Miracle-dz/UniFAR-FTUs huggingface dataset:
Weed Identification and Removal: Deep Learning Techniques and Research Advancements

In the next three decades, crop production must be doubled as the population is likely to increase by 35 percent. To enhance crop production to a great extent, the issues behind the scene should be rooted out. Right from the Neolithic era to this new age, weed identification and removal remains a significant challenge to attain a good crop yield. Technologies have supported agriculturalists on a large scale to overthrow this issue. This paper discusses such latest technologies, advancements, and methods imposed in the field of weed detection and removal. Overview of weeds, state-of-the-art deep learning techniques, and their research efforts in detecting weeds are briefed. Finally, current challenges, future improvements, and research ideas on weeds are pointed out for the sake of researchers.
Reliability Facility Location with Fuzzy Demand and Failure Scenarios

Historical war experience demonstrates that crashing critical supply facility has become an efficient tactic during wartime, so it is of great strategic significance to design an efficient and reliable emergency logistics network in face of the uncertainty at war. This paper presents a scenario-based modelling method by considering both demand uncertainty and facility failure scenarios in the mathematical model and the immune genetic algorithm (IGA) is applied to solve it. A set of numerical experiments has been conducted to verify the model and effectiveness of IGA. Finally, the sensitivity analysis results show facility failure probability has a greater impact on the final location decision, which provide model and approach for the location-allocation problem during wartime.
Future Glycemic Events Prediction Model Based On Artificial Neural Network

Predicting future glycemic events such as hypoglycemia, hyperglycemia, and normal for type 1 diabetes (T1D) remains a significant and challenging issue. In this study, an artificial neural network (ANN)-based model is proposed to predict the future glycemic events of T1D patients. We utilized five T1D patient datasets to build the models and predict future glycemic events with a prediction horizon (PH) of 30 and 60 minutes ahead of time. We applied the data preprocessing method based on the sliding window approach by sliding the blood glucose time-series data from the past 60 minutes (the last 12 data points) as input and using the next 30 and 60 minutes (the next 6 and 12-th data points) as output. All the numeric blood glucose output data are then transformed into a multi-class classification label, such as hypoglycemia, hyperglycemia, and normal. Our proposed model is then used to learn and create the prediction model from the preprocessed blood glucose dataset. Four performance metrics such as accuracy, precision, recall, and f-1 score were utilized to measure the performance of the classification models used in this study, such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN). The results showed that our proposed ANN-based model performed better at predicting future glycemic events than other models, with an average accuracy, precision, recall, and f-1 score of 88.649%, 76.661%, 71.731%, 72.609%, and 83.364%, 60.437%, 61.345%, 60.62% for the PH of 30 and 60 minutes, respectively. As a result, knowing this future glycemic event sooner can help patients avoid potentially dangerous conditions and can eventually be used to improve diabetes management.
Multi Attributes Recognition from Human Gait Analysis using MotionSense Dataset

Human Gait analysis is a very prodigious and flourishing field of research nowadays, due to its immense importance in clinical and medical studies, rehabilitation, security and surveillance, crime investigation, health, sports, development of marketing applications and product optimization etc. Every human has a distinctive gait pattern, which with critical scrutiny may exhibit a lot of information about his identity and personal traits. Although researchers have made remarkable efforts in this field of research but there is a lack of work regarding sensorial gait analysis for identifying multi-attributes of a person. This paper proposes a novel framework to recognize multi-attributes i.e., user, gender, age and weight of a person based on gait analysis using smartphone built-in sensors including accelerometer, gyroscope and motion sensor. We have used an existing dataset named “MotionSense” for human activity and attributes recognition. Multi-class machine learning algorithms are applied for training the dataset. We have achieved the accuracy of 99.75% for User, 99.74% for gender, 99.61% for age and 99.74% for weight recognition respectively. Experimental results and performance evaluation of the applied machine learning classifiers reveals the efficacy of the proposed scheme.
Application of a Gravitational-Benchmark Model for Road-Transport Energy Evaluation: Simulation Results

A systematic approach has been composed for studying the possibilities for application of an existing criterion model for informative assessment of the energy efficiency of transportation processes. Based on the proposed methodology and on six selected typical objects in the field, extensive simulation studies have been performed based on the average fuel consumption of motor vehicles. New data have been obtained for efficiency coefficient of transport processes, as the efficiency coefficient varies within the intervals (0.004… 59.7) %, (0.001…14.92) % and (0.001…3.89) % for the objects of classification groups A1, A2 and B, respectively. The performed research is the first step of the justification of the workability and the practical applicability of the criterion model.
A Comparative Analysis of Object Detection and Human Activity Recognition in Thermal Imaging

Over the last few years, algorithms and techniques for object recognition and human activity recognition have advanced dramatically. The introduction of thermal imaging has accelerated system performance and accuracy. This paper is a comparative examination of diverse methodologies and a quick assessment of several that require development so far in the subject of object recognition and human activity recognition in thermal pictures. In thermal pictures, the survey is divided into two sub-areas: object detection and human action detection. This research examines the performance of object and human activity recognition in thermal images techniques that were reported in the open literature within the last seven years (2016–2022). There is also a comparison table with the works covered and also given information's ad locations about datasets. This research study classifies the existing research study by using a complete taxonomy, which includes object and human action recognition, as well as the datasets to provide an understanding of those methods along with a potential comparison. Finally, the potential research directions are mentioned.
Spatiotemporal Emotion Recognition Method Based on EEG Signals During Music Listening Using 1D-CNN & Stacked-LSTM

Recognizing people's emotions accurately can help to improve people's feeling of happiness by adjusting their emotion immediately, which makes emotion recognition an active research topic recently. Electroencephalography (EEG) signals, which are electrical response of the human brain scalp, reflecting people's emotions and psychological activities, can be applied as an important tool for the emotion recognition. This paper focuses on the emotion recognition based on EEG signals during music listening. To this end, we first propose an emotion recognition scheme by combining the one-dimensional convolutional neural network (1D-CNN) and the stacked long short term memory (Stacked-LSTM), where the 1D-CNN is exploited to extract spatial features from EEG signals automatically and the Stacked-LSTM is applied for further temporal features extraction. We then conducted lots of experiments to validate the efficiency of our proposed scheme regarding the accuracy of emotion recognition. Finally, a comparison between our proposed scheme and other commonly methods used for emotion recognition based EEG signals (e.g., EEGNet, 1D-CNN, LSTM and SVM). The experimental results showed that our proposed scheme is feasible and outperform other commonly used methods in terms of classification accuracy.
Convolutional Neural Network (CNN) Algorithm Based Facial Emotion Recognition (FER) System for FER-2013 Dataset

Facial expression recognition (FER) is the key to understanding human emotions and feelings. It is an active area of research since human thoughts can be collected, processed, and used in customer satisfaction, politics, and medical domains. Automated FER systems had been developed and have been used to recognize humans' emotions but it has been a quite challenging problem in machine learning due to the high intra-class variation. The first models were using known methods such as Support Vector Machines (SVM), Bayes classifier, Fuzzy Techniques, Feature Selection, Artificial Neural Networks (ANN) in their models but still, some limitations affect the accuracy critically such as subjectivity, occlusion, pose, low resolution, scale, illumination variation, etc. The ability of CNN boosts FER accuracy. Deep learning algorithms have emerged as the greatest way to produce the best results in FER in recent years. Various datasets were used to train, test, and validate the models. FER2013, CK+, JAFFE and FERG are some of the most popular datasets. To improve the accuracy of FER models, one dataset or a mix of datasets has been employed. Every dataset includes limitations and issues that have an impact on the model that is trained for it. As a solution to this problem, our state-of-the-art model based on deep learning architectures, particularly convolutional neural network architectures (CNN) with supportive techniques has been implemented. The proposed model achieved 93.7% accuracy with the combination of FER2013 and CK+ datasets for FER2013.
A Robust Feature Extraction Method for Sound Signals Based on Gabor and MFCC

Along with the development of data scale and the high complexity of sound signals, feature extraction and classification methods of sound signals have become a major research hotspot. However, the current sound signal feature extraction methods are difficult to accurately and stably provide a high-precision classification effect for the sound signal due to the complex frequency distribution and the influence of noise. Therefore, a robust feature extraction method for sound signals based on multi-scale and multi-directional Gabor filters and Mel frequency cepstral coefficient (MtWGM) was proposed. This method performs preprocessing on the signal by mixing hard threshold and soft threshold wavelet denoising. The Gabor filter is used to the combined energy spectrum of the framed signal, to achieve the effect of relatively more balanced intra-class features and more prominent inter-class features, and finally improve the noise reduction performance and classification accuracy of sound signals. The experiments are conducted out on three different sound signal datasets. Three classifiers are trained to test the effectiveness of extracted features. The experimental results show that the proposed multiple wavelet Gabor_MFCC (MtWGM) method has obtained better classification accuracy and robustness than that of MFCC.
Using Artificial Bee Colony and Dragonfly Algorithms to Improve the Accuracy of Parameter Estimation of Li- FePO4 Battery Cell

Studies on li-ion batteries, which are increasingly used in various technological fields, are spreading to different areas. One of these study subjects is to determine the battery parameters with high accuracy and transfer them to the test and simulation system. In a study conducted for a similar purpose, the A123 ANR26650 Li-FePO4 battery cell has been discharged at room conditions with 2000 mA constant current. The real terminal voltage has been obtained by analyzing the obtained data. Mathematical expressions of internal resistance and terminal voltage are defined. The terminal voltage is characterized as a third-order function dependent on the state of charge. Simulink/Matlab, curve fitting, artificial bee colony algorithm, and dragonfly algorithm are used to forecast terminal voltage. While comparing the methods, the Simulink model has not been included in the comparison because it has been the ideal model. As a result, the most consistent method has been determined by using different statistical methods such as the actual error values, the sum of the squares of the errors, and the mean value of the sum of the squares of the errors. Artificial bee colony algorithm has performed best with an mean value of the sum of the squares of the errors of 1.1822e-05. It has emerged that dragonfly algorithm needs to be developed and a new field of study has been opened.
Solar Vapor Generation System Operating without Power Supply

We report an efficient and cheap method used to purify/desalinate water by combining porous paper, carbon black powder, and polystyrene foam. The properties of paper dyed with carbon black are then characterized by UV-VIS, SEM, and infrared camera analysis. The evaporation rate of 0.7 kg/m2.h was obtained when the system operated for 4 hours in the outdoor environment on sunny-cloudy days in Ho Chi Minh City and Ninh Thuan Provinces, Vietnam. The thermal insulation of polystyrene foam prevents the radiative adsorption of the bulk water and lessens the heat loss due to convection, increasing thermal efficiency. The system operates without a power supply and facilitates utilizing cheap and environmentally friendly materials, lessening the burden of a shortage of water in the rural areas.
Experimental investigation of 1:25 scaled model of Pendulum Wave Energy Converter

The experimental campaign of the 1:25 Pendulum Wave Energy Converter (PeWEC) scaled model is presented with the aim to understand the hydrodynamic behaviour of the device in operative and severe sea state conditions. The experimental setup is described and particular attention has been given to the mooring system scaling procedure and realization. Tests of system identification, irregular waves and extreme waves of the floating-moored model of the PeWEC device have been performed at the University of Naples Federico II. The results of the hydrodynamic analysis shows the influence of non-linearities depending on the sea condition. In addition the influence of the wavefront direction is evaluated through the loads acting on the mooring lines, resulting favorable for a wave front acting on the bow of the hull.
Distributed Adaptive Formation Control for Nonholonomic Mobile Robots over Noisy Communication Networks

In this paper, we solve the formation problem of nonholonomic mobile robots (NMRs) over noisy communication networks. The communication signals with noisy information are exchanged among different NMRs by a directed graph. Only the individual mobile robot directly receives the actual and feasible signals itself without communication. Besides, the sensor-to-control signals suffering from the noise are modeled by a nonlinear function including unknown parameters and uncertainties. Combined with the adaptive control technique and robust control technique, the unknown parameters and uncertainties of noisy signal models are respectively estimated and compensated, respectively. Then, a novel distributed adaptive formation controller is proposed to guarantee convergence to the optimal positions over noisy communication networks. It is proved that the formation errors are convergent to a small neighbourhood of origin and closed-loop signals of each NMRs are bounded. A simulation example is given to demonstrate the theoretical studies.
Spallation model for the high strain rates range

Measurements of the dynamic spall strength in aluminum and copper shocked by a high power laser to pressures of hundreds of kbars show a rapid increase in the spall strength with the strain rate at values of about 107 s−1. We suggest that this behavior is a result of a change in the spall mechanism. At low strain rates the spall is caused by the motion and coalescence of material’s initial flaws. At high strain rates there is not enough time for the flaws to move and the spall is produced by the formation and coalescence of additional cavities where the interatomic forces become dominant. Material under tensile stress is in a metastable condition and cavities of a critical radius are formed in it due to thermal fluctuations. These cavities grow due to the tension. The total volume of the voids grow until the material disintegrates at the spall plane. Simplified calculations based on this model, describing the metal as a viscous liquid, give results in fairly good agreement with the experimental data and p.
Analysis of Track Support Structure of Mountain Double-Track Transport Vehicle Based on Neural Network Response Surface Model

In the transportation of heavy materials in mountainous areas, there are problems such as low operation efficiency, high labor intensity, and serious environmental damage. Mountain double-track transport vehicles can effectively solve the above problems. The support scheme of the track is the key and difficult point of the design. This paper uses the ANSYS Workbench 19.0 tool to analyze the strength of the track support structure of the double-track transport vehicle, and conducts statics simulations for the three support structures simultaneously. The maximum deformation of the support structure is regarded as the goal, the Neural Network response surface model is used to optimize the design of the parameters of the three support structures. The results show that the stress of the diagonal bracing structure scheme is optimal; within the allowable range of the structural design, the height of the connection point of the diagonal bracing can be increased as much as possible to increase the structural stability.
Detection of Lanes and Objects Using Deep Learning Techniques

Over a period of time, the advancement in technology of computer vision has kept the users astonished. Computers replicate the processing of images same as that of humans and this branch of technology is called computer vision, which is part of artificial intelligence, and then provide the desired output. The goal of computer vision is to reconstruct the qualities of the world we observe. Recently, many experts and tech firms are in a race to prosper the development of self- driving cars. The ingenious innovation in the field of automation is self-driving cars. They provide a major solution to turn down the accidents and put a stop to damaging society. Self-driving cars have major issues in the detection of the road lanes and objects on the track. So, they need robust assistance to reach their destination safely. Consequently, sophisticated algorithms are used to detect lanes and objects appropriately. A deep learning approach is improved to obtain lane detection results. Our model implements a convolutional neural network (CNN) and OpenCV techniques to predict the lanes. Our model accepts the input video containing the road lanes and outputs the detected lanes stacked with green color. Subsequently, we extended the project to detect objects. To detect objects, we used the You Only Look Once (YOLO) algorithm that was trained using the COCO dataset. In the end, we combined the results of lane and object detection so that our final output will contain both detected lanes and objects.
Deconvolution in Compton profile measurements

Abstract Various deconvolution schemes are discussed in terras of harmonic analysis and then considered in relation to line profile measurements. A new practical solution of the method of least squares together with a tail stripping procedure and a realistic error estimation are presented. The use of a residual instrumental function in comparing experiments with a theory is suggested. The method is applied to γ-ray Compton profile measurements.
A Deep Learning-Based Approach for Pipeline Cracks Monitoring

In order to improve the efficiency and accuracy of pipeline surface cracks monitoring based on image processing, the Convolutional Neural Network (CNN) algorithm in target detection is introduced to quickly identify the type, location, and area for the extracted cracks area with borders, the CNN based on crack contour network (CCN) method used to locate and extract the crack shape. CCN algorithm introduces the accuracy rate (P%), recall rate (R%), and F-score (F%) index to evaluate the algorithm in the problem during cracks monitoring, and determines the corresponding contour area of the crack frame according to the maximum F-score. A pipeline image was carried out by using an inspection drone with high definition camera. The results show the recognition efficiency and accuracy of the proposed method. After the optimal value of the degree threshold, the accuracy rate, recall rate, and F-score are recorded 91. 8%, 86. 1%, and 84.6%, respectively.
Genetic Algorithms Optimization of a Reinforcement Learning-based Controller for Vertical Landing Rocket Case

A reward function in reinforcement learning is the formalization of the objective. Finding the ideal reward function is a challenge, that needs a search strategy to be constructed. Genetic Algorithm is a suitable approach for reward function search due to its thoroughness. The Deep Deterministic Policy Gradient (DDPG) algorithm, which is the focus of this research, is a reinforcement learning-based controller which performances are improved after the Genetic Algorithms optimizes the agent's reward functions. The optimized controller results in narrower missed distance and lower landing velocity compared to referenced DDPG controller, and significantly less fuel consumption compared to PID.
Proteins Classification Using An Improve Darknet-53 Deep Learning Model

Nowadays, the quantity of protein sequences saved inside the central protein database from laboratories around the sector is continuously increasing. The purpose is that experimental shape elucidation is exertions extensive and may be very time-consuming. Therefore, we want an automatic device that may classify the protein. The increased number of softmax classifiers and leakyrelu activation layer is used instead of the softmax classifier in the original darknet53 model which originally is less in number. Therefore, modifications helped in improving the structure and parameters of the Darknet model. These results revealed that this technique can also efficiently extract multi-layer features from protein images, regardless of batch size, and with greater accuracy. This model has the greater performance with an accuracy of 94.94 percent. The meaning of the experiment provides insight that helps biologists and scientists build the overall protein structure.
Load balancing strategy for wireless multi-hotspot networks based on improved NSGA-II algorithm

IEEE 802.11 Wireless Local Area Network (WLAN) is a popular way for mobile devices to access the Internet. Operators and service providers often increase the density of wireless access points in order to provide users with better connections and user experience. As a result, WLAN users find themselves covered by multiple access points. In traditional wireless multi-hotspot networks most user terminals choose the access point with the strongest signal without considering the existing load on that access point, leading to severe congestion and load imbalance problems. This paper proposes an improved NSGA-II-based load balancing strategy for wireless multi-hotspot networks, which determines which hotspot should be associated with each user terminal by taking into account the current access point load, the received signal strength and the throughput size of the user terminal. The simulation results show that compared with the traditional strongest signal First (SSF) strategy, the proposed load balancing strategy can improve the network throughput by more than 30%.