Miracle-dz/UniFAR-FTUs huggingface dataset:
Acne Skin Disease Detection Using Convolutional Neural Network Model

Skin issues have been a colossal concern among ladies as well as men. This paper proposes a strategy for recognizing and classifying skin breaks out on the skin using CNN pre-trained model VGG19. Now days, these pretrained models have accomplished close or indeed way better execution than human creatures based on clinical pictures. A dataset of pictures of skin break out on the skin have been collected and pre-processed to get guarantee that the skin breakout is obvious or not. A CNN VGG19 model shows that the points built and trained on the datasets employ an appropriate optimization calculation. The model's execution is assessed on a testing set and the hyperparameters that we got from it and the engineering of the demonstration are fine-tuned to move forward with better execution. This research paper presents the image acquisition methods, datasets, deep learning architectures, and frameworks used in skin disease diagnosis. The proposed strategy appears promising comes about for identifying and classifying skin breaks out on the skin and has potential applications in dermatology clinics and skin care item advancement.
Enhancing Irrigation Efficiency with AI-Based Instinctive Irrigation System (IIS) in Wireless Sensor Networks

An AI-assisted intuitive irrigation system built on Arduino and Zigbee that operates within a wireless sensor network is an example of an innovative application of technology in the field of agriculture. This technology enhances the process of watering in real time by utilizing sensors, microcontrollers, a wireless connection, and algorithmic programs that are powered by artificial intelligence. An open-source microcontroller platform serves as the “brains” of the system, receiving data from a wide variety of sensors and using that information to direct the operation of the irrigation system. The Arduino and the nodes that contain the sensors are connected by means of a bridge that is formed by the wireless connection module. In the field, numerous kinds of sensors are used to measure a wide variety of properties, including soil moisture, air temperature, relative humidity, and light intensity, to name just a few of these factors. The artificial intelligence program receives data from the sensors, and it uses that data to make judicious judgments regarding the amount of water to apply to the plants. A solenoid valve and a pump are only two examples of the types of gear that are utilized in irrigation systems to regulate the flow of water. They can be powered on using the Arduino if desired. When constructing wireless sensor networks, it is crucial to keep energy saving in mind as one of the important issues to consider about. ZigBee is a significant advancement in technology that contributes to the resolution of this complex problem. Through the utilization of wireless sensor networks and monitoring systems, the primary objective of this research is to advance our comprehension of data transmission via wireless media. The development of a fully automated irrigation system suitable for usage in Pennsylvania is the primary objective of this research. The regulation of temperature, the amount of moisture in the soil, and the level of humidity in the air are the three key parameters that will be of assistance in the achievement of this objective.
Appliance V-I Trajectory Detection for NILM based on Conditional Autoencoder

Non-intrusive load monitoring (NILM) analyzes the terminal voltage and total current to offer comprehensive appliance-specific consumption data. Many state-of-the-art NILM models make a crucial assumption that recognized devices within the training set are responsible for triggering switching events. However, continuous addition of new devices diminishes the practical efficacy of existing methods for load monitoring. In this manuscript, we introduce a novel approach based on the Conditional Variational Auto-Encoder (CVAE) to address the challenge of classifying familiar appliances and identifying unfamiliar ones by leveraging V-I trajectory characteristics. In our proposed approach, during the training phase, we promote the alignment of capsule features belonging to the same familiar class with a predefined Gaussian distribution, where each class has its own distribution. To achieve this, we adopt the variational autoencoder framework and utilize a collection of Gaussian priors as an estimate for the following distribution. By employing this approach, we can regulate the compactness of features belonging to the same class around the mean of the corresponding Gaussian distributions. This enables us to control the classifier’s capability to identify samples from unfamiliar classes. Testing findings on the public dataset illustrate the efficiency of our approach.
Secure Authentication Protocols For Internet Of Things (Iot) Devices

The ability to give typical gadgets an extra technique for distinguishing proof and correspondence is known as the Internet of Things (IoT). The scope of IoT application spaces is extremely wide and incorporates things like wearables, savvy urban areas, shrewd homes, and e-wellbeing. Accordingly, many billions or perhaps many billions of gadgets will be connected together. These shrewd gadgets will actually want to assemble information, process it, and, surprisingly, come to decisions all alone. In these circumstances, security is totally important, and authentication specifically is of extraordinary significance given the potential mischief that could result from a maverick unauthenticated gadget in an IoT framework. IoT has as of late arisen as the state-of-the-art innovation that is intensely affecting business, medical services, and military applications. Remote correspondences are especially powerless to security takes a chance since anything associated with the internet is an objective for programmers and a possible wellspring of cyberattacks. Numerous IoT issues are presenting security gambles and forestalling Start to finish encryption during information transmission. Most of IoT gadgets presently being used don't have further developed arrangements or security systems, which makes them helpless against programmer assaults.
Seasonality of submesoscale dynamics in the Kuroshio Extension

Recent studies show that the vigorous seasonal cycle of the mixed layer modulates upper ocean submesoscale turbulence. Here we provide model‐based evidence that the seasonally changing upper ocean stratification in the Kuroshio Extension also modulates submesoscale (here 10–100 km) inertia‐gravity waves. Summertime restratification weakens submesoscale turbulence but enhances inertia‐gravity waves near the surface. Thus, submesoscale turbulence and inertia‐gravity waves undergo vigorous out‐of‐phase seasonal cycles. These results imply a strong seasonal modulation of the accuracy of geostrophic velocity diagnosed from submesoscale sea surface height delivered by the Surface Water and Ocean Topography satellite mission.
An Optimized CNN and Transfer Learning Approach for Pneumonia Detection

The most prevalent lung condition affecting people worldwide is pneumonia. Diagnosing pneumonia only from a chest X-ray (CXR) might be challenging. The study aims to simplify pneumonia infection detection for experts and novices alike. We suggest a deep learning (DL) approach for identifying pneumonia using transfer learning (TL). A residual network previously trained on ImageNet is used in the proposed method to recover image features, which is then fed into a CNN classifier for prediction. The performance of the suggested model displays the ability to diagnose pneumonia, showing that the ResNet152V2 model could effectively distinguish between normal and pneumonia X-rays, reducing the burden of radiologists. Using residual learning (ResNet152V2), a model that can determine whether or not a person has pneumonia is trained. Here, the outputs of five different models are compared. The model is executed or trained using GPU through the Google colab. Compared to the CPU's performance, the GPU can considerably speed up the detecting process.
Point Cloud Map creation based on data Fusion of Lidar and IMU

Road system is a complex cooperative system composed of people, vehicles and roads, so the deep integration of self-driving and intelligent transportation will be inevitable. Self-driving will enter the intelligent era of vehicle-road coordination. As an important link between self-driving car and road system, HAD map is an important foundation of vehicle-road coordination. In this paper, a fusion method based on optimization is proposed. It fuses IMU data and lidar data to estimate the vehicle position and pose in real time, also builds the surrounding point cloud map simultaneously. By employing IMU, the amount of motion during lidar data acquisition is calculated, and a frame of lidar data is converted to the same coordinate system, so as to eliminate the distortion of point cloud. Meanwhile, IMU does pre-integration to construct the constraint of pre-integrated measurement. The lidar eliminates the point cloud distortion, applies the inter-frame matching, obtains the real-time odometry data and constructs the lidar odometry constraint. The key frame is selected according to the rules, the loop detection based on feature is carried out, which is used to construct loop closure constraint. All the constraints are solved in an optimization framework. Then the exact position and pose after fusion are applied to get the modified point cloud map. In this paper, simulation and experiment results have been compared with the classical algorithm by employing the opened source data set and our experimental platform. The results show that this algorithm can reduce the drift of odometry, improve the accuracy of mapping and reduce map ghosting.
Leveraging Computational Vision Techniques for Weed Detection in Crops

The precise utilization of herbicides can result in a significant decrease in their application levels within agricultural fields. The detection system plays a vital role in smart sprayers, enabling the identification of target weeds and facilitating informed spraying decisions. The development of near-ground image capture and processing techniques for weed detection in vegetable crops is the main topic of this research. The first step in the image processing involves detecting green plants, which helps to minimize irrelevant information and streamline the weed detection process. Subsequently, the study has emphasized vegetation segmentation through the application of morphological filtering and object labeling techniques, enabling weed detection based on a threshold derived from the area of detection. The proposed methods incorporate color and area analysis techniques, effectively distinguishing between crops, weeds, and soil.
Prediction Pathological Structure of Computed Tomographic Colonography Polyps via Machine Learning

Computed tomographic colonography (CTC) has shown to be an effective means of detecting the precursor polyps leading to colorectal cancer (CRC). Accurately diagnosing these polyps can assist the overall treatment plan and follow-up examination determination to prevent CRC. In this work, we examine a machine learning approach to predicting the three main pathological structure type of polyps, comparing hyperplastic and serrated adenomas (HS), tubular adenomas (TA), and advanced neoplasms (AN). We find that imaging textures can differentiate any two pathological groups with AUC scores ranging from 0.624-0.749 for polyps less than 10mm in size and 0.731-0.887 for polyps greater than 10mm. Classification of all three pathology groups simultaneously shows a sensitivity of 61.6% for identifying the highest risk group of advanced neoplasms.
Low-Complexity Control for Uncertain Steer-by-Wire Systems With Input Nonlinearity and Prescribed Tracking Performance

This paper investigates the prescribed tracking performance control problem for Steer-by-Wire (SbW) systems subject to model uncertainty, (i.e., unknown nonlinear function and control coefficient), and input nonlinearity including dead-zone and actuator faults. A low-complexity prescribed tracking performance control is proposed in which actuator fault and dead-zone are handled by using novel error transformation and robust control techniques. More importantly, the time derivative of the virtual control law and a prior upper bounds of initial states are no longer required in the control design, such that the application exibility of the controller can be improved. Theoretical analysis shows that the tracking error can be constrained to prescribed sets within a presetting time. Simulations are given to verify the rationality of proposed methods.
Lane-keeping Control of Autonomous Vehicles Using Reinforcement Learning and Predictive Safety Filter *

The urgent need for both safety and high-performance in the motion planning and decision-making system of the autonomous vehicle presents a significant challenge for learning-based technologies. In this paper, we introduces a lane-keeping control structure that combines reinforcement learning and a predictive safety filter to ensure the vehicle’s safety motion. A vehicle agent under the racetrack environment is trained by the soft actor critic algorithm, to achieve high-performance continuous lane-keeping motion. Additionally, we establish the safety task for lane-keeping, which results in a learning-based safety filter, to avoid violation of driving on the outer lane. Through simulations, we demonstrate the effectiveness of our method in ensuring the safe and trustworthy control of autonomous vehicle lane-keeping.
Advancing Parkinson's Disease Detection: An Empirical Evaluation of Machine Learning Models Based on Speech Analysis

Parkinson's disease (PD) is a neurodegenerative disorder that adversely impacts a considerable portion of the population, especially senior citizens. Currently, the basis of PD diagnosis is clinical assessments, which can be costly, time-consuming, and invasive, that can be subjective or prone to errors. Dysarthria, a prevalent condition characterized by slow and distorted speech, often coexists with PD, presenting an opportunity to leverage speech features for diagnostic purposes. In this research paper, the altered speech features caused by Dysarthria are harnessed to train a diverse set of cutting-edge machine learning algorithms, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests, and Decision Trees. A thorough examination of the latest advancements in the diagnosis of Parkinson's disease (PD) using speech analysis is provided. Additionally, a comparative analysis between the performance of a newly introduced classifier, HyperTab, and existing models is conducted. The findings reveal the remarkable promise of machine learning in speech-based PD diagnosis, paving the way for developing a cost-effective tool for accelerating disease detection. Moreover, this contribution is crucial in providing vital support to those areas with limited access to specialized medical facilities, significantly improving the quality of life and outcomes for PD patients in rural regions.
The Lunar OutpOst Neutron Spectrometer (LOONS)

We present a new detector concept, the Lunar OutpOst Neutron Spectrometer (LOONS), designed for the measurement of energetic (~0.4–150 MeV) neutrons of solar, terrestrial and lunar origin, as part of a low-Moon orbit or lunar-lander opportunity. Neutrons represent an important space radiation component for multiple aspects. They are produced in the interactions of cosmic rays (CRs) with the Earth’s atmosphere and spacecraft materials, posing a considerable hazard for satellite electronics and air/spacecraft crews. The detection and monitoring of neutrons originated in the CR collisions with the lunar surface is particularly important for near-future manned missions to the Moon, since they constitute a significant component of the lunar radiation environment and, at the same time, they can be used to probe the crust composition (e.g., water content). Finally, the measurement of neutrons produced in solar flares provides a unique opportunity to investigate particle acceleration mechanisms at the Sun, complementing the observations based on γ-rays and charged CR components. LOONS is based on state-of-the-art techniques for neutron detection using segmented organic scintillators coupled to silicon photomultipliers (SiPMs), and subsequently processed with modern ASICs. In this work we describe the detector development and performance, and the future plans to integrate a flight-ready instrument.
Modeling and Control of Modular Multilevel Converters Under Asymmetric Grid Conditions

The modular multilevel converters (MMCs) are attractive topologies for voltage sourced converter based high voltage direct current (VSC-HVDC) transmission system. Asymmetric grid conditions are common encountered in VSC-HVDC system. The resulting voltage sag and power fluctuation from such conditions amplify current stresses on devices, posing a threat to the safe and reliable operation of MMC. This article establishes an equivalent model for MMC under asymmetric conditions, based on which, a decoupling control method for the MMC under asymmetrical operating conditions is proposed.
Tumor DNA hypomethylation of LINE‑1 is associated with low tumor grade of breast cancer in Tunisian patients

DNA hypomethylation of long interspersed repetitive DNA retrotransposon (LINE-1) and Alu repeats elements of short interspersed elements family (SINEs) is an early event in carcinogenesis that causes transcriptional activation and leads to chromosomal instability. In the current study, DNA methylation levels of LINE-1 and Alu repeats were analyzed in tumoral tissues of invasive breast cancer in a Tunisian cohort and its association with the clinicopathological features of patients was defined. DNA methylation of LINE‑1 and Alu repeats were analyzed using pyrosequencing in 61 invasive breast cancers. Median values observed for DNA methylation of LINE-1 and Alu repeats were considered as the cut-off (59.81 and 18.49%, respectively). The results of the current study demonstrated a positive correlation between DNA methylation levels of LINE-1 and Alu repeats (rho=0.284; P<0.03). DNA hypomethylation of LINE-1 was also indicated to be associated with low grade (P=0.023). To the best of our knowledge, the current study is the first study regarding DNA methylation of LINE-1 and Alu repeats element in breast cancer of the Tunisian population. The results of the current study suggest that, since hypomethylation of LINE-1 is associated with low grade, it could be used as a biomarker for prognosis for patients with breast cancer.
Anticancer potential of garlic bioactive constituents: Allicin, Z-ajoene, and organosulfur compounds

Cancer, a formidable disease with a significant mortality rate, continues to claim the lives of thousands of individuals annually in contemporary society. Conventional cancer therapies are notorious for their substantial adverse effects and lack of specificity. Within the context of neoplastic development, cancer hallmarks represent fundamental biological properties that cancer cells progressively acquire. A promising approach for combating cancer involves the simultaneous targeting of multiple cancer hallmarks. Plant-derived natural compounds stand out as a promising reservoir for the development of novel and more efficacious anticancer therapeutics due to their structural diversity and minimal toxicity profiles
Certifiable Safety Techniques in Mobile Robots as Tools for Precise and Assistive AI Regulation

The widespread application of artificial intelligence (AI) systems across industries such as healthcare, finance, and technology has surged, raising complex legal and regulatory challenges. Adapting to and addressing bias, discrimination, and accuracy issues as technology progresses rapidly proves difficult. Deploying autonomous vehicles, delivery robots, and other physically embodied AI agents in pedestrian environments adds new layers of complexity for regulation. This paper examines recent trends in regulating AI and autonomous vehicles and current research in explainability, validation, and statistical verification of learning-based autonomous systems to emphasize the importance of diving deep into engineering techniques to produce well-informed and nuanced regulatory approaches and encourage interdisciplinary work. Using a collision avoidance example, safety and validation methods are explored to bolster confidence in robot deployment and suggest a more context-specific approach to regulation. These tools can ensure public acceptance, performance accountability, third-party assessment, and adaptive regulation. We highlight in the example the ongoing challenge of safe interactions with dynamic agents and the need for regulatory guidance in balancing innovation and public safety. We add to the growing calls for interdisciplinary discussions among roboticists, computer scientists, legal professionals, policymakers, and ethics experts to generate informed regulations that promote the development of AI technologies that operate safely under uncertainty while supporting innovation.
Convergence of artiﬁcial intelligence research in healthcare: Trends and approaches

A value-based strategy relies on the implementation of a patient-centered care system that will directly beneﬁt patient care outcomes and reduce costs of care. This paper identiﬁes the trends and approaches to artiﬁcial intelligence (AI) research in healthcare. The convergence of multiple disciplines in the conduct of healthcare research requires partnerships to be established among academic scholars, healthcare practitioners, and industrial experts in software design and data science. This collaborative work will greatly enhance the formulation of theoretically relevant frameworks to guide empirical research and application, particularly relevant in the search for causal mechanisms to reduce costly and avoidable hospital readmissions for chronic conditions. An example of implementing patient-centered care at the community level is presented and entails the inﬂuence of the context, design, process, performance and outcomes on personal and population health, employing AI research and informational technology.
TOURIST´S MOTIVATIONS TO VISIT THE WESTERN REGION OF PORTUGAL

The Western Region, located in the Central Region of Portugal, has a vast and rich natural and cultural heritage allowing a wide range of tourist experiences. Consequently, the aim of this study is to analyse the tourist interests and motivations that lead tourists to visit the Western Region of mainland Portugal. In 2021, 355 individuals were surveyed through a questionnaire survey, most - ly living in mainland Portugal. The results reveal that the preferences of respondents for the types of tourism they most like or would like to undergo in the Western Region are Sun and Sea Tourism, Leisure Tourism, Cultural Tourism, Adventure and Nature Tourism, and Gastronomic Tourism. This study may contribute to a better understanding of the tourists’ motivations to visit the Western Re - gion, and may be an important contribution to the tourism management entities, in order for them to enhance and/or improve their offers in this region of Portugal.