Miracle-dz/UniFAR-FTUs huggingface dataset:
An In-Depth Analysis of Electric Vehicle Charging Station Based on LSTM and SVM Hybrid Model

As part of their future smart city ambitions, many countries hope to increase environmental sustainability by electrifying their transportation networks. This means that the percentage of electric vehicles in any particular city's total vehicle population will rise rapidly. Electric vehicle (EV) batteries can be recharged in a number of ways, but charging stations will be given top priority. Charging stations should be dispersed across a city in such a way that all EVs may reach one within a reasonable period of time and then use that charge to travel the entire city. This data preprocessing phase makes use of normalized processing, time interval processing, and outlier processing. The features are derived using principal component analysis. The LSTM method entails a set of four gates (an input, a forget, a learn, and a remember), All of which contribute to the model's training. The models are tested using an LSTM-SVM method. When compared to LSTM and SVM, the proposed method produces higher-quality results.
A Novel Wire-Driven System for a Robot-Assisted Laparoscopic Surgical Training

Minimally invasive surgery (MIS) has gained popularity owing to its potential to facilitate fast postoperative recovery. However, a major limitation of MIS is the extensive training required for surgeons to master the complex hand-eye coordination required to safely execute surgical procedures. The traditional approach of learning from demonstration and hand-over-hand training has many constraints in laparoscopic surgery. Attempts have been made to enhance learning effectiveness using robot-assisted surgical training. In this paper, we describe a new robotic system that is driven partially by a wire-driven mechanism to assist surgeons in acquiring laparoscopic surgical skills. Experiments showed that the new robotic system could be used to provide realistic motion guidance for novice laparoscopic surgeons. The proposed system can also be modified for robotic laparoscopic surgery.
A Synthetic Dataset for Robotic Food Handling System

This paper aims to address the issue of labor shortage in the food industry by constructing a food automatic sorting system to replace manual labor and alleviate the workload of employees. Currently, deep learning is widely applied in the food industry. However, the process of creating a self-made dataset through capturing and annotating images consumes significant resources. To overcome the challenge, this study adopts a method of automatically generating the dataset, specifically generating instance segmentation data, to train the deep learning model for visual prediction of overlapped food items. Additionally, a soft robotic end-effector is used for food handling to prevent food items from damage during the sorting process. By implementing the proposed food sorting system, the overlapped food items were accurately predicted using a limited number of food items, and were successfully sorted in the food handling tasks.
An Intelligent HealthCare System Using Blockchain Techniques

Blockchain is the significant cutting-edge technology and inventive advances currently with an important function in the professional world. The exponential growth of blockchain technology has opened new application possibilities as in field of healthcare. The proposed system outlines a decentralised e-healthcare application framework for user-owned and accessible personal data management. The incorporation of cutting-edge techniques as big data, blockchain has boosted medical innovation along global development and enhanced design. The proposed model utilizes interpretive structure modelling (ISM) split order based on theoretical concepts, manage the intricate interactions between different elements and attributes which utilizes fuzzy set theory technique to remove not necessary attributes. The observation findings indicate firstly that the system's root causes include the first-level design, management of medical information, and the organisation of doctors. Secondly, key to system improvement is application optimisation. The unique intelligent contracts are at the heart of most blockchain applications in the smart healthcare space, which is dependent on the arrangement of medical information, but it is limited by system. Thirdly, development of system and effective protection of stakeholders' interests are protected by medical insurance, environmental governance, and internal and external legislation. Fourthly, blockchain application system for smart healthcare must be constructed using three consecutive layers such as the layer containing transaction, layer integrating data, and layer with stakeholder. The development in blockchain applications for smart healthcare is implemented by a theoretical hierarchical structure.
Data Analysis and Visualization of the Industrial IoT Based on the Big Data Approach

The present work proposes a solution for the visualization of temperature and pressure data from plastic injection machines. From the collected data. , an analysis of these data is carried out. , to present the data in the clearest possible way. This data is sent to a local database. , which then must be visualized in the form of graphs in real time. For further queries of the data. , a dashboard is also developed for these data. , divided by time intervals. , in which the maximum. , average. , and minimum data of temperature and pressure are visualized. , in the form of line and circular graphs. The solution intends to be used not only in a web environment. , but also on the production line in low-cost single-board systems. , such as RaspberryPI. , allowing real-time visualization.
A Comprehensive Overview of the Novel Approach to Detect Alzheimer’s Disease using Deep Learning and Convolutional Neural Network

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. Early and accurate diagnosis of AD is crucial for effective treatment and intervention. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for automated disease diagnosis from medical imaging data. This abstract presents a promising approach utilizing deep learning and CNNs for Alzheimer’s detection. The proposed method involves leveraging neuroimaging data, such as magnetic resonance imaging (MRI) scans, to develop a reliable and automated AD diagnostic system. Initially, the MRI scans are pre-processed to enhance image quality and remove noise. Subsequently, a CNN architecture is designed and trained using a large dataset of both AD and non-AD images. The CNN learns intricate features and patterns directly from the data, enabling it to distinguish AD- related structural abnormalities and identify potential biomarkers. The trained CNN model is then employed for AD diagnosis by inputting new, unseen MRI scans. The model applies its learned knowledge to extract relevant features from the input images and generates a diagnostic output indicating the likelihood of AD presence. The diagnostic output can be further validated and interpreted by medical professionals, assisting them in making informed decisions regarding patient care and treatment strategies.
Graph Neural Networks for Drug Response Prediction

Drug response prediction plays a crucial role in precision medicine, such as cancer analysis and treatment. Due to the uncertainty of drug efficacy and the heterogeneity of cancer, predicting drug response in vitro is expected to assist in guiding anticancer drug design and understanding cancer biology. Based on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), we propose a deep learning method for predicting drug sensitivity. Specifically, we first construct graphs of drugs and cell lines in the benchmark dataset. And we design a drug feature extraction module based on Graph Transformer and a cell line feature extraction module based on Graph Attention Networks (GAT) to capture the embeddings of drugs and cell lines. Finally, we feed the drug embeddings and cell line embeddings into the module for prediction. Experiment results show that our method outperforms several advanced models in drug response prediction, demonstrating the good performance and enormous application potential in precision medicine. In addition, the results of ablation experiments demonstrate the effectiveness of the modules in our model.
Smart IoT Solutions for Personalized Health: MQTT- Based Blood Pressure Monitoring System

Healthcare awareness can be achieved via remote monitoring of vital signs using Internet of Things (IoT) technology. The proposed system aims to monitor blood pressure and other important signs with the help of the MQTT (Message Queuing Telemetry Transport) protocol. The main objective of the blood pressure monitor is to increase health awareness through real-time warnings for high and low blood pressure. The device has sensors that can measure blood pressure quickly and accurately. Using IoT technology, the monitor may send this information wirelessly to a central server where it can be processed and analyzed. MQTT is the protocol for two-way communication between the blood pressure monitor and the main server. When a measurement is taken, it is compared to a set of medically recognized and individually adjusted threshold values to determine whether or not an individual has high or low blood pressure. IoT devices get notifications whenever blood pressure measurements exceed predetermined limits. After receiving these notifications, a device can communicate them to the user interface, such as a web application. This wireless IoT blood-pressure monitor improves health awareness by warning users of unhealthy blood pressure levels. Alerts allow users to contact medical help, modify medication, or adapt their lifestyle to maintain ideal blood pressure.
Transfer Learning-Based Tomato Disease Prediction using Leaf Images

Tomato plants are among the most important of all the fundamental crops, valued highly by both farmers and consumers. The biggest concern for tomato farmers is that the plant is becoming infected with unidentified diseases. Farmers must recognize the type of disease to treat the plants appropriately when numerous diseases could hinder growth. The goal of this project is to create three distinct transfer learning models that can accurately identify the type of sickness. The most effective algorithm for this purpose is also finalized. For this purpose, a dataset of more than 10,000 images is collected from Kaggle. This collection contains pictures of tomato plant leaves in both a healthy and a diseased state. Three sorts of data must be created: training, testing, and validation. The smallest portion is utilized to validate the model, while the second largest portion is used to test the model for its final parameters. After separating, the photographs are preprocessed. In this study, image scaling is the only preprocessing technique used. The transfer learning models that were developed utilizing three different transfer learning strategies are then tested and trained using the preprocessed and divided images. In this experiment, the VGG-16, AlexNet, and hybrid algorithms were applied. Testing, validation, and training outcomes are recorded. But next, to enhance readability and facilitate analysis, the data is converted by being plotted onto a graph. Throughout the training, the hybrid model showed significantly greater performance than the other two models. The models created using the VGG-16 and AlexNet algorithms displayed a lot of oscillations, even when the margin shrank during validation, which could lead to inconsistent performance. The model can be used in the future to help farmers and other agriculturists accurately identify a problem with their plant and search for a workable remedy by integrating it into the backend processor of a website or software application uses pre-trained convolutional weights to optimize the model.
Integration of Web of Tactile Things for Soft Vision-Based Tactile Sensor Toward Immersive Human-Robot Interaction

Web of Tactile Things (WoTT) aims toward a standardized platform designed for tactile devices, facilitating the exchange and updating of data across diverse domain web services. However, the requirements for specified devices pose a challenge when deploying on low-profile devices. Furthermore, the concentration of processing the sensing and server operations on a single computer creates a bottleneck that restricts the system's scalability. To enhance the potential applications, this study introduces an extended platform based on WoTT that decentralizes processing. This separation involves distinct processing for the tactile sensor and the WoTT server becomes independent elements. The tactile sensor is integrated with an onboard computer for sensing tasks, enabling wireless transmission of tactile data packets. In this proposed system, each external device can establish wireless connections with the server via the MQTT (Message Queueing Telemetry Transport) message protocol. In order to validate this approach, we construct a comprehensive system incorporating tactile sensors, AR devices, and 2D linear stages. Leveraging a deep neural network (DNN), the tactile sensor's data drives the anticipation of soft skin deformation by capturing visual cues through marker displacement. Tactile data is then encoded and transmitted to end devices using the MQTT protocol, directing the 2D linear stage's interaction with a phantom arm, while AR devices visualize the tactile sensor's soft skin deformation.
Discriminant Method between Consciously Paying Attention and Unconsciously Seeing using Gaze Rate Distribution

This paper proposes a discriminant method whether a person pays attention to something consciously (intentionally) or sees it unconsciously by using a time-series of gaze rate distributions. The gaze rate is defined as a statistical possibility of when and where many people look, e.g. in some target movie. In this paper, first, eye movement data from multiple participants wearing a head mount display (HMD) are measured when some movie is displayed. Second, the gaze rate is calculated as a distribution of statistical probabilities at each frame (time). Lastly, verification experiments of our proposed gaze rate-based conscious/unconscious discrimination have been conducted compared to results from saliency-map-based prior method, and have got better results numerically. The author think it is quite important and meaningful to distinguish whether a person look at something consciously or just sees unconsciously, because it is not difficult to understand such discrimination improves analysis of persons' potential consciousness more precisely and more in detail.
Automatic Update System for Evaluation of a Condition of Rheumatoid Arthritis Using Deep Learning

Artificial Intelligence (AI) is used for various fields including the medical field. A lot of data are necessary to improve the accuracy. However, it is difficult to gather the medical data. This paper describes the AI system that improves the accuracy with every usage automatically by the online improved techniques applied to the diagnosis of rheumatoid arthritis (RA). And its effectiveness is predicted. The result of the running using two PCs and one server and the realization of the system are shown. As a result, we confirmed that the serve-client system that is to update the model automatically using uploaded data set made by each client is constructed.
LiDAR Scan Images for Mobile Robot Motion Planners

During autonomous navigation, mobile robots are required to avoid obstacles ahead in some cases. We assume a mobile robot equipped with a $2D$ LiDAR. For the robot, we have proposed motion planners based on deep neural networks, such as multilayer perceptrons, MLPs. The networks were trained through imitation learning. However, the robot based on these motion planners using range data inputs sometimes failed to avoid obstacles in unknown environments. This is because that the robot planned avoidance motions without considering the spatial configuration. In this paper, therefore, we focus on the obstacle avoidance capability of the robot, and propose to use LiDAR scan images as inputs. The LiDAR scan images are generated from the range data, and used as inputs to four types of motion planners based on MLP and convolutional neural network, CNN. In the navigation experiments, the effectiveness of the motion planners for improving the obstacle avoidance capability is discussed. Moreover, we examine the applicability of the motion planners to the actual mobile robot for autonomous navigation in a real environment.
Assessing Human Settlement Sprawl in Mexico via Remote Sensing and Deep Learning

Understanding human settlements' geographic location and extent can support decision-making in resource distribution, urban growth policies, and natural resource protection. This research presents an approach to assess human settlement sprawl using labeled multispectral satellite image patches and Convolutional Neural Networks (CNN). By training deep learning classifiers with a dataset of 5,359,442 records consisting of satellite images and census data from 2010, we evaluate sprawl for settlements across the country. The study focuses on major cities in Mexico, comparing ground truth results for 2015 and 2020. EfficientNet-B7 achieved the best performance with a ROC AUC of 0.970 and a PR AUC of 0.972 among various CNN architectures evaluated. To evaluate human settlement sprawl, we introduce an information-based metric that offers advantages over entropy-based alternatives.
Sentimental Analysis on Media

Sentiment analysis, commonly referred to as opinion mining, is a method for figuring out the emotional undertone or attitude of a text. Since it enables better knowledge of how the general public feels about certain issues, goods, or events, sentiment analysis has grown in popularity in the media industry in recent years. In particular, movies and series are the emphasis of this paper’s sentiment analysis of media material. The goal is to create a system that can categorize media material as good, negative in emotion. A sizable dataset of media content is first gathered for the study and preprocessed to eliminate noise and unimportant data. The dataset is then subjected to several machine learning methods in order to determine the text’s emotion. Each algorithm’s performance is assessed, and the top performing algorithm is chosen for more examination. The study’s findings demonstrate that sentiment analysis may be utilized to accurately assess media material with a rate of above 80%. The technique may be used to monitor public opinion on a range of subjects, including reaction and emotion. As media companies and users can use sentiment analysis to better understand the attitudes and opinions of their target audience, the study’s findings have significant ramifications for all of these groups.
High Speed Transmission Characteristics on Glass Based Interposers

Interposers for system in package will became more and more important for advanced electronic systems. As the industry moves toward HPC (High Performance Computing) for huge data transmission with low power consumption. The major engineering requirements for HPC application are high-density, high speed data transmission, low loss, precision manufacturing and low cost. On the 2.5D heterogenous package structure, key technology is fine wiring connected with CPU chip and HBM chips. The fine wires needed high speed transmission. This paper presents the demonstration of Glass-based twin type interposers, Glass interposer with fine pitch metalized through via and RDL interposer with low loss dielectrics on Glass carrier. We compare twin types of Interposes fabrication process capability with panel size format 300×400mm. Finally, we compare the transmission characteristics using eye diagram for heterogenous integration application. Glass Interposer has low loss glass via, it is effective vertical interconnection. RDL interposer has low loss dielectric layers, excellent transmission characteristics obtained on fine pitch trace area.
Detection of Texting While Walking in Occluded Scenarios Using Variational Autoencoder

Texting while walking is a common behavior exhibited by pedestrians. While several studies explored the detection of texting while walking, the influence of occlusions were neglected. In this paper, we propose an image-based method which utilizes a pre-trained Variational Autoencoder. The proposed method takes sequence of 2D coordinates of upper body key points of the pedestrians as input, encodes the data into a 2D latent space, and uses the encoded data to distinguish text walkers from normal pedestrians. The proposed architecture enables the model to extract meaningful features from occluded data. Results of ablation test and comparison with a previous method revealed that the proposed architecture is successful in identifying text walkers even under heavy occlusion, outperforming a previously proposed method.
Beginning and Ending Years of Technological Revolutions Estimated by Tabular Pattern Calculations as well as Further Thinking

In the past 200 years, five technological revolutions have occurred. What makes us curious is the information concerning the sixth and the seventh ones, as a topic of future measure. In this paper, we use the tabular pattern calculations method, which is based on the separation and combination considerations of the data, to make predictions concerning the possible beginning years and ending years of these technological revolutions. Besides, we conduct further thinking based on known data and predicted results. Moreover, we discuss the possible themes of the seventh technological revolution. Finally, we draw the summary that the fifth technological revolution would be related to 1971 - 2029; the sixth technological revolution would be related to 2029 - 2072; the seventh one would be related to 2072 - 2124.
An Adaptive Dynamic Bandwidth Management Model for Smart Building Systems Using Elastic WLAN Mechanisms

The development of the implementation of Internet of Things (IoT) technology to support services in various sectors has been massive in recent years. This development is predicted to continue in line with the increasing quality of the Information and Communication Technology network. As an effort to maintain service quality, increasing IoT implementation requires adequate bandwidth availability. This research develops a model that seeks dynamic and adaptive bandwidth management to traffic needs in the Smart Building system. This bandwidth management model used the Elastic WLAN mechanism to manage bandwidth optimally for the bandwidth needs of each room or area in the Smart Building system. Testing is done by detecting the number of people detected by IoT with a maximum bandwidth of 30Mbps in two different Laboratories. So that it is obtained in the Telecommunication Lab with 5 visitors obtained bandwidth 7.89 Mbps and in the Computer Lab with 14 visitors obtained bandwidth 22.10Mbps. Furthermore, it succeeds in simulating bandwidth changes dynamically by using a random number generator on the amount of traffic seen from changes in the amount of bandwidth of each class which is directly proportional to the amount of traffic of each class.