Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Techniques for using Intelligent Internet of Things Appliances to Protect Health Department Information

Profound Learning (DL) is an innovation that repeats the manner in which the human mind’s capabilities with regard to deciphering information and showing up at choices. DL was created by Google and IBM. DL and brain networks have a wide assortment of uses, some of which incorporate discourse acknowledgement, normal language handling (NLP), and PC vision. In the field of medical care, it has been put to use in the determination and therapy of an expansive assortment of constant sicknesses, as well as in the counteraction and the board of such circumstances. These calculations might help with ending the spread of ailment, improving the proficiency with which clinical analyses are made, and diminishing the monetary weight put on patients as well as authoritative staff. This paper examines the technique of profound realizing, which is utilized in an assortment of medical care disciplines, in addition to analyzing the Elliptic Curve Cryptography solution to the healthcare data security problem. Following the completion of the summaries of the data obtained from each category, comparison tables are prepared employing the essential criteria. The frameworks for DL make use of a broad array of applications, tools, techniques, and data sets. DL stands for deep learning. In order to bring this whole thing to a close, we will talk about the opportunities and challenges that lie ahead for further research into profound learning models.
Data Reduction Techniques in Wireless Sensor Networks with AI

Due to their numerous uses in practically every part of life and their related problems, such as energy saving, a longer life cycle, and better resource usage, the research of wireless sensor networks is ongoing. Its extensive use successfully saves and processes a considerable volume of sensor data. Since the sensor nodes are frequently placed in challenging locations where less expensive resources are required for data collection and processing, this presents a new difficulty. One method for minimizing the quantity of sensor data is data reduction. A review of data reduction methods has been provided in this publication. The different data reduction approaches that have been put forth over the years have been examined, along with their advantages and disadvantages, ways in which they can be helpful, and whether or not using them in contexts with limited resources is worthwhile.
System Design of a Toxic waste management system Empirical study for Chiang Rai province

Waste or garbage is a daily occurrence and growing in quantity. Humans try to find a way to dispose of, recycle or decompose those wastes naturally to return to nature as a resource. However, one toxic waste type that cannot decompose naturally is hazardous waste. Hazardous waste has long been a problem in disposal, especially in Thailand, where there is a lack of reasonable hazardous waste management procedures. Moreover, hazardous waste causes many problems for both communities and government agencies. In this research, there is collaboration between the research team, Chiang Rai Provincial Administration Organization (PAO), and 143 local governments. We research and develop a hazardous waste management system called the “D-ToC” (Data supporting system for Toxic waste in Chiang Rai). We propose system design and UX/UI design of the system in this paper. However, the goal of this research work is to create zero toxic waste in Chiang Rai province. Through this cooperation, the work has been successful for five years since 2017. - 2021, and it will continue to be implemented and developed.
Machine Learning Based Power Control for a Secondary Cell-Free Network

Cell-free networks, in which a large number of distributed antennas transmit high speed data to multiple users, is emerging as a next generation wireless technology. Cell-free networks that share spectrum in an underlay manner provides above benefits and improves the utilization of the crowded spectrum. We focus on a well motivated problem of optimal power allocation to maximize the energy efficiency (EE) of an underlay cell-free network. This forms a non-convex optimization problem, which is computationally complex to solve. To reduce this complexity, in this work, we develop different machine learning models that can estimate the power allocation coefficients. We develop two deep neural network models and a decision tree model. Training data is generated with multiple practical limitations such as imperfect channel state information and spatial channel correlation. Our simulations show that these machine learning models perform very close to the traditional optimization techniques.
Forwarding Factor Routing Protocol for Opportunistic Internet of Things Networks

The delay opportunistic network is one of the subnetworks that make up the more tolerant network. The Internet of Things and the Opportunistic Network are the two components that come together to make up what is known as the Opportunistic IoT Network. Mobile ad hoc networks, on the other hand, need a connection that is always active and goes all the way to the end in order to function properly. But in an opportunistic network, the nodes are free to go anywhere they choose, and messages can use different channels to get where they need to go. Therefore, opportunistic networks make the process of routing more difficult. Opportunistic networks are characterised by the fact that the connections between nodes in the network are dynamically rearranged in response to the mobility of the nodes. This is accomplished by communication with neighbouring nodes using a technique referred to as “store-carry-and-forward. " The message is transmitted from one node to the next until it reaches the node that is its final destination. As a result of our investigation, we propose a brand new routing system that we term Forwarding Factor with Acknowledgement. Because it identifies potential forwarders by watching how nodes communicate with one another, the number of copies that are dispersed throughout networks is decreased. An acknowledgement is transmitted in the similar manner back to the sender node to determine whether or not the delivery was successful at the destination node. Forwarding Factor with Acknowledgement is a protocol that was developed with the intention of improving upon the performance of earlier protocols such as Epidemic and Prophet by lowering the amount of communication overhead and raising the delivery ratio.
A review on Artificial Intelligence based Sign Language Recognition Techniques

The World Wide Web Consortium describes web accessibility as the ability to access any information online regardless of the technology used and interacting with the interface of the web if required. Over the last few decades, significant efforts have been made in the field of web accessibility. Successful tools were developed leveraging technologies such as Artificial Intelligence, Speech Recognition systems and motion detection using Computer Vision. Sign Language is a form of non-verbal, hand movement or gesture based mode communicating thoughts and ideas. It is the primary way of communication for many people, majorly including differently abled people, such as people with speech-impairments. Despite being popular amongst people who are unable to speak or hear, there exists a great disparity in the knowledge of Sign Language in the general population consisting of able-bodied individuals. This necessitates the existence of a system that bridges this gap, and allows for efficient interpretation of Sign Language. Sign Language recognition is a growing field of interest in the domain of Artificial Intelligence, and many tools have been created to automate this process using image recognition techniques. These techniques are used to classify sign language and map them into their corresponding meaning in the English language. The steps required in this include the acquisition of data, extraction of relevant features followed by the training of the classifier. This paper gives an overview of the level of work done in this field, and delineates the scope of some models created for this purpose. It also discusses the challenges that are being faced and the scope of work that is yet to be done.
A BBL-Net based OFDM Signal Detection in the Presence of RF Impairments

Channel estimation is an essential technology for any multi-carrier modulation (MCM) framework. Traditionally, least square (LS) and minimum mean square error (MMSE) estimators are used based on the availability of channel state information (CSI). However, in the presence of radio frequency (RF) impairments, i.e., carrier frequency offset (CFO) and phase offset(PO), the statistical models lose their optimality. This paper proposes a batch normalized Bi-directional long short memory network (BBL-Net) based joint channel estimation and signal detection approach for orthogonal frequency division multiplexing (OFDM) system in the presence of CFO and PO at the receiver. A combination of experiments has been conducted to find the optimum activation function for the BBL-Net architecture. Simulation results show that the trained model can recall and predict the information efficiently even in high interference scenarios and also provide robust performance in lower pilot numbers.
A Novel Single Phase Three Level Triple Boost CG Switched-Capacitor Based Grid-Connected Transformerless PV Inverter

A new three-level inverter based on the switched capacitor (SC) with a common grounding (CG) configuration has been introduced in this article, which is capable of boosting the input voltage up to three times. Therefore, this inverter is configured without any boost dc/dc converter stage for low voltage PV application. To step up the input PV voltage and facilitate three level in output voltage, two SC cells are connected in parallel. This inverter's common grounding configuration helps in bypassing the stray capacitance, which eliminates the leakage current and consequently reduces electro-magnetic interference (EMI) considerably. Hence, for electro-magnetic compatibility (EMC), this inverter does not require any EMI filter. Additionally, this inverter handles reactive power during the grid's lagging/leading power factor (pf) condition. A proportional resonant (PR) current controller is designed to regulate active and reactive grid power. The proposed modulation strategy helps in balancing capacitor voltages at their base values with allowable ripple. The inverter circuit details, control technique, PWM scheme, thermal modeling, and loss analysis with component design guidelines are described in detail. Experimental results are presented to validate the effectiveness and feasibility of this proposed inverter.
User Profile and Mobile Number Portability for Beyond 5G: Blockchain-based Solution

Mobile Number Portability (MNP) as a regulatory requirement for Mobile Network Operators (MNO), allows users to easily switch their operators while keeping their number. User subscription, profile management, and MNP procedures in current MNOs are handled in centralized systems and databases that rely on trusted third parties. Having a single point of failure, low scalability, high latency, imposed porting fees, data leakage in the central database, low availability, and need for re-subscription while porting procedure are the most highlighted defects of the conventional systems. Addressing these issues, we propose a Blockchain-based system to 1) manage user subscription and user profile/identity in a distributed database, and 2) mobile number and profile porting procedure. This solution eliminates any central point via managing the processes using smart contracts. Moreover, it decreases IT complexity, increases automation, provides low latency, and high confidentiality. The experiments confirm that this solution can provide fast and scalable profile management and porting solution.
Deep Learning-based Topic-level Examination of Social Media

Due to its superior processing power in fields like text, picture, and audio processing, deep learning (DL) is becoming more and more popular. The exponential growth and extensive accessibility of digital social media (SM) make the evaluation of this data challenging, if not impossible, using conventional methodologies and technology. It has been determined that DL is a suitable answer to this issue. After the enormous work put into SM analytics, we provide a taxonomy-focused assessment of the DL architectures employed in this research (SMA). However, rather than focusing on a technical explanation, this work aims to explain the SMA-related problems utilizing DL-based remedies. We also discuss the potential and difficulties for DL research in the future, including scalability, heterogeneity, and multimodality.
AI and IoT Enabled Technologies for Monitoring the Right to Health of Disabled People

Healthcare services conveyance need the help of new advances like AI, the Internet of Things (IoT), & AI to battle and look forward against new illnesses. This paper aims that how technology would enhance healthcare system and prepares the role of technologies. There is a requirement of proper monitoring the People with disabilities (PWD) as they are too sensitive, AI based healthcare system can be adequately handle the situation of emergencies of differently abled people and will provide the advanced technologies for taking care of special people. So, this study we have explored the technological interferences that can help people with disabilities for health advances. In this article we step forward to safeguard the disabled privileges by means of arising advances and give ideas for their better support, well-being and prosperity to working on by and large their personal satisfaction.
The Classification of Edible-nest Swiftlets Using Deep Learning

The edible bird’s nest (EBN) is in high demand due to being widely consumed in China and many Southeast Asia countries. It claims that it is healthy food and there are many medical benefits. EBN farming is a lucrative industry because it is considered one of the most expensive animal products. However, for quality grading, this process is time-consuming because it is done manually by the expert based on color, shape, texture, and size. This paper presents a method of computer vision to grade EBN automatically. Deep convolutional neural network (CNN) algorithms are applied to classify the images of edible-nest swiftlets in the following categories Grade A, Grade B, and Corner. In the experiment, seven CNN architectures are compared in terms of the performance: MobileNetV2, Xception, DenseNet121, DenseNet169, DenseNet201, VGG16, and VGG19. As a result, DenseNet201 and VGG16 have the same highest accuracy of 99.34%. Therefore, this work is useful for automatically quality grade EBN.
Shear thickening in model suspensions of sterically stabilized particles

The onset of shear thickening (dilatancy) has been studied in submicron model suspensions of sterically stabilized spherical particles. Stress controlled rheometers have been used for this purpose, so that measurements can be performed beyond the onset of sudden shear thickening. Systematic data are presented for the effect of particle size, particle concentration, and the nature of the suspending medium on the onset of shear thickening. As a first approximation, the critical shear rate changes inversely proportional with the medium viscosity. The changing solvency of the medium for the stabilizer polymer introduces additional changes through effects on the thickness and stiffness of the steric barrier. Thinner, stiffer barriers cause lower critical shear rates. In the softer systems the critical shear stress becomes independent of particle concentration in dense suspensions. This does not seem to be the case for the harder systems. The effect of particle radius a could shed some light on the underlying m.
Learning-Based Anomaly Detection Using Log Files with Sequential Relationships

Modern IT systems have been transitioning from traditional on-premises solutions to a dynamic mixture of on-premises and off-premises solutions. This transition has also included a trend to run more systems on software-defined resources. The ease of setting up new software-defined servers and systems has led to an increase in IT system complexity as well as the amount of log data generated. Automatic log analysis has become a subject of interest because of the problems with manual log analysis in case of intrusion detection and root-cause analysis. Therefore, this paper proposes and tests a sequence based anomaly detection method. The work has been done in collaboration with the Swedish Social Insurance Agency’s IT department. Real system log data with high privacy requirements and limited available information was generated for training and testing. The generated log data was produced with expected time regions of anomalous behavior. Our proposed anomaly detection model was then able to perform at a state-of-the-art level and could accurately detect certain error types. Showing the potential of the approach when applied directly to a real-world system.
Inferencing component maps of gas turbine engine using bayesian framework

A Bayesian framework is proposed for characterization of uncertainty in gas turbine performance predictions induced by poorly known component maps and uncertain model structure. Uncertainty in component maps and the model structure is characterized by independent Gaussian processes. Bayesian calibration is used to update the uncertainty whenever new information is available through experimental observations and expert opinion. The Markov Chain Monte–Carlo method is used to sample from posterior distribution. Parameters from the posterior distribution are identified that provide information about the validity of the model and experimental observations. Updated uncertainty is propagated to system response using theMonte–Carlo method. The propagated uncertainty in system response is represented using Bayesian confidence bounds. Proposed framework is demonstrated for calibration of a design intent single spool turbojet engine simulator. Compressor and turbinemaps are inferred using the Bayesian framework.
Machine Learning Based Corporate Climate Change Disclosure in Integrating Institutional and Resource Approach based on Computational Intelligence Method

There has to be a coordinated effort between academia, government, and business to evaluate and shape the impact of AI and ML on efforts to slow global warming. It’s not clear how ML will affect GHG emissions in the future, but it’s already having an impact now. This is due in part to the fact that assessing and predicting the effects of these emissions is complicated by the fact that their various processes are not well characterised. As a result, we present a structured approach for characterising the consequences of ML on GHG emissions, which is comprised of three levels: (1) compute-related implications; (2) immediate repercussions of using ML; and (3) effects on the whole system. This approach is used to assess and rank the importance of the information and studies required for carrying out an effect evaluation, doing a scenario analysis, or identifying crucial policy levers.
Predicting the On-The-Job Training Performance of IT Students on Virtual Internship Using Naïve Bayes

The student on-the-job training is one of the major concerns of the Higher Education Institution (HEI) in this time of pandemic. Virtual Internship was introduced and can be challenging to the students. This study focuses on data mining techniques to predict the performance of student on-the-job training in an online environment among IT students in Central Luzon State University. The researcher used Naive Bayes classification technique and 80 IT students are the respondent of the study. The performance of Naive Bayes algorithm using the WEKA tool 3. S.5 having a precision of 0.867 and a recall of 1.000 which is a high accuracy can be applied in predicting student performance in an online environment of every HEI for more efficient and effective on-the-job training performance of every student. This approach can help the HEI in developing student success in online work related performances. It will also give a positive perception that taking the on-the-job training in an online environment is the same as taking the actual working environment.
Hadamard Estimated Attention Transformer (HEAT): Fast Approximation of Dot Product Self-attention for Transformers Using Low-Rank Projection of Hadamard Product

In this paper, the author proposes a new transformer model called Hadamard Estimated Attention Transformer or HEAT, that utilizes a low-rank projection of the Hadamard product to approximate the self-attention mechanism in standard transformer architectures and thus aiming to speedup transformer training, finetuning, and inference altogether. The study shows how it is significantly better than the original transformer that uses dot product self-attention by offering a faster way to compute the original self-attention mechanism while maintaining and ultimately surpassing the quality of the original transformer architecture. It also bests Linformer and Nyströmformer in several machine translation tasks while matching and even outperforming Nyströmformer's accuracy in various text classification tasks.
Thai License Plate Recognition Based on YOLO Detector and CNN-Classifier

License Plate Recognition (LPR) has many different applications, especially in the intelligent transportation system, for example, an automatic tollgate that allows people to pass through and then bills the cost later according to the identification captured by the LPR system. Although a significant amount of research has been done on Western and Chinese license plates, there is a lack of work on Thai license plates. In this work, we introduce an LPR system that focuses on Thai license plates using a combination of YOLOv5 (You Look Only Once) object detection, mathematical morphology, and Convolutional Neural Network image classification. The proposed method is trained using images of vehicles that are presented at tollgate. The plate detection model is trained with 400 images while the OCR model is trained using 23003 images. In testing, the system achieves 98.4% recall for plate detection, 98.22% accuracy for character recognition, and 93.37% accuracy in the end-to-end scenario.