this study, we propose a Proportional-Integral-Derivative (PID) controller to regulate the positions of the joint angles. The PID controller adjusts the control output based on the error between the desired setpoint and the actual system state. To optimize the controller's performance, we employed a Genetic Algorithm, which improves the parameters of the controller through natural selection. We simulated the proposed method using SIMMECHANICS in MATLAB and demonstrated highly accurate tracking of the desired trajectory of the leg angles and significant stability of the robot during movement. The results indicate the proposed approach's effectiveness in regulating the hexapod robot's leg angles. The utilization of an optimized PID controller through the Genetic Algorithm in this study enhances the advancement of hexapod robots. By employing this method, we achieve automatic optimization, which significantly reduces the time and effort required to determine the best PID parameters. Consequently, this approach enhances the stability, control, and overall performance of the robot across diverse locomotion tasks.
Performance Comparison of Attention Mechanism and Multihead Attention Seq2seq Models for Fog Application Placement Problem

In the era of expanding Internet of Things (IoT) applications, managing data transfer and network management in fog networks poses significant challenges. The Fog Application Placement Problem (FAPP) concerns the optimal positioning of applications within a fog network to optimize a given objective function without violating constraints of minimal fog nodes resources. To address FAPP, this study compares different sequence-to-sequence (seq2seq) deep learning models, specifically attention-based, multihead models, against the heuristic hop aware method. This method are chosen because of the similarity of how seq2seq predict with heuristic placement algorithm works but with even lower time complexity. The models are trained on the available fog network dataset from the Yet Another Fog Simulator (YAFS) and are evaluated based on Key Performance Indicators (KPIs) including average total response time, average byte, and average hop. Seq2seq are proven to minimize all KPIs resulting in 189.23 ms for average response time, 9.26 MB of communication usage, and 3.14 hops per application in problem instance, with 0 not available apps. This result not only prove the benefit of implementing seq2seq models to solve FAPP, but also to better understand an under-explored implementation of seq2seq.
Performance Analysis on Drone-Captured Aerial Images Using Computer Vision Techniques for Industrial Applications

This study describes the creation of a cutting-edge computer vision system that can recognize and differentiate between individuals (people) and trees in real-world data collected by drones. This system harvests priceless insights from aerial imagery using cutting-edge machine learning algorithms and image analysis techniques, opening up new prospects for applications in forestry management, disaster response, and ecological study. In our project, specialized machine learning models are developed that are intended for accurate object recognition and classification. These models are painstakingly designed to recognize and distinguish between human forms and different tree species, even in difficult outdoor settings with variable lighting and dynamic viewpoints frequently found in drone captured video. It enables automated tree detection and classification in forestry management, making resource planning and monitoring more efficient and effective. In instances involving disaster response, it helps in the quick identification of people, potentially saving lives and speeding up rescue efforts. This system can be a valuable resource for ecologists conducting research on tree populations and human-environment interactions. By enabling drone-based data analysis and advancing cutting-edge technologies, our research helps to improve resource allocation and decision-making across a variety of situations. Our study addresses significant real-world difficulties and provides a way towards more effective and efficient data-driven solutions by improving the identification of people and trees within various environments.
Comparative Analysis of Passive, Active, and Hybrid Active Filters for Power Quality Improvement in Grid-Connected Photovoltaic System

The use of photovoltaic (PV) systems to generate electricity is expanding, making it more crucial than ever to ensure the quality of the power sent to the grid. In this paper, a grid-connected 50 kW PV system with load is considered. The comparative analysis of passive power filters (PPF), active power filters (APF), and hybrid active power filters (HAPF) for power quality improvement in grid-connected PV systems is demonstrated in this work. These filtering techniques aim to reduce Total Harmonic Distortion (THD) and improve the quality of power injected into the grid. PPFs, the simplest form of harmonic mitigation, employ passive components such as inductors and capacitors to attenuate specific harmonic frequencies. However, the performance of PPFs is limited, especially at low frequencies, and they may not be effective in reducing higher-order harmonics. APFs, on the other hand, offer greater flexibility and dynamic response compared to PPFs. However, a higher rating of active filter is considered a drawback of this filter. A HAPF has been proposed to combine the benefits of both active and passive filters. The HAPF offers better performance and a more affordable way to reduce harmonics for power quality improvement. The study is conducted through simulations in MATLAB/Simulink using a grid-connected PV system model. The performance of these filters is evaluated in terms of harmonic distortion reduction. The outcomes of this study will aid in determining the most effective type of filter to use while enhancing power quality in grid-connected PV systems.