Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the Effectiveness of Tree-based Machine Learning Classifiers for Cybersecurity Threat Detection

No firewall is unbreakable and can contain errors. Attackers find many different ways to breach the firewall and network security. Numerous attacks use social engineering techniques or additional software to get user’s passwords and any other information. Intrusion Detection System (IDS). It is defined as a security system that investigates network or system activity to identify if any activity is unfriendly or unauthorized. With the enormous growth of the internet and computer advancement, the IDS have been pushed to their limit to keep up with these technologies and continuous study has been done to upgrade these systems. In today’s environment, hackers utilize several forms of assaults to obtain vital information. The main objective of this paper is to detect different kinds of attacks and suggest methods or ways to prevent them using ML models i.e., it aims to identify various attacks that are taking place on a user’s device using Machine Learning. After identifying the type of attacks, various suggestions are provided to the user to secure his device against attacks. At the core of our project is a Machine Learning project that uses various pre-available datasets that are available on the internet and our custom datasets. In this paper we have used 4 different datasets and 5 different ML algorithms. For suggestion various methods are fetched from the internet via an API. And to provide knowledge about IDS, types of attacks, tools and methodology used to detect them.
Translation-Equivalence-Based Unsupervised Ghost Imaging

Ghost imaging (GI), as a burgeoning imaging technology, has gained a lot of attention in the past few years. To achieve better reconstructed image quality at low sampling rates, deep learning is introduced to GI. While supervised learning is widely used in GI, unsupervised learning also deserves to be studied in depth as it does not need real labeled images to train a network. Following this idea, we proposed a translation equivalence ghost imaging (TEGI) method only based on unlabeled bucket signals. A new loss function is designed to do unsupervised learning. It is demonstrated that our TEGI method can reconstruct target images with better quality than the differential ghost imaging method at low sampling rates.
Rotation Control of a Novel Crane Gripper with Visual-Inertial Feedback

Achieving automated assembly with construction cranes is an essential step for efficient and sustainable construction of housing stock. A key requirement for automated assembly is the automated alignment of payloads to predefined orientations. For this reason, a novel hook-mounted crane gripper was constructed. This paper focuses on the rotation control of the gripper, which can be rotated by the thrust of two attached propellers. The rotation dynamics are described by a second order differential equation derived with the principle of angular momentum. The rotation angle and its speed are measured by a camera detecting ArUco markers and a gyro sensor of an inertial measurement unit (IMU). The crucial control design challenge is that the payload's moment of inertia is unknown and that the gyro measurement is biased at the same time. Knowing the moment of inertia is crucial for designing a controller with good tracking behavior and without overshoot. For this reason, an extended Kalman filter (EKF) is designed enabling the online identification of the gripper's moment of inertia. The observability condition of the extended system is checked. The EKF is then included in a Two-Degree-of-Freedom control structure, which is updated online. The controller is applied to the real gripper and its performance is illustrated based on measurement data captured during the trajectory tracking of exemplary set point transitions.
An Improved CCCV Controlled Charging Technique for Electric Vehicles

Quickly rising cost of fossil fuels, global warming, and CO2 emissions can all be addressed by using electric vehicles. In electric vehicles, the battery is used to power the electric motor. Due to improved charging methods that are controlled and quick enough, lithium-ion batteries are prominently implemented. A battery charger is designed to charge the battery while considering several factors like capacity, nominal voltage, and state of charge. To maximize the battery’s life cycle and give the necessary quantity of electricity to the EV motor, this paper discusses various charging technologies and their characteristics. The various battery charging types and power levels are introduced. MATLAB Simulation are performed and the results are analyzed for different charging techniques and which technique will be much suitable for on-board charger of the proposed lithium-ion battery (48 volts and 72 Ah capacity) for electric vehicles.
On past temperatures and anomalous late‐20th‐century warmth

Evidence from paleoclimatic sources and modeling studies support AGU's official position statement on climate change and greenhouse gases; namely that there is a compelling basis for concern over future climate changes, including increases in global-mean surface temperatures, due to increased concentrations of greenhouse gases, primarily from fossil fuel burning. More specifically a number of reconstructions of large-scale temperature changes over the past millennium support the conclusion that late-20th century warmth was unprecedented over at least the past millennium. Modeling and statistical studies indicate that such anomalous warmth cannot be fully explained by natural factors, but instead, require a significant anthropogenic forcing of climate that emerged during the 19th and 20th centuries.
The QUENDA-BOT: Autonomous Robot for Screw-Fixing Installation in Timber Building Construction

Autonomous robots are practical and innovative solutions to reducing the risk of fatigue and back injuries during mass engineered timber constructions. Screw installation is a common repetitive and strenuous activity performed by humans at construction sites. This article presents a novel robotic system for screw fixing installation. A range of technologies were implemented including those for mapping, localisation, sensing, planning and force-torque control of a robotic manipulator attached to a mobile base. Also developed was custom hardware for the screw driving operation. Extensive tests of the robotic system were conducted in lab and field environments. The results demonstrate the suitability of this robot for autonomous screw fixing installation.
Design of MIMO-FSO based IoT system

Free space optics (FSO) allows extensive transfer rate, low energy consumption and wide spectrum along with immunity against electromagnetic interference. This work reports a 10×10 Gbps multiple input multiple output FSO (MIMO-FSO) based internet of things (IoT) system under the impact of diverse climate conditions has been reported. The novelty of this work is to design an IoT based high speed as well as long reach wireless transmission system considering various environmental crises. The results show that FSO transmission range of 130 km for clear weather conditions. Also, the system using optimum value of transmission as well as reception aperture diameters means 10 and 40 cm respectively, FSO range can be considerably extended more than 110 km.
MAPE-K Loop-Based Goal Model Generation Using Generative AI

Goal modeling constitutes a systematic modeling of representation, specifically crafted to capture and depict stakeholders' intentions, desires, and objectives. Notwithstanding its importance, describing the entire scope of goals to be achieved remains a complex task. To address this challenge, we propose a semi-automatic goal model generation process. The feature of the process lies in its use of a generative AI based on the MAPE-K loop mechanism. We conducted two case studies that built goal models using this proposed process. The results demonstrate that our process, grounded on the MAPE-K loop mechanism, efficiently aids goal model construction.
Maithi-Net: A Customized Convolution Approach for Fake News Detection using Maithili Language

Online news consumers now face serious difficulties due to the widespread distribution of fake news on social media platforms. To distinguish fake news from real, the paper suggested a customized CNN model named "Maithi-Net". The model being suggested is composed of five convolution layers which are capable of automatically acquiring the distinguishing features essential for identifying fake news. Both the CGU-Maithili and ISOT fake news datasets have been used to successfully validate the proposed model. The efficacy of the model is verified with several evolution metrics like accuracy, specificity, sensitivity and F1 score. The model provides the detection accuracy 96.85 % for CGU-Maithili and 97.28% for ISOT fake news datasets. The experimental results show substantial gains over prior state-of-the-art results in the area of fake news detection and validate the potential of our method for categorising misinformation spread via social media.
Introduced the link topology structure of fertility group multiobjective salp optimization algorithm

To solve the problems of insufficient diversity, weak information exchange ability between populations, and easy to fall into local optimization when solving multi-objective optimization problems with the Salp Swarm Algorithm (SSA), based on the Pareto Envelop-based Selection Algorithm (PESA), a multi subgroup optimization for Salp swarm algorithm with multi chain ring topology is proposed. In this algorithm, a multi-link topology structure is proposed to enhance the information exchange between populations, and a dynamic multi-strategy subgroup is constructed to improve the diversity of the population. In addition, a population adaptive attenuation strategy is proposed to not only ensure population diversity, but also improve the convergence. The variation strategy of Levi's flight is used to jump out of the local optimal. By comparing the result sets of 8 multi-objective benchmark test functions, the proposed algorithm has certain competitiveness compared with other 5 multi-objective comparison algorithms.
Food Detection and Recognition with Deep Learning: A Comparative Study

Food detection and recognition involves the use of computer vision and machine learning techniques to identify and classify food items in images or videos. It has numerous applications, such as dietary tracking, nutrition analysis, and inventory management. This research paper presents a comparative study of six deep learning models (SSD (VGG-16), Faster-RCNN (Resnet-50), Faster-RCNN (Mobilenet-V3), Faster-RCNN (Mobilenet-V3_320), RetinaNet (Resnet-50), and YOLOv5) for food detection and recognition. The models' performance is evaluated using three publicly available datasets: School Lunch Dataset, UEC FOOD 100, and UEC FOOD 256. Notably, Faster R-CNN (Mobilenet-V3) achieved mAP of 0.931 in the School Lunch Dataset, while YOLOv5 achieved 0.774 and 0.701 mAP in the UEC FOOD 100 and UEC FOOD 256 Datasets, respectively. YOLOv5 demonstrates comparable results to Faster R-CNN but with a smaller input image size and a larger batch size in food detection.
Multi-Layered Cardiac Arrhythmia Detection Using RMSE Method

An unusual heartbeat is known as a cardiac arrhythmia. Heart rhythm issues develop when the electrical signals that coordinate the heartbeats are not functioning correctly. The heart beats abnormally, too quickly, or too slowly due to the poor signal. The majority of systems employ signal processing to assess electrocardiograms (ECGs). Accurate QRS (rpeak) detection is crucial for the investigation of variations in heart rate. The multi-layered framework consisting of IoT, Edge and Cloud for real-time processing of ECG data was used with cardiac arrhythmia detection using the Root Mean Square Error method. For efficient analysis of QRS detection, the suggested method uses a conventional arrhythmia data set from MIT-BIH. 29 ECG data were considered for the analysis of the proposed method and we obtained 82.75% accuracy of the arrhythmia detection.
An Underactuated Tendon-Driven Gripper with Variable Stiffness for Deformable Agri-Food Objects

Grasping and manipulation of deformable objects are challenging problems in the food-processing industry. Food objects vary wildly in shape, size, mass, and stiffness. We present the design and control of an underactuated tendon-driven finger-based gripper with as key features high adaptability and variable joint stiffness. We first present a kinematic analysis of tendon routing in the context of grasp force and adjustable stiffness, as well as a control strategy to leverage these features. We then present a proof-of-concept prototype, which is validated in experiment. The results show that stable grasps can be achieved adaptively, and that grasp force and joint stiffness can be regulated independently for fragile objects.
A comparative analysis of reflective and transmissive PPG sensor in pulse acquisition system

This study aimed to compare the performance of PPG (Photoplethysmography) sensors, based on time domain signals, frequency domain spectra, correlation coefficients, mean square errors, and signal-to-noise ratios (SNR). Two types PPG sensors were used: reflective and a transmissive PPG sensor. MAX30100 is a reflective PPG sensor used to acquiring the signal from the fingertip. Time domain signals acquired using the reflective PPG sensor contained more noise and motion artifacts than those acquired using the transmissive PPG sensor. The IR and RED-light absorption patterns from the MAX30100 were similar in time domain. The transmissive PPG sensor provided clearer pulse features than the reflective PPG sensor. The systolic wave phase showed more fluctuations in the IR and RED reflective PPG sensor. The correlation coefficients indicated a strong correlation between IR and RED PPG sensors and moderate correlation between IR and transmissive PPG sensor and RED and transmissive PPG sensor. The SNR values were found to be 14 dB for IR PPG, 11 dB for Red PPG, and 41 dB for transmissive PPG. These results suggest that transmissive PPG sensors are more reliable for pulse analysis compared to reflective PPG sensors.
Advanced Convolutional Neural Network Model to Identify Melanoma Skin Cancer

Today, a significant number of people are diagnosed with cancer of the skin. Melanoma and other types of skin cancer make up the vast majority of cases. The early detection and treatment of this cancer is the most wise and reliable technique for avoiding the development of the condition. The purpose of this research report is to offer a hybrid neural network model that can identify melanoma and non-melanoma skin malignancies with a higher degree of precision. This categorization model utilizes ACNN to integrate three different models. Both the training and evaluation of the model make use of two different benchmark datasets. In this investigation, the EfficientNetV2 model was used. This model showed better accuracy. The Inception layer of the model has been constructed, and the model’s overall accuracy is 98%. The model has a ROC value of 99, which stands for receiver operating characteristic. The model received a score of 97.5% on the F1 scale. As a main diagnostic procedure, this strategy is beneficial for patients as well as for clinicians, and it has the potential to reduce the death rate among skin cancer patients.
Performance Analysis and Anomaly detection of Power Distribution Insulators using Deep Learning Techniques

In the field of Machine Learning, Deep Learning(DL) is a rapidly growing area and it uses Convolutional Neural Networks(CNN) for image classification as it gives the most accurate results. CNN has various pre-trained architectures available for every type of problem. A variety of insulator parts with various forms and construction materials are found in the overhead power transmission lines. These Electrical assets are continually exposed to environmental factors and operational circumstances, which could result in size, color, and structure variations. This work offers a reliable image classification methodology that seeks to identify insulator classes effectively regardless of the degree of degradation. Here, we used various classical classifiers and identified the insulator defect pieces. In order to use a previously trained CNN as a feature extractor for training new classifiers, it must first undergo a step of fine-tuning. To determine which type of classifier provided the best non-conforming component discrimination performance, a comparison study was done.By identifying non-conforming component classes with an overall accuracy of 96%, the suggested methodology significantly improved classification performance.
Reset Observer Based Dynamic Event-Triggered Control of Multi-Agent Systems with Disturbances

This article addresses the reset observer-based event-triggered consensus control problem of multi-agent systems (MAS) with directed network and disturbances. Reset observer is developed instead of traditional observer, which contains an additional reset element. The reset element contributes to accelerating convergence rate of the estimation errors. An improved dynamic event-triggering mechanism (ETM) is developed, and it is rigorously prove that the developed dynamic ETM leads to larger inter-event times than the current results, and Zeno triggering behavior is ruled out by adding an exponential term in the ETM. Then, a hybrid dynamic model, is established for the MAS. On the basis of the hybrid model, whether the developed reset observer based controller can guarantee consensus property and the explicit form of event conditions are given. It is shown that convergence performance is enforced in contrast with linear observer-based approaches. Finally, the reset observer based design is illustrated by numerical simulation.
Multi-Agent Pickup and Delivery in Transformable Production

This paper focuses on multi-agent pickup and delivery (MAPD) in the context of the transformable production system. With the increasing demand for personalized products in today's globalized world, manufacturers are providing customers with the option to purchase items with specific details. Transformable production is a solution to manufacture customized products, which involves both static agents responsible for manufacturing and mobile agents responsible for pickup and delivery tasks. Numerous works in the literature have studied the MAPD problem. However, these works consider pickup-and-delivery tasks to be independent of each other. In the transformable production system, a delivery task can only start if the corresponding manufacturing process is finished. Therefore, it is necessary to consider the task dependencies when addressing MAPD in transformable production. To tackle this challenge, we propose a framework to solve this problem. We employ a heuristic algorithm to assign tasks to static and mobile agents and introduce a parameter that adjusts the expected pickup and delivery duration. Additionally, we introduce heuristic cost functions for path finding tailored to our problem. Through comparative analysis using synthetic test sets, we highlight the significance of the parameter used in pickup-and-delivery task scheduling in obtaining improved results. Furthermore, we demonstrate that our proposed algorithm and cost functions achieve slightly better performance than the baseline algorithm.
Analysis of evapotranspiration in the catchment of the Nurzec River, Poland using MODIS data

Abstract The objective of this study is to analyse the spatial diversity and temporal variation of evapotranspiration in the catchment of the Nurzec River in the years 2001-2010. The study is based on MODIS images highlighting monthly accumulated values of evapotranspiration, Normalized Difference Vegetation Index (NDVI), and land use categories. Monthly evapotranspiration was calculated for the whole catchment as well as in different land use categories. The study focuses on assessing the relationship between the values of NDVI and evapotranspiration. The annual ET according to MODIS varied from 392 mm to 458 mm. Deciduous broadleaf forests have a higher ET than other land use categories. The analysis shows a strong correlation between monthly ET and NDVI.
Path Tracking Control for Helical Microrobots Based on Fusion of Geometric and Model-Free Methods

Helical micron-sized robots driven by magnetic fields have garnered significant interest, particularly in the field of tracking control. However, at the microscale, time-varying uncertain perturbations in both the environment and electromagnetic system can cause these robots to deviate from their preset state. Here, we propose a novel control method that combines geometric and model-free controllers in a complementary manner to achieve accurate path-tracking of a helical microrobot on underwater unstructured surfaces. The geometric controller acts as a feedforward controller handling path information and formulating guidance law, while the model-free controller responds directly to the lateral error. To compensate for unmodeled nonlinear dynamics and unknown disturbances and achieve system dynamic linearization, we employ an extended state observer. To validate the proposed method and determine suitable ranges for input parameters of the controllers, simulations were carried out. The effectiveness of our path-tracking control method is validated through experimental results. The control technique provides an initial investigation into addressing the effects of uncertainties and perturbation on the control capabilities of magnetic microrobots.
Eco-design implementation in Power Electronics: a litterature review

Objective – In our society, the production of electricity from renewable sources and its usage in the mobility, air conditioning, and manufacturing industries are increasing day by day. To be able to use this electricity efficiently and to control and shape such energy from the various sources to the numerous types of loads, the required interfaces are Power Electronic Converters (PEC)s. The production and usage of PECs are today increasing, proportionally to the share of electricity in the energy mix of our societies. Therefore, the environmental impact of these PECs has become critical considering their accelerated usage not only in renewable energy sources but also in electrical household products, electrical mobility, etc. Therefore, PECs should integrate eco-design requirements and be developed, used, and repaired in a circular perspective. This review paper explores the current PEC eco-design practices implementation and the corresponding environmental impact indicators considered in these approaches. From this review, the discussion highlights the opportunities to enhance PEC products ’ eco-design considering circularity scenarios in a Design for Sustainability (DfS) perspective. Findings