Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Gamelan's Sound Manipulation Toward the Mood of Calm on Teenagers

This study examines the effects of Javanese gamelan instrumental music on inducing calm. Music has been used for ages for hypnosis or “mind washing. ” Based on instinctive reaction and conditioned reflex, the physiological psychological model employing music stimuli has spread the belief that music can affect self-control. Hypnotherapy's music-induced relaxation helps “physiological psychology” as a reflex response to sound. From hypnosis induced by musical instrument sounds and genres to conditioned reflexes that impact physiology through conscious thought. Mesmer used the managed universe hypothesis to heal patients in the 1770s. Thus, “magnetic sleep” may have inspired hypnosis for relaxation. Magnetism is a “sympathetic vibration” that is communicable, disseminated, and strengthened through sound. Western musical instruments dominate, whereas Eastern ethnic instruments like gamelan are rarely utilized. This study used a quasi-experimental, one-post-test design on the same group. The study included teens without hearing or health issues. N = 20 individuals passively listened to a Javanese gamelan composition based on gender, gambang, and gong for 5.5 minutes, then completed self-report questionnaires and integrated group discussions. 67.5% of subjects did not feel drowsy, 81.3% believed the listening duration was not long, and 86% were satisfied with the music intervention. 32.5% were a little tired, 18.7% thought it was too long, and 14.5% were satisfied. However, several individuals experienced Bluetooth headset signal interference while listening, which led to these results. Thus, music relaxes all subjects, proving the idea.
An Organized Examination of Image Processing for Detection and Diagnosis of Plant Diseases

Agriculture is one of the most important and necessary sources of national wealth. Plant and crop diseases are one of the major factors that affect output quality and quantity. Lack of quality and quantity in food production creates a negative impact on the economy as well. Hence, it is crucial to identify plant diseases. Studies reveal a wide range of plant diseases occurring in different parts of the world. Various components of plants exhibit symptoms of plant diseases especially, the leaves. Extensive research is conducted across the globe and many horticulture specialists in liaison with technological experts apply computer vision and soft computing methods to automatically detect plant diseases from leaf photos. This article provides a summary of several aspects of such studies, along with their benefits and drawbacks. The authors addresses common infections as well as the state of such detection systems’ research at various stages. The current feature extraction methods are examined in order to determine which one seems to function best across a variety of crop categories. This study will aid researchers in their understanding of the use of computer vision for identifying and categorizing plant diseases.
Control of Hybrid PV System with Battery Storage

A control scheme for a hybrid solar inverter is proposed which can operate the inverter in both ‘on grid’ and ‘off grid’ modes and have a seamless transfer between the two methods. The input for this inverter is obtained from multiple strings of PV arrays. An individual maximum power point tracking (MPPT) for each string is implemented to optimize the power extraction from the PV module. A battery bank controlled by a charge-discharge regulator is interfaced with the DC bus of the inverter. In grid-connected mode, this battery bank can be charged from the grid as well as from the PV panel depending on user preference. In off-grid mode, this battery compensates for the mismatch between the generation from the PV panel and the load. The proposed control scheme for the hybrid solar inverter is validated through extensive simulation studies.
Artificial Intelligence Technology-Embedded Learning: Rethinking Pedagogy for Digital Age

Artificial Intelligence Digital pedagogy is a novel approach to the teaching and learning process, which is being used in education. This study explains as how these approaches influence student learning. To foster a culture of usefulness and learning, a new pedagogies and assessment tools are explored for the digital age. This study aims to explore new online platforms and use them in classrooms to maximize the learning plan and establish sustainable development in the current situation and the future. If to talk about the present educational situation in India, there is a considerable gap between theory and actual work. But technology is a tool that can bridge these gaps. Therefore, this article will explore these tools to fill in these gaps.
Research on the identification of Tibetan herbal medicine based on STM-A Net

In order to implement the accurate, non-destructive, and fast classification of Tibetan medicinal materials, taking the current images of various Tibetan medicinal materials as the research object, an STM-Attention module of plug-and-play is proposed based on deep learning. In this paper, the STM-Attention modules are stacked according to the ResNet method, forming a new classification backbone network structure-STM-A Net. At the same time, the network has the advantages of small parameters and computation, and can better extract features under the condition of ensuring speed. Experimental results show that under the same conditions, the network achieves a balance between parameter quantity and accuracy, and the accuracy rate reaches 97.18% on the test set of Tibetan medicinal materials.
A Hybrid Deep Neural approach for multi-class Classification of novel Corona Virus (COVID-19) using X-ray images

People all around the world are facing challenges to survive due to Corona Virus (Covid-19). Pneumonia is often caused by COVID-19. Biomedical field has witnessed the success of Artificial Intelligence (AI) models for automatic diseases analyses and detection. Deep Learning (DL), a sub-field of AI, is used in this work to classify COVID-19 from Normal and Pneumonia patients. Three architectures i.e., Novel Convolutional Neural Network (N-CNN), Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Random Forest (CNN-RF) models are proposed in this work for the classification of covid19 images from pneumonia and normal cases. We have used the X-ray image dataset in which 1212 training images consists of 404 images for each class and 300 validation images in which 100 images for each class. Five pre-trained models (VGG-19, VGG16, ResNet50, Inception v3 and Inceptio$\mathrm{n}_{-}$ResNetv2) are used to compare the classification performance with the proposed models. Among these pre-trained models and three proposed models, CNN-RF model outperformed and achieved an accuracy of 94.66% whereas N-CNN and CNN-LSTM models got an accuracy of 89.67% and 90.33% respectively.
Racial Disparities in Performance of Artificial Intelligence Algorithms for Diagnosis of Common Dermatological Diseases

According to several surveys and research works, dermatological disease is one of the most widespread diseases across the globe. Around 20% of the world's population suffers from these diseases, whether it is a simple skin problems like acne and rashes or serious disorders like melanoma skin cancer, psoriasis, and others. Dermatological diseases affect individuals of all races, but it's not distributed equally among different racial and ethnic groups. Racial biases exist in the prevalence, diagnosis, and treatment of skin diseases, leading to disparities in healthcare outcomes. This study explores the impact of race on dermatological diseases, examining how skin conditions present differently in various racial groups and the underlying biological and sociocultural factors that contribute to these differences. It also discusses the challenges that arise in diagnosing and treating skin diseases in patients from diverse racial backgrounds and the need for a more inclusive and culturally competent approach to dermatology. Understanding the racial biases in dermatology is crucial for achieving health equity and improving outcomes for all patients.
Event-Based Risk Assessment for Alert Limits in Automotive Lane Keeping

This paper analyzes the integrity risk allocation for automated driving applications involving lane keeping, with an emphasis on converting a risk-per-mile specification into a risk-per-event specification. The integrity risk allocation is the basis for defining a confidence bound (or protection level) for the navigation sensor error (NSE) distribution. For the vehicle to operate safely, the protection level must not exceed the alert limit, a geometric bound that specifies the worst-case tolerable error. In this comparison, the NSE distribution describes the range of values expected from the random error at a particular instant. However, safety targets are typically quoted on a per-mile or per-hour basis. Thus, research is needed to determine the frequency per mile (or hour) of independent events that cause a loss of integrity. This paper models lane keeping in two-way traffic to estimate, for that use case, the rate of occurrence of hazardous events. We combine these models with 2019 data for a particular road segment (part of MA Route 16). For that roadway, we estimate an events/mile rate of 19 in heavy traffic, 0.27 in low traffic, with a mean of 9.5 averaged over a typical weekday.
Flexible Activation Bag: Learning Activation Functions in Autoencoder Networks

An active area of research in the field of Machine Learning is the optimization of network structures, including activation functions. However, selecting a suitable activation function is not that simple and usually requires an elaborate empirical process. Unfortunately, this process is very resource intensive and does not always lead to the best solution. This paper presents a novel approach to select, compose and generate adaptive activation functions without any supervision. The best-suited activation function is learned from a collection of function candidates while training the neural network. Standard activation functions are replaced by a sub-network that outputs an indi-vidually parameterized function. The work raises the question whether modern activation functions that are rectified linear unit shaped are beneficial in unsupervised models. We evaluate our approach in autoencoder structures on industrial data and show performance improvements compared to existing methods. The approach is generally applicable to any task and data domain. Finally, ablation studies on the corresponding parameters under-line the cardinality of the approach and performance impacts.
Pansharpening of Multispectral Images using Deep Learning Architectures: Trends & Future Directions

The process of combining the geographic and spectrum information of the source pictures into a combined one is called panchromatic. The multispectral image fusing is also referred to as pan-sharpening. The final picture is more reliable for subsequent tasks because it has a higher spatial and spectrum precision than any original image. Many methods have been proposed to enhance the quality of the results obtained by image fusion. The fast growth of deep learning (DL) and artificial intelligence (AI) in recent years has significantly influenced the creation of pan-sharpening techniques. However, a thorough summary of recent advancements in this area aided by the rise of AI and DL is not yet accessible. A wide range of distinct deep-learning methods is examined in this summary piece. The substantial computation needed to use neural network-based methods for practical mapping prevented them from being evaluated. The methods were assessed using four spectrums and three geographic quality metrics. The results showed that most hybrid-based methods had the best spatial quality, methods based on CS had the worst results both spectrally and spatially, and methods based on Sparse representation did better regarding spectral quality.
Comparative Analysis of the Current Status of the Cosmetics Supply Chain Imported from China to Thailand by Applying the SCOR Model

In recent years, due to the impact of the COVID-19 pandemic, offline activities have tended to be turned online. In Thailand, during the week of March 22, 2020, the government enforced a countrywide lockdown, which facilitated an increase of sixty percent in the number of app downloads for shopping-related purposes. Due to various advantages, a large number of goods imported from China are sold through online platforms in Thailand, and the hot-selling category report of the sales platform shows that the sales of cosmetics are at the top. This article will focus on the research on the cosmetics supply chains of exporting products from China to Thailand. There are mainly two different types of cosmetics supply. This study will take company A and company B as the case and apply the Supply Chain Operations Reference (SCOR) model to analyse the integrity of the supply chain process and whether it has a performance indicator system so as to compare a better supply chain. According to the process definition of the SCOR model and the first-level performance indicator system, the research result shows that the process in company A's supply chain is incomplete, the division of department functions is unclear, and there is no complete performance indicator assessment system. Company B's supply chain process is complete and has coherent performance indicators, as well as a cost calculation and asset management system within the company. It shows that company B with Enterprise Resource Planning has higher efficiency and makes more profit.
PlastOPol: A Collaborative Data-driven Solution for Marine Litter Detection and Monitoring

Marine plastic pollution as a generally accepted global challenge has comprehensive impacts on our living environment. However, in practice, it is extremely difficult to monitor the severity of the problem based on the shortage of data and relevant tools. Thus, this has attracted academia and industry not only as an urgent environmental challenge but also as an interesting research problem. Many projects have been developed to collect relevant information with the help of beach clean-up volunteers and semi-professional. Although data collected by beach-cleaners cover large geographical areas and provide invaluable help in the effort of mapping marine litter hot-spots, the lack of uniformity in the classification systems, combined with the irregularity of clean-up activities makes it difficult to obtain the full picture. This issue further impacts on the information processing, analysis, and decision-support potential of the available tools. In this paper, we provide a collaborative data-driven solution to monitor marine litter along the coast. It includes a mobile application to collect images, a back-end server to map and train the classification model, and a general database to store and manage related data. The case study has been conducted in Ålesund city in Norway, preliminary results are further analyzed for system validation. This work aims to contribute to the United Nations' sustainable development goals 14 for life under water and 11 for sustainable cities and communities.
Modelization and Identification of Feed Drive Axis in CNC Machine Tools Using Two-Mass Model and Particle Swarm Optimization

The feed drive axis of a computer numerical control (CNC) machine tool is a composite mass system. This study modelized the feed drive axis of a machine tool into a two-mass model, and then used particle swarm optimization (PSO) to identify the physical parameters of the feed drive axis two-mass model for subsequent application. The modelization and identification method for the machine tool feed drive axis developed in this study could identify the following parameters: equivalent inertia of the driving motor and ball screw of the feed drive axis; equivalent mass of the moving table and nut; equivalent viscous damping among motor, ball screw, and nut; equivalent viscous damping between the moving table and linear guides; equivalent axial stiffness; and internal damping coefficient. The frequency characteristics of the feed drive axis were used as constraints of PSO to improve the identification results. The experimental results of the CNC lathe machine tool demonstrated the feasibility of the developed approaches.
Study of Highly Educated Women's Dating Willingness Based on XGboost and CART Algorithm

The willingness of highly educated women to choose a mate is discussed. First, based on the "one-to-one matching" mode of a well-known marriage platform in China, the training data set and test data set are constructed. Secondly, based on the CART decision tree algorithm, an interpretable model of the female's willingness to choose a mate with high education is constructed, which outputs the common characteristics of the female's willingness to choose a mate and the characteristics of the most popular male group. Then, based on the XGBoost algorithm, an inexplicable model of women with high academic qualifications' willingness to choose a mate is constructed, and a set of intelligent recommendation strategies is proposed, which can provide personalized member recommendation results according to the needs of different female members. The experimental results on the test data set show that the F1-Score of CART decision tree model is 78.1%, and the F1-Score of XGBoost model is 85.2%. The actual test results show that the success rate of the intelligent recommendation strategy is significantly higher than the manual recommendation of the matchmaker.
A Hybrid Approach to Parkinson's Disease Detection using Speech Attributes: The Combination of SMOTE and Active Learning

The neurodegenerative condition Parkinson's disease (PD) is characterized by a progressive loss of motor function over time, most notably by the onset of tremors and muscle rigidity. PD is not lethal, but it can worsen and have a substantial influence on daily living. Early in the course of PD, diagnosis may be challenging since symptoms may res emble thos e of other illnes s es and age-related physiological changes. Techniques like patient-directed sketching exercis es are employed to identify aberrant motor patterning. In order to handle unequal datasets, this article first analyses how effectively the SMOTE technique can categorize data before offering an Active Learning method Experimental results show that the propos ed strategy outperforms several popular clas s imbalances learning techniques in terms of accuracy, precision, and recall (AUC, F-measure, and G-mean)
Spoofing-Resilient LiDAR-GPS Factor Graph Localization with Chimera Authentication

Many vehicle platforms typically use sensors such as LiDAR or camera for locally-referenced navigation with GPS for globally-referenced navigation. However, due to the unencrypted nature of GPS signals, all civilian users are vulner-able to spoofing attacks, where a malicious spoofer broadcasts fabricated signals and causes the user to track a false position fix. To protect against such GPS spoofing attacks, Chips-Message Robust Authentication (Chimera) has been developed and will be tested on the Navigation Technology Satellite 3 (NTS-3) satellite being launched later this year. However, Chimera authentication is not continuously available and may not provide sufficient protection for vehicles which rely on more frequent GPS measurements. In this paper, we propose a factor graph-based state estimation framework which integrates LiDAR and GPS while simultaneously detecting and mitigating spoofing attacks experienced between consecutive Chimera authentications. Our proposed framework combines GPS pseudorange measurements with LiDAR odometry to provide a robust navigation solution. A chi-squared detector, based on pseudorange residuals, is used to detect and mitigate any potential GPS spoofing attacks. We evaluate our method using real-world LiDAR data from the KITTI dataset and simulated GPS measurements, both nominal and with spoofing. Across multiple trajectories and Monte Carlo runs, our method consistently achieves position errors under 5 m during nominal conditions, and successfully bounds positioning error to within odometry drift levels during spoofed conditions.
A Study on Battery Storage Exploitation using Reinforcement Learning

An integral part of the new smart grid, battery storage is becoming an increasingly common requirement. Batteries are considered as an attractive alternative for other distributed intelligent energy resources because of their speed in responding to events like changes in renewable power or grid interruptions. Various research studied exist to explain about making money out of this talent. Any solution must take into account both the fast-paced nature of electrical phenomena and the more gradual changes in the relevant power markets. An area of AI called reinforcement learning has demonstrated promising results in optimizing difficult tasks with high degrees of uncertainty. As a result, the need for first-of-its-kind smart buildings is growing in tandem with the complexity of decentralized power systems and the widespread use of renewable energy sources. The initial portion of the algorithm is based on deep learning using recurrent neural networks to deal with the unknowns of power pricing and load needs in the future. The other method uses reinforcement learning to determine the best time to charge or discharge a battery bank based on profit, load, and grid peak conditions. This research study presents a reinforcement learning approach for battery scheduling, which is considered as a crucial component to meet the consumer objectives. The framework relies on a customer making a multi-criteria choice in order to maximize local energy production from batteries during peak demand. This will save costs by reducing the amount of power used by the grid. The reinforcement learning system used to choose the best battery scheduling actions uses predictions of available wind power. Consumers' understanding of what to do to maximize battery life in a variety of time-dependent settings is expanded thanks to the built-in learning mechanism. The established framework enables smart consumers to acquire knowledge of the stochastic environment and apply it to the process of making decisions about energy management strategies.
Indoor LiDAR Point Clouds Upsampling for Object Detection Enhancement

LiDAR sensors generate noisy, sparse, and non-uniform point clouds. Although 3D sensing has had significant progress in recent years, it is still expensive to produce high-quality point clouds. Therefore, given a low-density dataset, point cloud upsampling is required to obtain point clouds with high density and more complete details. This study addresses an upsampling network called PU-GCN on a LiDAR dataset collected in an indoor environment such as an underground parking lot to generate a dense and uniform dataset. Then, the VoteNet model is used for evaluating the impact of point cloud upsampling on the output of object detection. The dataset applys in this study is collected at an underground parking lot located at Highland Square, Kitchener, Canada. The results indicate that after implementing upsampling, the Average Precision increase from 67.8179% to 95.02% and the Average Recall increase from 67.8571% to 95.24% at a threshold of 0.25.