Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-based Weather Prediction: A Comparative Study of Regression and Classification Algorithms

Accurate weather forecasting is essential in many industries, including agriculture, transportation, and disaster management, making it a prime use case for machine learning algorithms. In this study, we investigate how to forecast several types of weather, including rain, sunshine, clouds, fog, drizzle, and snow, using a variety of fundamental machine learning methods and boosting algorithms. To train and evaluate the various algorithms, we utilized a dataset made up of historical meteorological data, including characteristics like temperature, humidity, wind speed, and pressure. We performed tests on many machine learning methods, some of which you may be familiar with: decision trees, random forests, naive bayes, k-nearest neighbors, and support vector machines. We also used boosting techniques like XGBoost and AdaBoost to further enhance the precision of our forecasts. Our results indicated that XGBoost and AdaBoost, two popular boosting algorithms, achieved the highest levels of accuracy (87.86% and 87.33%) compared to the other algorithms we tested. The findings were verified using ROC Curve Analysis and Lift Curve Analysis, which demonstrated that the XGBoost and AdaBoost models performed better in terms of true positive rate, false positive rate, and lift.
Virtual Community based Peer-to-Peer Energy Trading

With the increase in the use of renewable energy sources (RESs) and advances in technologies, peer-to-peer (P2P) energy trading is emerging as a promising approach to the local energy market. This work proposes a virtual community (VC) based P2P energy trading for buildings equipped with RES and flexible loads. A modified Nash bargaining solution is used for fair incentive distribution in the cooperative game. In this work, the equilibrium strategies of all players are obtained using a privacy-preserving decentralised approach. Shareable battery energy storage is considered in each VC, and Hong's 2m point estimation method is used to cope with the uncertainties associated with RESs.
A Study on Deep Learning Models for Automatic Species Identification from Novel Leather Images

This paper introduces a new set of large-scale leather image data. Unlike the existing dataset, it comprises 7600 images with varied non-ideal behavior. The aim is to develop a versatile identification model that can efficiently determine the species from complex/practically challenging leather images. Hence, this research proposed to train a convolutional neural network (CNN) on a large-scale dataset. It performs a comparative study on five CNNs: ResNet50, MobileNet, DenseNet201, InceptionNetV3, and InceptionResNetV2. The analysis reveals that InceptionNetV3 outperforms with 98.23% accuracy and 1.71% negligible error. It also evaluates the generalization power of the trained InceptionNetV3 on the existing small-scale dataset (1200 images). Although the model is trained on non-ideal leather images, it results in 94.07% accuracy. However, learning from present and existing datasets improves the prediction rate to 98.5% accuracy. Thus, this work efficiently models a deeper CNN to predict species from leather images with ideal and non-ideal behavior. Contrary to the previous machine learning-based species prediction methods, the present deep learning method designs a fully-automated model with accurate and robust results.
On-board Converter for Electric Vehicle Charging using LCLC Resonant Topology

Due to their high efficiency, high power density, and soft switching characteristics, LLC-based AC-DC resonant converters are a great choice for EV chargers. Adding a capacitor across the magnetizing inductance of the LLC resonant architecture (LCLC configuration) enhances efficiency and reduces the need for a larger series inductor. The output DC voltage of the converter is generally regulated using switching frequency control. However, the power factor of the converter varies significantly with the switching frequency. As a result, any fluctuations in load may cause the converter to operate at a lower power factor. This paper proposes a single-stage topology based on the LCLC resonant structure. Zero voltage switching (ZVS) of the IGBTs used in the converter is ensured by the LCLC resonant configuration. Converter have a power factor correction (PFC) stage on the front of the converter to achieve natural power factor correction. Since the PFC stage and the resonant stage are controlled by the same switches, the converter is smaller and less costly. Simulations in MATLAB/Simulink are used to validate the topology.
A Model Based Comparison on the Efficiency of Electric Vehicles to Conventional Vehicles

A model based comparison between an electric vehicle (EV) and a conventional vehicle with an internal combustion engine (ICEV) is presented in this paper. The study is performed by use of “Advanced Vehicle Simulator” (ADVISOR) in MATLAB environment. Both types of vehicles are subjected to a New European Driving Cycle (NEDC) procedure and to a combined “city-highway” fuel economy test procedure. As these test procedures are US based ones, it is not explicitly the case in terms of Bulgaria's conditions. Thus, in addition, a comparative fuel economy evaluation is presented. Based on obtained results from ADVISOR, it can be seen that NEDC's nature of fast and aggressive style of driving does not enhance EV's inherent advantages and the necessity of charging infrastructure is vividly exhibited. Emissions' results of simulated ICEV exceed the normative values of hydrocarbons and of particle matters set by US' Environmental Protection Agency (EPA) and Europe's EURO 6 standards. However, the simulation done by ADVISOR does not account for emissions reduction techniques. In addition, the fuel consumption and fuel economy evaluation shows that to travel by EV is less expensive. This, however, is a direct comparison of “tank-to-wheel” efficiency of EVs and ICEVs. Whenever “well-to-wheel” efficiency is considered the efficiency with which the primary source of energy to respective vehicle is produced has to be included. That is to say the “well-to-wheel” efficiency is the product of the “tank-to-wheel” efficiency and the efficiency which is known as “well-to-tank”. Then, if an EV is charged with electricity generated by coal-fired power plants its “well-to-wheel” efficiency is similar to the one of ICEVs. Furthermore, albeit ICE's inherent lower efficiency, the accessibility of ICEVs is still widely needed as there are many uncertainties regarding EV's charging purely by spatial point of view at residential complexes. Thus, taking everything below into considerations a complete extinction of any type vehicle seems unlikely and it is more likely that in the future a vehicle diversification comprised of ICEVs, EVs and hybrids will be the standard.
Manipulator Collision Avoidance Path Generation Algorithm Considering Predicted Human Path

In a collaborative robot system, humans and robots can share a work area to improve work efficiency. However, since there is a high risk of human injury when a collision occurs within the workspace, robots must be able to avoid collisions with humans while performing tasks. Existing studies generate a path by assuming a constant speed for a moving person, resulting in a straight line or arc that cannot reflect complex human motor characteristics. In this paper, we propose an algorithm that predicts the path of human motion with a network using a Gated Recurrent Unit (GRU) and generates an Artificial Potential Field (APF) that reflects the predicted path while considering the future collision potential. Within the created APF, we generate a collision-avoidance path that reaches the destination quickly.
Microwave-Based Remote Bio-Sensing behind Walls

In this paper a novel method for microwave based remote sensing of vital signs behind walls is presented. The method is based on temporal spatial analysis of back scattered microwave signals. The use of non-optical electromagnetic radiation enables monitoring from larger distances and behind objects in contrast to similar concepts in optics. Further, such use of non-optical radiation omits the need for direct line of sight between the monitoring system and the target and enables monitoring through walls or other barriers. Micro-vibrations due to breathing and heart pulsation affect the reflection of microwaves and cause the self-interference random patterns (i.e. speckle patterns) to vary in time and space. By using this approach, the temporal change of the speckle patterns due to changes in vital signs can be tracked behind walls. In this paper we present a system in WiFi frequencies band consisting of two radio-frequency (RF) transmitters and either one or three RF receivers to detect single or multiple breathing or non-breathing human subjects, behind different type of walls. The results showed that the system was able to distinguish between breathing states, to determine the vibration frequency of heartbeats, and to distinguish between subjects’ vital signs in few cases. In low SNR cases statistical analysis was applied to extract breathing and heartbeats frequencies, by a sweep of 20 different transmitted signals in sequence. This is the first step towards real time remote detection of human vital signs using RF speckle patterns.
Detection and Quantification of Nocardia crassostreae, an Emerging Pathogen, in Mytilus galloprovincialis in the Mediterranean Sea Using Droplet Digital PCR

Nocardia crassostreae is a novel pathogen responsible for infections in oysters (Crassostrea gigas) and mussels (Mytilus galloprovincialis). N. crassostreae is also responsible for nocardiosis both in immunocompetent and immunocompromised patients. We investigated N. crassostreae DNA in mussels grown in marine sites of the Mediterranean Sea in the Campania Region. We examined 185 mussel pooled samples by droplet digital PCR (ddPCR) and real-time quantitative PCR (qPCR), each pool composed of 10 mussels and 149 individual mussels. ddPCR detected N. crassostreae DNA in 48 mussel pooled samples and in 23 individual mussel samples. qPCR detected N. crassostreae DNA in six pooled samples and six individual mussel samples. The two molecular assays for the detection of N. crassostreae DNA showed significant differences both in the pooled and in individual samples. Our study demonstrated that ddPCR outperformed real-time qPCR for N. crassostreae DNA detection, thus confirming that ddPCR technology can identify the pathogens in many infectious diseases with high sensitivity and specificity. Furthermore, in individual mussels showing histological lesions due to N. crassostreae, the lowest copy number/microliter detected by ddPCR of this pathogen was 0.3, which suggests that this dose could be enough to cause infections of N. crassostreae in mussels.
Accurate Calculation of Induced Voltages and Currents in Fibre Optic Units Integrated in Three-Core Armoured Cables

Cables used in offshore wind farms are usually three-core (3C) with metallic armour, including one or more integrated Fibre Optic Cable (FOC) units for data transmission. Voltages and currents induced in the metallic part of FOC are crucial for the integrity, not only of the FOC unit itself, but also of the entire 3C armoured cable. To compute them, the cable series impedance at power frequency is necessary. The various metallic components of the cable, i.e., the power cores, FOC and armour, are mutually coupled and this is expected to affect the derived figures. The existing methods do not account for the actual three-dimensional (3D) effects, stemming from the complicated cable geometry. Twisting effects may be included in a two-and-a-half-dimensional (2.5D) fashion, which, however, does not consider the magnetic field component driven along the armour wires. This paper proposes a 3D method to fully consider the actual coupling between the various cable components and compute induced voltages and currents at the FOC, along the cable route. The proposed method is evaluated against existing approaches and, finally, is used to study several operating scenarios.
Classification of Alzheimer’s Disease Using 2D/3D Convolutional Neural Networks

The neurodegenerative brain disorder known as Alzheimer’s disease (AD) is brought on by the buildup of amyloid proteins, the formation of plaques, and the loss of neurons. Parkinson’s disease (PD), another prevalent subtype of dementia like AD, is characterized by the loss of dopaminergic neurons in the midbrain region known as the substantia nigra pars compacta. Both AD and PD aim to reduce healthcare expenses by reducing the world’s ageing population. Therefore, techniques that aid in the early detection of these disorders are required. In the area of medical imaging, where there is typically a limited amount of data that can be used for training, these drawbacks make it difficult to employ the most recent deep learning methods. Humans have difficulty distinguishing between early forms of Alzheimer’s disease and correctly diagnosing them. Convolutional networks can be used to classify human MRI scans, as this paper demonstrates. Mild Cognitive Impairment, Alzheimer’s Disease, and Clinically Normal are the three categories. Finally, a functional model constructed with three-dimensional convolutional layers that performs well across the three classes is presented. The assessment results show that the proposed strategy accomplishes the model exhibition on Promotion conclusion by normal of 82%exactness.
Bayesian Optimized Ensemble Decision Tree models for MT-VSC-HVDC Transmission Line Protection

Over the last few decades, the High Voltage Direct Current (HVDC) technology has experienced significant growth. HVDC grid technologies are increasingly being employed for strengthening transmission systems and improving connectivity. In cases of long-range and bulk power transmission, HVDC systems have proven to be an attractive option compared to HVAC systems. HVDC grids exhibit reduced power loss and almost negligible lines reactive power. Faults must be fixed promptly, regardless of any challenges. This study presents a fault detection and classification method based on Bayesian optimized decision tree classifiers for an MT-VSC-HVDC transmission system. The primary objective of this research is to extract the DC voltage and current signal through the relays installed in the HVDC network. Afterward, fourteen features are formulated using these signals for the experimentation. Based on these features, Bayesian-optimized decision tree classifier is used to identify and differentiate the faults events. The proposed approach enables rapid identification, faster detection, and fixation of both internal and external faults. The proposed approach is rigorously assessed for various probable fault circumstances simulated with varying transmission system operating parameters. This experimental approach considerably reduces the complexity and time required to identify faults at various points on the HVDC transmission grids with high precision.
Inventory of Timber in Finnish Residential Houses

To better understand the reuse and recycling potential of timber available within buildings, this paper creates an inventory of timber used in the construction of building typologies in Finland by analysing the technical documents of twenty typical houses and suggesting timber components that might be the best for reuse and recycling. The analysis showed that Finnish buildings contain timbers that could be reused and recycled. This highlights an opportunity and a need for the development of guidance on the principles and strategies for design for deconstruction and reuse. It was also determined that due to the design of these buildings, it is currently uneconomic to extract the timber components intact, even if they could be reused or repurposed. By making only minor changes to existing design, construction, and demolition practices, significant possibilities for reusing and recycling may be generated, adding value to timber, and reducing the demand for virgin wood.
Impulse Vector: A Basic Mathematical Tool to Design and Analyze Flexible Robots for Removing Residual Vibrations

Flexible robots always accompany residual vibrations during work operations, which should be quickly eliminated to improve productivity. Traditionally, input shapers were designed and analyzed to remove residual vibrations in flexible robots differently for undamped and underdamped systems, and for positive and negative input shapers. This paper presents a unified approach to design and analyze input shapers for undamped and underdamped systems and for positive and negative input shapers using a basic mathematical tool, impulse vector, recently proposed by the first author. The usefulness and effectiveness of the impulse vector method in designing and analyzing input shapers is demonstrated through simulation studies for a second-order system and verified experimentally using a flexible up-and-down device.
Design & Implementation of Novel AI Based Hand Gestured Smart Wheelchair

Disability is a significant health concern in India, according to research of disabled people carried by NSSO. Cerebral palsy, dwarfism, muscular dystrophy, as well as other conditions can cause disability, although the majority of these disorders are acquired at birth or accidentally. The wheelchairs are the ideal mobility support for those with physical impairments. The purpose of this proposed work is to create a programmable wheelchair which would aid individuals with physical impairments in their daily activities and fulfilment of some routine life. Individuals who have physical abnormalities lack the ability to move around just like regular people. They are indeed forced to rely on others for all of their daily necessities. They feel discouraged due to their dependence on others. An AI-based hand gestured smart wheelchair is a type of wheelchair that uses artificial intelligence technology to interpret hand gestures and translate them into wheelchair movements. This type of wheelchair is designed to provide greater mobility and independence to people with disabilities, particularly those who may have limited mobility in their arms or hands. The system works by using sensors and cameras to detect and interpret the user’s hand gestures, which are then transmitted to the wheelchair’s control system. The control system processes these gestures and translates them into specific commands, such as moving the wheelchair forward, turning left or right, or stopping. One of the key benefits of an AI-based hand gestured smart wheelchair is that it allows for a more intuitive and natural control mechanism, as users can simply gesture in the direction, they want to go rather than having to manipulate joysticks or other controls. Additionally, this type of wheelchair can be adapted to individual users, allowing them to customize their gestures to better suit their needs.
Optical Interconnection for Datacenters: To Switch or Not to Switch

Optical interconnection is seen as a promising solution to alleviate the congestion problems inside datacenters. Previously reported studies focus solely on optical circuit switching to establish connections. In this work we compare performance of these previous schemes with a model where static optical channels are deployed. A switch architecture using only demultiplexers and couplers is proposed for datacenter optical interconnection, and its performance is compared with that of WSS based switch. Results obtained by simulation show that the performance of the proposed simpler interconnect approaches that of the WSS-based one when datacenter traffic profile follow normal random distributions.
Creating a Large-scale Memory Error IoT Botnet Using NS3DockerEmulator

DDoSim, a simulation testbed for mimicking real-world, large-scale botnet DDoS attacks, is presented. DDoSim offers various capabilities, including running user-specified software, testing botnet-recruitment exploits, and measuring the severity of resulting DDoS attacks. DDoSim leverages NS3DockerEmulator's Docker and NS-3 integration to load Docker containers with actual binaries and connect them over a simulated NS-3 network. DDoSim is validated through a comparison with results from real hardware experiments. This paper focuses on the results of an experiment series concerning deploying a memory error botnet on IoT devices. Unlike the Mirai attack, which relies on default credentials, these experiments exploit memory error vulnerabilities to access IoT devices. DDoSim also implements realistic IoT churn, reflecting dynamic network conditions in real-world IoT environments. The results reveal that memory error vulnerabilities enable botnet recruitment, while network conditions, attack size, and duration all have a proportional impact on target servers. DDoSim is publicly available for researchers' use.
A Practical Teaching Tool for Optical Camera Communications

Optical camera communication (OCC) is a rapidly growing research field within optical wireless communication (OWC) technologies that uses light-emitting diodes and cameras to provide wireless communication between devices in the visible light spectrum. Its growth has been propelled by the extensive availability of digital cameras in the consumer market in the form of smartphones, navigation cameras, surveillance systems, and others. Therefore, training new students with the necessary skills to apply and develop this technology is essential. This paper presents a practical teaching tool for the field of OCC aimed at students in undergraduate and postgraduate programs in electronics, telecommunications, and computer engineering. The tool is an interactive demonstrator of the OCC technology based on open-source hardware and software that is easy to reproduce and adjust. The workflow for presenting the tool covers the basic principles of OCC, including modulation techniques and image acquisition and processing techniques. As a core part of the class, students are given hands-on experience with OCC equipment to test the technology in a didactic way. This paper provides a resource for educators interested in adopting experimental learning into their courses on OWC.
DARPA: Combating Asymmetric Dark UI Patterns on Android with Run-time View Decorator

It has been extensively discussed that online services, such as shopping websites, may exploit dark user interface (UI) patterns to mislead users into performing unwanted and even harmful activities on the UI, e.g., subscribing to recurring purchases unknowingly. Most recently, the growing popularity of mobile platforms has led to an ever-extending reach of dark UI patterns in mobile apps, leading to security and privacy risks to end users. A systematic study of such patterns, including how to detect and mitigate them on mobile platforms, unfortunately, has not been conducted. In this paper, we fill the research gap by investigating the dark UI patterns in mobile apps. Specifically, we show the prevalence of the asymmetric dark UI patterns (AUI) in real-world apps, and reveal their risks by characterizing the AUI (e.g., subjects, hosts, and patterns). Then, through user studies, we demonstrate the demand for effective solutions to mitigate the potential risks of AUI. To meet the needs, we propose DARPA - an end-to-end and generic CV-based solution to identify AUIs at run-time and mitigate the risks by highlighting the AUIs with run-time UI decoration. Our evaluation shows that DARPA is highly accurate and introduces negligible overhead. Additionally, running DARPA does not require any modifications to the apps being analyzed and to the operating system.