Miracle-dz/UniFAR-FTUs huggingface dataset:
Assistive System and GPS Tracker for the Visually Impaired

We present a novel project involving an Arduino Uno-powered blind assist device that integrates an ultrasonic sensor and a GPS module. The ultrasonic sensor offers comprehensive obstacle detection, ensuring a full range of environmental awareness. Complementing this, the GPS module provides real-time navigation, enhancing user mobility. The detected obstacles prompt the JQ6500 voice sound module to generate audio instructions through a speaker module, guiding users to move or halt appropriately. This innovative setup empowers the visually impaired to navigate their surroundings confidently by receiving timely and precise guidance via real-time path view instructions. This device promotes independent and safe travel through the help of ultrasonic sensing, GPS navigation, and audio cues, granting users a newfound sense of freedom.
GemID: A Hybrid CNN-Random Forest Approach for Accurate Gemstone Identification

In this paper, a reliable gemstone identification system is shown that combines Random Forest as well as CNNs (Convolutional Neural Networks). With a 70:30 training to testing ratio, a diversified dataset of 6265 photos was carefully picked to provide thorough model assessment. The many convolutional and pooling layers added to the CNN architecture effectively identified key information from gemstone photos. This hybrid strategy improved the classification process by integrating a Random Forest classifier. The model's outstanding accuracy of 74.76% demonstrates how adept it is at identifying gemstones. This work shows the possible uses and accountability in more general domains like mineralogy, capacity building, and geological analysis while also advancing automated gemstone categorization. This research area provides a promising precedent for future development in the study of geological specimens by demonstrating the effectiveness of integrating deep learning and machine learning approaches for exact gemstone detection.
Hierarchical Classification Using Ensemble of Feed-Forward Networks for Insider Threat Detection from Activity Logs

Insider threat is a significant cybersecurity concern that poses challenges in detection due to its infrequent occurrence and diverse data types. Recent Machine and Deep Learning-based approaches to insider threat detection mostly focus on using sophisticated feature extraction models. However, an ensemble of relatively simpler neural network models is also known to be an effective classifier and there has not been much work on insider threat detection with supervised ensemble models, which we aim to study in this work. The proposed approach follows a two-stage hierarchical process in which the first stage involves training multiple Deep Feed Forward Neural Network models on different subsets of data to identify whether a time-series of user activity is either malicious or non-malicious. The second stage involves identifying the threat scenario if the time series activity is classified as malicious in the first stage. The individual learners in the ensemble are trained with a balanced dataset formed by random undersampling of the majority class instances without replacement to prevent biasedness towards any particular class during the prediction. Further, no two learners are trained using the same dataset which helps in learning accurate decision boundaries between the target classes. Experiments on the CMU CERT insider threat data (version 4.2) verify the effectiveness of our approach in identifying the different insider threat categories.
Sound Event Detection Using Multi-Scale Dense Convolutional Recurrent Neural Network with Lightweight Attention

Neural network based sound event detection has attracted considerable attention due to high detection accuracy. Existing sound event detection algorithms often improve performance by constructing deeper and more complex detection models. To address the issue of high computational complexity in existing high performance sound event detection algorithms, this paper proposes a sound event detection algorithm using multiscale dense convolutional recurrent neural network with lightweight attention (MS-AttDenseNet-RNN). It utilizes multiscale dense convolutional neural network with lightweight attention (MS-AttDenseNet) to capture information from different scales of sound signal receptive fields and then generates importance-enhanced multi-scale feature. Finally, recurrent neural network (RNN) is used to model the temporal dependency of the enhanced multi-scale features. Compared to the system model that won the DCASE challenge, this method achieves a 18.9% improvement in F1 score and a 4.0% reduction in error rate, while reducing the number of parameters by 75.8% and the computational complexity by 47.5%. This demonstrates that the proposed method can achieve lower parameter count and computational complexity while maintaining accuracy.
Network Intrusion Detection in Software-Defined Network Using Deep and Machine Learning

Software Defined Network (SDN) has been known for the great potential to become the development direction of a new generation network architecture. For the large-scale deployment of SDN in the future, security issues are a big concern. Accordingly, this paper applies several Machine Learning (ML)and deep learning (DL) models for Network Intrusion Detection System (NIDS), respectively, aiming at improving the accuracy performance on NIDS. The benchmark dataset, NSL-KDD, is used for evaluating the performance of the algorithms. Extensive experiments show that the F-measure rate can reach up to 87.72 % for multiclass label on NSL- KDD data set with twenty-two features using KNN algorithm. Furthermore, compared to other ML models, the k-Nearest Neighbour model has better performance under multiclass classification through numerous experiments.
QoS-Aware Reinforcement Learning Based Green Trajectory Design for UAV-Aided Backscattering

Backscatter communication (BackCom) has been gaining a lot of interest as a low-energy consumption energy harvesting solution. Here, the limited transmission range of BackCom systems constraint can now be resolved by using mobile data collectors or readers such as unmanned aerial vehicles (UAVs). In this study, we investigate a monostatic UAV-assisted BackCom system where backscatter devices (BDs) are served in a time-division multiple access (TDMA) fashion. Here we solve an energy efficiency (EE) maximization problem by jointly optimizing the transmit power allocation and the UAV's trajectory while adhering to the quality of service constraints. Since the problem is non-convex and combinatorial in nature, we employ a reinforcement learning framework that utilizes a finite-state Markov decision process. We introduce a low-complexity Ex-pected State-Action-Reward-State-Action (ESARSA) algorithm to determine the UAV's optimal trajectory with power allocation. A closed-form solution is proposed for global power optimization. In simulations, we compare the implemented ESARSA against the State-Action-Reward-State-Action (SARSA) and Q-learning algorithms and show that the proposed ESARSA algorithm can provide a 24% gain over the fixed allocation benchmark.
Research on Personnel Safety Risk Early Warning Technology Based on Power Infrastructure Samples

In response to the problems of dispersed professional knowledge, complex management system, and complex business processes in the infrastructure business control of State Grid Corporation, this article proposes a personnel safety risk warning technology based on power infrastructure samples. Firstly, preprocess the scattered infrastructure business data to form a high-quality infrastructure sample library; Next, based on the sample library, a safety risk knowledge graph for infrastructure personnel is constructed based on dynamic graphs; Finally, based on the constructed safety risk knowledge graph of infrastructure personnel, the PinSage model and DynGCN model are combined to capture the spatial structure features and time series features of the dynamic graph. The model is trained through comparative learning, and knowledge inference technology is used to predict the risk information of construction personnel, achieving rapid identification and warning of typical risk sections, providing intelligent auxiliary decision-making for the assessment of personnel behavior and facility status risks on the infrastructure site.
Efficient Deep Learning Model for Classification of Patients with Sleep Disorders from Healthy Individuals

We propose novel deep learning architectures, which are small and efficient in distinguishing individual sleep disorders from healthy people. We perform this classification in subject-independent mode, which shows its reliability in real-life scenarios. We propose models that utilize electromyogram, electrooculogram, and electrocardiogram and do not require electroencephalogram at all. This work uses a publicly available dataset called CAP Sleep database, which has overnight polysomnogram recordings from healthy subjects and patients with sleep disorders such as insomnia, narcolepsy, bruxism, nocturnal frontal lobe epilepsy, periodic leg movement syndrome, and sleep-disordered breathing. We have achieved F1 scores of 0.78 to 0.86 for the detection of different sleep disorders.
Research on Adaptive Camouflage System Based on Deep Learning

In the military field, camouflage technology has always been a key research object. Integrating camouflage goals with the environment has great significance on both sides of the war. Traditional digital camouflage is primarily designed for a single background and state. Its camouflage can only have a certain effect in a fixed time and place, but with the change of place and time, its camouflage performance is greatly weakened. Therefore, camouflage technology can change with the environment in real time, which is the inevitable development direction of the future military camouflage field. This paper proposes an adaptive digital camouflage method based on deep learning. The camouflaged target is identified by object detection algorithm, and the target is repaired by image repair algorithm, so that the image can be adapted to change into the environment under different scenes.
A Survey on Facial Emotion Recognition of Students/Office Workers Using Machine Learning

Emotions of a person gives insights on various aspects about a person, be it their physical or mental health, or even their personalities. Facial clues also help identify a person's emotions. This article presents a comparative study of different techniques which can be used for facial emotion recognition of students and office workers. Facial emotion recognition system can be a tool for educators, providing them the emotion dynamics of a class, can monitor the emotional state of employees, and identify the best suited candidates for various high stress job positions. This article presents a comparative study of the techniques which can be used for facial emotion recognition and identifies the best techniques.
Bt vs. non-Bt corn (Zea mays L.) hybrids: effect on degradation of corn stover in soil

Bt vs. Non-Bt Corn (Zea mays L.) Hybrids: Effect on Degradation of Corn Stover in Soil. (May 2009) Herminia Teresa Salvatore, B.S., Universidad Tecnologica Nacional, Argentina Co-Chairs of Advisory Committee: Dr. Frank M. Hons Dr. David A. Zuberer A billion tons per year of genetically modified corn residues are soil incorporated having both direct and indirect effects on the belowground environment, soil carbon (C) sequestration, and nutrient cycling. If Bt genetic modification has non-target effects on corn stover structural/non-structural carbohydrate and nitrogen (N) concentrations, then the degradation rate of Bt-corn stover may be different than that of non-Bt isolines, possibly influencing soil C storage and N mineralization. Thus, this research focused primarily on the comparison of C and N mineralization of corn stover in soil as affected by Bt-trait, plant portion, water-availability and HFC-trait; and secondarily on the existence of Bt-related variations in the chemical structure of corn residues that might affect the degradation rate of stover in soil and consequently the soil C and N dynamics. A laboratory experiment was conducted under non-limiting N conditions with stover of Bt/non-Bt isogenic pairs of two varieties, a ―high fermentable corn‖ (HFC) line harvested at Snook, Texas and a non-HFC corn line harvested at the irrigated field of Snook and the non-irrigated field of College Station, Texas. The stover was partitioned into three plant portions, incorporated into a Weswood soil and incubated during 223
Low Complex Receiver Signal Detection algorithms for GFDM Systems

Generalised Frequency Division Multiplexing (GFDM) is considered one of the most promising solutions for the 5G physical layer architecture. It overcomes the limitations of Orthogonal Frequency Division Multiplexing (OFDM) by providing more flexibility in designing the transmitter and receiver. Maximum likelihood (ML) is a popular receiver side signal detection algorithm that has good performance but suffers with high computational complexity. This paper proposes a low complex alternatives for Maximum likelihood by comparing performance and computational complexity of other non linear signal detection algorithms derived from maximum likelihood namely QR decomposition with M-algorithm maximum likelihood detection (QRM-MLD), Sphere decoding (SD) and find the best suited technique for GFDM systems.
An Explainable ML Approach for Diabetes Detection Using the Influential Features

Diabetes is one of the most common non-communicable diseases in the public health sector. Although diabetes doesn’t directly cause mortality, it does raise the risk of dying. Diabetes weakens the overall immune system of the patients. However, detection of diabetes in the early stage and proper treatment can save millions of lives. Nowadays, Machine Learning (ML) and Artificial Intelligence (AI) are contributing to disease diagnosis. ML can be useful to identify the influential features for diabetes detection. In this study, we have proposed a complete ML approach for identifying influential features to detect diabetes. We have used the Min-Max normalization and the iForest for outlier removal in the data pre-processing. To solve the class imbalance issue, the Synthetic Minority Oversampling Technique (SMOTE) has been used. The Chi-Square test as the feature selection method and the Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naive Bayes (NB) as the classification method have been utilized in this research. The SVM has achieved a classification accuracy of 99.58%, AUC 0.9881, and F1 Score 0.9824. The SHapley Additive exPlanations (SHAP) has been used to explain the classification method. The glycosylated hemoglobin, stabilized glucose, age, cholesterol/hdl ratio, and waist are identified as the influential features for diabetes detection.
MIMO Beamforming and Signal Modulation Design for Federated Learning Optimization

In this paper, we consider the optimization of federated learning (FL) over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp). In such a system, MIMO devices transmit their locally trained FL models to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. AirComp enables efficient wireless model aggregation by the PS in bandwidth-limited settings. However, wireless channel fading can produce distortions in AirComp-based FL. To tackle this challenge, we develop a novel aggregation scheme that combines digital modulation with AirComp to mitigate wireless fading while ensuring communication efficiency. We formulate this as a joint transmit-receive beamforming design optimization problem which dynamically adjusts the beamforming matrices to minimize the FL training loss with transmission errors. To solve this problem based on limited information at the PS, we employ an artificial neural network (ANN) to estimate the local FL models of all devices. Then, we derive a closed-form optimal design of the transmit and receive beamforming matrices based on predicted FL models. Numerical evaluations validate the advantages of the proposed methodology in terms of model training performance compared with baselines.
Guaranteed Data Recovery and Oscillatory Mode Estimation in Power System Using Novel RPCA

Presence of malicious injection occurs as the consequence of cyberattacks or PMU malfunctioning due to which the signals obtained from PMUs may have an impact on the stability & functionality of the power networks. A technique for detecting phony data in PMU measurements is presented in this paper. A robust Principal Component Analysis (RPCA) with Probabilistic Distributional Clustering (PDC) based technique is used to recover the malicious signals in the compressed sensing problem formulation for bad data detection. Lastly, Esprit based method is utilized for mode estimation. By doing a statistical analysis research on a synthetic signal using Monte Carlo simulation, the efficacy & resilience of the Novel Robust PCA technique are confirmed and contrasted with other recently discovered methods. Additionally, real probing data and two area data from the Western Electricity Co-ordinating Council (WECC) are used to validate this technique.
Competency Comparison of Deep Neural Networks for Identifying Gender in Color Fundus Photographs

Retina specialists and researchers have been using color fundus photographs for almost 100 years for diagnosing retinal diseases, systemic disorders and recognizing humans. In 2018, researchers for the first time showed that gender identification can also be done by color fundus photographs using a deep neural network (DNN). Subsequent studies further confirmed the capability of DNNs in fundus photograph-based gender identification. However, the findings of these studies cannot be directly comparable with each other, since different datasets were used in different studies. In this paper, we conduct a competency comparison of six DNNs in gender identification by using a single dataset of color fundus photographs.
A Comprehensive Study of Blockchain Technology Based Decentralised Ledger Implementations

Information management and decentralized, secure transactions are made possible by the groundbreaking idea of blockchain technology. Blockchain has attracted considerable attention from a wide range of businesses as a result of the rising popularity of cryptocurrencies like Bitcoin and Ethereum. The primary goal of this paper is to present a thorough examination of decentralized ledger systems based on blockchain technology. It examines at the basic ideas, underpinnings, and real-world uses of blockchain in many industries. The ability to scale, effectiveness, privacy, security, seamless integration, governing scenarios, and consumption of energy are just a few of the technical factors that are looked at in relation to the deployment of blockchain technology. In this research paper we also pinpoint forthcoming developments and future prospects for the blockchain industry like Solutions for scalability at the subsequent layer, protocols for seamless integration, integration with the Internet of Things (IoT), apps for decentralized finance (DeFi), and digital currencies that are issued by central banks (CBDCs) are a few examples.
Clustering Method of Driving Scenes based on Road Shape by using Road Map Images

Currently, vehicles are equipped with various sensors that can collect a series of driving behavior data while driving. The driving behavior data obtained in this way can be used to analyze the causes of traffic accidents and evaluate driver skills. However, when performing the above analysis, a specific driving scene is defined and driving behavior data for that driving scene is required. On the other hand, the series of driving behavior data obtained during one driving operation is enormous, and it is not easy to pick up desired driving scene data from among them.This research aims to establish a driving scene clustering method for a series of driving behavior data. In this paper, a driving scene is defined as a scene in which road shape and operation are associated, and propose a driving scene clustering method considered road shape. Here, a road map image is acquired from position information obtained from a GPS sensor, and the relationship between the road map image and operation data is clustered based on Coupled-GP-HSMM.
Construction of Smart Glasses Assisted Road Sign Recognition System

Visually impaired individuals are typically unable to perceive color information within the images they encounter during daily life. To assist such individuals in recognizing roadway signage while traveling within urban environments, this paper proposes a system utilizing smart glasses equipped with a Head-Mounted Display (HMD) device. The glasses frame utilizes a USB cable to separate the display unit from the control unit, which utilizes the Google Coral series development board. Additionally, the system employs the Yolov5m model for edge algorithm functionality. It captures images from the camera, adds target detection and character information, and displays the resulting reality picture on the HMD. This creates an edge AI mobile assistance system that helps visually impaired individuals recognize road signs.
Comparative Study of Proposed linear regression algorithm to Scikit-Learn Algorithm

The majority of real-world applications of machine learning employ supervised learning. With an input variable (x) and an outcome variable (y), supervised learning allows one to apply an algorithm to train the mapping function from the input to the result, which is Y = f(X). The goal is to approximate the mapping function as nearly as feasible to predict the output variables (Y) with fresh input data (x). Techniques used by supervised machine learning algorithms include support vector machines, logistic and linear regression, decision trees, and multi-class classification. To enable supervised learning, the data used to train the system should be labelled with the appropriate results. Often, linear regression is used to model the relationship that exists between a dependent variable and one or more independent variables. Using estimated parameters from the data, linear models are created. By trying to fit a predictive model to a collection of observed data values, linear regression can be used to forecast and predict outcomes. To minimize error, the least-squares method is widely used to fit linear regression models. Problems with classification and regression fall within the heading of supervised learning. The goal of both issues is to develop a compact model that can precisely predict the value of the dependent variable from the attribute variables. The dependent attribute is different for the two tasks since it is categorical for classification and numerical for regression. Using a dataset, we are proposing an algorithm for linear regression in this study. We are also comparing their implementations with and without Sklearn.