Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of Water and Riverine Sand using Deep Learning on Multispectral Remote Sensing Images

The study of land-use-land cover (LULC) has become a necessity with the advancement of the urbanization process. Increased erosion of soil, increased silting, and sedimentation of the rivers are key effects that require study and analysis. Deep learning has a significant impact on classification tasks, particularly in the field of remote sensing image analysis. The proposed framework classifies LULC classes by employing the characteristics of deep learning. In this work, we compared the proposed method with the traditional machine learning methods in extracting water and riverine sand from multispectral remote sensing images. Further, we analyse the impact of Stochastic Gradient Descent (SGD) and Adam optimizers. The Adam optimizer implemented in this work gives higher accuracy than other combinations.
Social feature-based routing methods in delaytolerant networks: Assessment of current research

In recent years, the Delay Tolerant Network (DTN) has garnered significant attention from academics. Delay-Tolerant Networks (DTNs) are a type of network that concentrates on the issue of disconnection. However, the selection of the suitable relay node is a challenge in this dynamic context. Social feature-based routing addresses this issue by leveraging the social relationships among nodes. This study examines the utilisation of social features for current social feature-based routing. The analysis of social feature-based routing is conducted by evaluating the positive and negative social characteristics associated with it. This study offers a comprehensive analysis and assessment of the current state of research on routing algorithms in delay-tolerant networks that are based on social features.
Gluing-Free Soft Robotic Hands for Cake Topping

This paper focuses on cake topping performed by a soft robotic hands. Recently robots have been introduced into food factories for improving productivity and reducing labor costs. Robots are actively used in various manufacturing processes. Unfortunately, it is currently difficult to introduce robots into topping, which is a delicate operation dealing with soft food materials. Thus, this research challenges topping operations of cakes using soft robotic hands. We fabricated soft fingers without gluing. Soft fingers were cast in silicone rubber using gelatin as a sacrificial material. A soft robotic hand was composed of a pair of soft fingers. Topping experiments were conducted using the fabricated soft hands. First, we confirmed the usefulness of the soft hands by conducting a topping experiment using strawberry samples. Next, topping experiments were conducted using real strawberries and cake bases. On the ‘nappe’ base with a flat surface, strawberries could be topped straight in a uniform posture. Although the topping was successful on the ‘piping’ base with uneven surface, it was found that the posture of the strawberries varied.
Design of Dual Polarization Antenna Using Genetic Algorithm

This work presents a rectangular microstrip patch antenna for dual polarization applications at 8 GHz. Roger's substrate has been used as a dielectric material with loss tangent 0.0027 and relative permittivity as about 3.55. The transmitter and receiver ports of the antenna operates at different instants. Therefore, their insertion loss must meet at below -3 dB. Genetic Algorithm (GA) was used in the selection of various physical parameters of antenna to bring the return loss (SII) under - 10dB. Another requirement of the proposed antenna gain must be above 4 dBi at both ports (transmitter and receiver). Therefore, a multi-objective fitness function was considered to optimize the proposed antenna electrical performance and gain based on the tournament selection in GA. After achieving the matching and gain performances as per the requirement, the radiation patterns were analyzed in E-plane and H-plane.
Counting the Number of Spanning trees in Fan Graphs Families

The number of spanning trees is a parameter of architectural and dynamic proprieties of a graph. Computing spanning trees of a graph is a NP-hard problem in graph theory. Algorithms counting this number could implement universal algebraic methods namely "Matrix tree theorem". However, the higher computational complexity makes these algorithms ineffective in the practical areas for a large network. In this paper, we suggest a combinational method based on separation and contraction techniques, investigating analytic formulas compute the number of spanning trees in the fan graphs families.
Fractional Order PID Controlled PV Fed Buck Boost Converter with Coupled Inductor for Battery Charging Systems

solar energy is one of the promising alternate energy sources for easing the strain of fossil fuels. The load control is made better by the PV supplied Buck Boost converter with linked inductor. The many closed loop regulated techniques for battery charging systems are examined in this investigation. The objective of this effort is to enhance the battery charging system’s dynamic responsiveness for the use in electric vehicle applications. Modelling, simulation, and analysis of the resulting data are done for PV fed closed loop controlled battery charging systems. Using a FOPID controller, the suggested system’s performance has improved by means of peak over shoot, settling time, steady state error, and ripple in current.
Enhancing Sense of Agency in GUI-Based Teleoperation Through Robot Trajectory Generation Focused on Motor Imagery

This paper discusses the relationship between robot movements and sense of agency in the teleoperation of a robot arm using a graphical user interface. Based on the sense of agency model, this study focuses on the imagery of robot movements held by operators. It verifies the relationship between imagined robot movements and preferred actual robot movements, as well as the relationship between robot movements and the sense of agency during operations, through two experiments. These findings suggest that operators exhibit a bias toward favoring swifter motions than those depicted in the imagery. Additionally, this study suggests that when there are significant differences in the movement distance and speed between movements based on imagined robot actions and those that consider bias, the sense of agency decreases. In particular, when considering the bias towards preferring faster movements, adjusting the control to achieve the same acceleration as movements that consider this bias, even when the movement distance changes, can evoke a sense of agency during the operation.
Electrical Characteristic analysis of Gate Underlap Strain Channel Cylindrical GAA FET

In this study, a gate underlap strained channel Cylindrical Gate All Around FET with an 8nm gate length was proposed and analyzed to provide faster switching with a lower subthreshold swing. It was accomplished by raising on-current by the use of a Type-II hetero-band structure, which improves increased carrier density and strained engineering on the channel, improving carrier flow via quasi-ballistic movement, and carrier density. The proposed device is made up of a cylinder-shaped outer s-Si layer that surrounds and creates a quantum well with the center s-SiGe layer and gate underlap area. This boosts the switching speed of the device while decreasing leakage current and attaining a reduced subthreshold swing.
Analysis of User Behavior Patterns using Machine Learning Algorithms

A weblog is a dynamic record of transactions regularly updated by website visitors. It contains a variety of information, such as IP addresses, status codes, bytes sent, categories, and timestamps. The primary purpose of a weblog is to monitor user behavior and classify their interests based on different categories and qualities. This study aims to achieve two main objectives: first, to categorize successful responses, and second, to distinguish between normal and abnormal user behavior. The research process outlined in this paper involves several steps. It begins with data collection, where relevant information is gathered from the weblogs. The next step is data pre-processing, which involves cleaning and organizing the data to make it suitable for analysis. The researchers then employ clustering techniques to identify different patterns of user activity, enabling a quick assessment and prediction of user behavior. The main focus of the research is user prediction, achieved by analyzing user preferences extracted from the weblogs at various levels. To accomplish this, the researchers utilize various machine learning techniques. One of the implemented models is the Random Forest Classifier, which is utilized to predict user behavior based on specific input parameters.
Optimization Research of Transportation Costs and Efficiency of Multimodal Transportation System

Multimodal transportation is an important medium for the circulation of goods. Due to the insufficient consideration of cost factors in traditional models when predicting route selection, the cost of multimodal transportation route selection is relatively high. Therefore, a multimodal transportation route selection model considering transportation cost optimization is studied. This article uses the network graph theory in operations research to construct a multimodal transportation network graph. Based on the minimum total cost, a model is established, and the Dijkstra algorithm for solving the shortest path is used to solve the model. The feasibility of the entire model is confirmed during the process of solving the example problem. This article provides suggestions for strengthening the establishment of logistics systems, strengthening the application of information technology, and strengthening the construction of standard systems, in order to improve the efficiency of multimodal transportation.
Synthesis of Speech Reflecting Features from Lip Images

Recently, models that synthesize speech from multimodal information other than text have been devised and have attracted much attention. We focus on lip image information and propose a model for speech synthesis that reflects lip movements. The architecture consists of an image feature extractor using an autoencoder and an encoder-decoder model similar to the Tacotron2 that outputs a mel spectrogram. We have succeeded in synthesizing speech that reflects lip movements under limited conditions.
Unlocking 5G Potential: Exploring a Single-Layer MIMO Antenna for Enhanced Applications

This article presents a hexagonal Multiple Input-Multiple Output (MIMO) antenna designed for 5G at 27.5 GHz in the mm-wave spectrum. With soaring data demands, the 28 GHz mm-wave band is vital for high data rates and minimal air attenuation. The hexagonal MIMO antenna efficiently mitigates interference while maintaining a compact design. Utilizing MIMO technology to its fullest, the antenna delivers substantial enhancements in system performance. It achieves exceptional isolation, surpassing -29dB across the entire operating bandwidth, and boasts an impressive peak gain of 6.06dBi. The proposed antenna has robust performance with 89% of overall efficiency and minimal return loss. Beyond 5G, this antenna holds promise in AI applications, serving as a wireless signal collector for critical feature extraction. Notably, in radar data collection, it aids object identification and tracking, critical in autonomous vehicles, aviation, and defense. This article quantifies the antenna’s advantages, emphasizing its role in advancing 5G and its potential in enhancing AI systems. In summary, the hexagonal MIMO antenna is a high-performing solution poised to revolutionize 5G communication and open new vistas in AI signal processing.
Car Lane Detection using Deep Learning Techniques

Crime is a global problem that affects individuals, communities, and societies at large. There are many different types of crime, and the rates at which they occur vary widely from country to country. While the need to be able to detect crime as it is happening is a very important part of safety and security, systems that can predict and analyze crimes are just as vital to a safer world. Understanding crime patterns and trends is crucial for effectively addressing the issue. One way to gain insights into crime is through the use of crime data analysis systems. These systems can help law enforcement agencies, policymakers, and researchers to identify patterns and trends in crime, as well as evaluate the effectiveness of different crime-prevention strategies. In our research we used four distinguished approaches: eXtreme Gradient Boosting (XGBoost), CatBoost, Tabnet, and a hybrid model 1 Dimensional Convolution Neural Network through three individual datasets to compare the results of each approach as well as a comparison between the three datasets. Our results demonstrated a very significant advantage of utilizing feature oriented datasets over a large sample size such as the Chicago dataset. We observed XGBoost provides the best results for Chicago and San Francisco 96.51% and 26.50% respectively. And as for Boston, we obtained best accuracy (20.80%) using the Bagging classifier.
Proposed Framework for Performance Tuning in Enterprise Applications using Machine Learning

IT systems and enterprise applications are crucial and their failure can lead to production losses, data loss, and missed commercial opportunities, resulting in costly efforts to recover. Nowadays, most of the applications are data-intensive, as a result of which the systems and the applications are becoming more and more complex in terms of their configurations. There are hundreds of configuration knobs that affect the performance of the system. Due to these changing environments and requirements of the software systems, the systems should be robust, versatile, autonomous, energy efficient and customizable at run-time. These requirements of the system to maintain its performance has surpassed human capabilities. Therefore, this research study proposes a novel approach to automatically tune the performance of the system using machine learning approaches. Here, the idea is to propose a technique that will identify anomalous behavior of the system because of one or more configurational parameters and auto-remediate the system to its normal state.
Identification of Indian Traffic Signs Using Artifical Neural Network

The paper addresses a crucial aspect of modern transportation technology by focusing on enhancing the robustness of traffic sign recognition systems. In the context of intelligent transportation systems, accurate traffic sign recognition plays a vital role in ensuring road safety and optimizing traffic management. This study introduces an innovative approach that combines color information with edge magnitude patterns, demonstrating its effectiveness in achieving robust traffic sign recognition. The proposed methodology includes data collection from diverse sources, grouping traffic signs into categories, preprocessing steps for standardization, and the extraction of 22 features using Gray Level Co-occurrence Matrix (GLCM) analysis. These features are then organized into a database and utilized for training a feedforward neural network with two layers for precise traffic sign detection. The project culminates in an overall accuracy rate of 90.2%, underscoring its potential to significantly enhance road safety and traffic regulation.
Deep Learning Based Automated Pneumonia Detection from X-ray Images

Artificially Intelligent (AI) based systems possess the capability of successfully realizing the insights from frequently available huge datasets. Various health care and medical imaging applications have been significantly benefited for making accurate decisions for improving the health conditions of the patients. Nowadays, medical science area have witnessed enormous advancement with the effective utilization of AI and deep learning (DL) techniques for various applications namely automated patient monitoring, prescribing medications, in-depth analysis of severe disease patterns and performing laser based surgeries based on the observations of the disease patterns occurring in the medical based digital images. Most of the automated medical oriented digital images are generated using MRIs, X-rays and CT scans which makes the processing of such images by numerous modern AI and DL algorithms efficient and highly accurate for critical decision making and medical treatment diagnosis. Conclusively, human based expert system is replaced by various AI and DL based systems which has fast processing and accurate reporting capabilities especially in medical science domain. Thus, our article proposes the utilization of AI and DL based techniques to efficiently propose diagnosis for patients suffering from pneumonia based on the gathered X-ray images. Precisely, our article focuses upon the effective utilization of DL based modified convolutional neural network (CNN) and visual geometry group (VGG-16) for accurately classifying the chest X-ray images into normal and pneumonia categories. Proposed model based on the recognition of the pattern changes in the chest X-ray images have the ability to classify the disease into either a normal or a pneumonia case with an overall training and validation accuracy of 97% respectively.
A Preliminary Study of a Soft Artificial Pump Based on ROBIN - Rotation-Based Buckling Instability Analysis

This paper introduces a novel soft artificial fluidic pump based on the conceptualization of ROBIN - Rotation-based Buckling Instability Analysis, which leverages the unique deformation of a two-layer shell-body under rotational actuation to achieve pumping capability. In more detail, the proposed device, hereafter, called ROBIN-based Pump, employs two servo motors to twist soft skin layers until buckling deformation manifests as a series of inward folds. Such complex transformation triggers a significant collapse of the fluid cavity (e.g., water, in this paper) to perform the pumping action. The proposed soft pump underwent empirical evaluation through a series of experiments with different actuation states defined by the speed ω and rotation angle θ of the motors. The main aim is to establish the relationship between actuation configuration [ω θ] and the ROBIN-based pump's characteristics described by two features: the flow rate of the fluid stream and the generated stroke volume. Preliminary results demonstrate that the soft pump could fulfill the functional requirements for either soft robotic mechanisms (e.g., soft grippers) or biomedical devices (e.g., human heart's simulators or support devices). This work is expected to showcase a sustainable approach to developing soft robotic systems, where the deformation of one component could provide energy to others.
Performance Analysis of Acoustic Scene Classification Using ANN and CNN Techniques

Artificial neural networks have achieved notable improvements in various learning tasks and have also been used to classify environmental sounds. The technique for allowing devices to recognize and interpret the surroundings from a recording is called Acoustic Scene Classification (ASC). Computational Auditory/Scene analysis is analyzing scenes using computational techniques. This research paper’s primary goal is to categorize audio recordings based on the scenarios or environments in which they were captured. In most applications, deep learning is one of the most recent trends. In this work, we have concentrated on two deep learning techniques employed to classify audio scenes: Convolution Neural Networks (CNN) and Artificial Neural Networks (ANN) to develop the model. ReLU and LeakyReLU activation functions are used to train and validate the model. For evaluating the model performance, K-fold cross-validation was used with K=10 and obtained an accuracy of 75.8% using an Artificial Neural Network and a proposed model with 90.6% accuracy using a Convolutional Neural Network. Comparing the results with other baseline models that achieved a performance of 73.3% for MCLNN with Mel-spectrogram, 78.2% for Pyramid-Combined CNN, 78.3% for Envnet-v2, and 81.9% for D-CNN.
Detection and Alerting of Driver Drowsiness using a Machine Learning Approach

Driver fatigue is a significant causative factor to accidents worldwide. To signify this issue, we have created a prototype system for detecting tiredness in drivers. This system monitors the driver’s level of drowsiness by tracking their eye movements and issuing an alert if prolonged eye closure is detected. Our focus is on improving driver safety while maintaining a non-intrusive approach. In this project, we utilize the Haarcascade library in OpenCV to detect facial features and accurately detect eye blinks. Through research in the field of computer engineering, we aim to prevent accidents caused by fatigued or sleepy drivers. The project discusses various strategies, their outcomes, and the implemented fixes. By providing insights into the system’s functionality and potential adjustments, the article aims to enhance the system’s overall effectiveness. Furthermore, the project offers observations and recommendations for future optimization in this field, with the goal of maximizing utility at a lower cost and promoting safer roads. The article provides a practical understanding of the system’s operation and serves as a valuable resource for advancing research and development efforts in driver drowsiness detection.
Research on the Application of Association Rule Algorithm in Big data of College English Teaching

In this paper, data mining tools and methods are used to provide data basis for college teachers to evaluate students’ English learning, and personalized evaluation model is developed. Teachers’ teaching methods are adjusted according to the evaluation results of data information, so as to understand students’ learning ideas and facilitate English teaching management. Therefore, in college English teaching, association rule algorithm is used to explore valuable teaching information from students’ English data database, so as to explore feasible teaching programs. Based on the actual situation of college English teaching database, this paper analyzes the application of college English teaching big data based on association rules algorithm. From the experiment, we discovered that teaching methods affect teaching effectiveness, and it is necessary to improve hardware facilities and teaching methods. The correlation between teaching attitude and teaching effectiveness is high, it is necessary to correct the attitude of teachers, encourage them to participate in training, and improve their physical and mental health.
Automatic Crowd Detection, Prediction and Surveillance System using Collaborative IoT Sensor Network for Physical Security

Surveillance systems of the modern day have been improving upon the concept of physical security. From manual inspection, to CCTV surveillance, there have been many different techniques that have evolved over the years. Although the traditional method of monitoring and estimating crowd density involves analyzing footage and applying equations to find out estimated number of persons, this paper discusses a different approach to crowd monitoring, by focusing on WiFi-enabled devices on each person and is carried out by utilizing collaborative Internet of Things sensors to scan the environment for Wi-Fi enabled devices and observe the number of devices. Not only the number of persons are found from the surveillance videos, but also to predict future value of number of persons by using Time Series Forecasting through the proposed method. This method is found to be accurate in estimation of numbers as it does not depend on image and video to capture the density of crowds.