Reviewerly/exHarmony huggingface dataset:
A comparative analysis on liver segmentation from CT images -ResNet and VGG

Most of the segmentation of liver in CT images used U-shaped structure. Although the segmentation results of these studies are superior to classical UNet, there is no study comparing the effects of using different encoders in U-shaped structures. In this paper, we compared the use of two classic encoder structures, ResNet and VGG, to form a U-shaped structure for liver segmentation in CT images. The U-shaped structure formed here is based on the classic network structure of UNet and LinkNet, and has been tested on the public dataset 3Dircadb. The experimental results show that using VGG19 encoder combined with Linknet network structure achieved the best liver segmentation results, with a mean Dice 0.963 and a segmentation speed of about 62ms per slice. In addition, LinkNet, which uses the ResNet18 encoder, has the fastest segmentation speed, with approximately 35ms per slice, and its mean Dice value of liver segmentation results is 0.955.
Multi-Authority Attribute-Based Searchable Encryption in Cloud Storage

Ciphertext policy attribute-based encryption (CP-ABE) has been widely used as a fine-grained access control mechanism for data sharing. Existing research on CP-ABE schemes in cloud computing environment predominantly focuses on a single attribute authority, which may lead to high computation overhead or a single point of failure. Besides, the encrypted data stored in the cloud restricts ciphertext retrieval. Therefore, we propose a multi-authority attribute-based searchable encryption scheme in cloud storage called MASEP for sharing data securely. In addition, the MASEP scheme supports the validity of retrieving user attributes before decryption, reducing the unnecessary computation cost. Finally, the experiment results show that MASEP scheme is proved to be IND-CPA secure under the chosen plaintext attack, and it is more efficient and promising.
Designing a Transporter Mobile Robot for the WorldSkills Competition 2022 Using a Finite-State Machine Approach

This paper delves into the progress made in robots in competitive environments such as the WorldSkills Competition (WSC) for Mobile Robotics. The 2022 WSC focused on hospital service robots. It assigned them responsibilities, including sorting medications, delivering items to patient rooms, inspecting medical waste and moving patient gurneys. We introduce a mobile robot that utilizes three omni wheels for seamless movement in all directions. Noteworthy features of this robot include a manipulator arm and gripper for handling objects, a storage space capable of accommodating three medicine cubes and the ability to transport gurneys simultaneously. The robot employs LabVIEW as its programming medium to control its operations within a defined mission area effectively. It is equipped with the myRIO microcontroller. The Finite State Machine (FSM) approach was chosen due to its relevance to the robot's tasks and functions.
Immersive Journalism: A Systematic Review of the Literature in Scopus over the Last Five Years

The present research gathers information about immersive journalism, which was classified into three categories: immersive 360° video reporting, viewer experience and the importance of binaural audio. The study answers the question: What is known about immersive journalism in the Scopus database during the last five years? For the development of the manuscript, information was collected from the SCOPUS database and the PRISMA method was used. The inclusion and exclusion criteria were part of the strategic design for finding papers. As a result, 12 articles were examined. The analysis and systematization carried out concluded that it will be necessary to use a series of digital, technical, and physical resources to create a sensation of realism and include the user in the narrative.
Simulated Sonar Image Generation Method Based on Improved CycleGAN

The stylization method based on CycleGAN network can easily synthesize simulated sonar images when the dataset is small. However, the CycleGAN network does not retain the structure of the real sonar image, resulting in a large gap between the structure and texture of the generated image and the real image. To solve this problem, this paper proposes an improved CycleGAN algorithm. By fusing Unet and Otsu segmentation algorithms, the segmentation effect of sonar image is improved. And by adding SSIM loss, the structural characteristics of the generated image are further limited. Through the evaluation of multiple indicators, it is proved that the algorithm in this paper has improved the image similarity compared with the original CycleGAN, and better retains the structural characteristics of the original image.
Adaptive Evolution Strategy for Symbolic Regression

This paper presents an adaptive evolutionary strategy that combines a genetic algorithm with an evolution strategy to solve symbolic regression problems. Symbolic regression aims to determine a regression model. Although genetic programming has been widely used to solve this problem in the past, it has to choose coefficients from a set of randomly selected constants, which prohibits gradual searching towards optimal or near optimal coefficients. To address this limitation, the proposed technique leverages the strengths of an evolution strategy in evolving coefficients and a genetic algorithm in evolving the rest of functional forms. In each learning step, the evolution strategy gradually adjusts the values of coefficients based on fitness values. Experimental results on symbolic regression problems demonstrate that the proposed technique outperforms traditional genetic programming, with statistically significant improvement demonstrated through a hypothesis test. With 95% confidence, the latter incurs the average error 1.81 times that of our proposed method.
Photovoltaic Power Generation Prediction Based on Graph Neural Network and Superimposed Satellite Cloud Images

A short-term prediction method based on graph neural networks and superimposed satellite cloud maps is proposed for the short-term prediction of photovoltaic power generation. First, a novel PV power prediction model incorporating meteorological data and cloud features is constructed based on satellite cloud map data. Then, the traditional graph convolutional neural network (GCN) is combined with the novel graph convolutional neural network (GCN) for feature extraction and prediction modeling, respectively. In the feature extraction stage, the satellite cloud data is used as the input features; in the prediction modeling stage, the original PV power data is fused with the output of the new GCN network model to complete the short-term prediction of PV power. The experimental results show that the model has high prediction accuracy and strong robustness, and it has better generalization ability and higher computational efficiency compared with the traditional methods.
Flight Dynamics and Dual Front Rotor Tilt Mechanism for a Quad Plane UAV

This paper presents a quad-plane tilt-rotor innovation, representing an advancement in Unmanned Aerial Vehicle (UAV) design. The quad plane boasts improvements in crucial aspects, including motor placement, motor count, airfoil selection, vehicle typology, and wing and tail configurations. One of the standout features highlighted in our study is its ability to conduct mapping designed explicitly for detailed plant health analysis. What sets it apart is its take-off capability that eliminates the need for launch areas and provides unmatched flexibility during deployment. When operating in fixed-wing mode, the quad plane excels in surveillance missions by ensuring data recording is automatically stored on an SD Card to maintain data integrity from take-off to landing. We employ a PID control method further supported by an IMU sensor to ensure stability during flight control. Integrating a GPS guarantees accuracy by processing satellite-derived radio signals to determine latitude and longitude information. With a flight duration of 15 minutes and reliable control outcomes, the UAV is a game changer in aerial surveillance applications. The quad-plane tilt rotor is more than a technological achievement; it has the potential to completely transform agricultural methods in Indonesia, tackling the issues of diminishing yields and rice production. Our research indicates that this unmanned aerial vehicle (UAV) is on the verge of establishing benchmarks in monitoring and analysis.
QVD-Querying Video Databases for Event Related Frames Using Text Keywords

With increasing camera enabled devices huge video data is generated every second. With unlimited storage offered by cloud, most of this video data is moved to cloud storage directly or indirectly. But manually querying such a huge data is challenging for most of the people. We have proposed an indexing method where from tokens of query we can find related frames and videos in a short amount of time. Queries could be very specific e.g. “car accident in area * at time*” Thus video summarization is used to produce short summary of long videos. Manually checking these summaries is again challenging task. This work aims to generate algorithm to query any video database in fraction of time, with few keywords related to video. Aim of generating a comprehensive algorithm is achieved by creating summary of each video. And the aim is also to achieve to Generate caption for each frame and then generating cumulative summary from summarized video frames using LSTM. This is achieved by searching user keywords in these summaries and ranking related summaries using CNN.
Implementation of Burmese Language News Classification System by Using SVM and LSTM Machine Learning Algorithm

With the increasing availability of documents in social media and the rapid growth in the number of data exchange systems, the task of automatically categorizing documents has become a key method for organizing information and knowledge retrieval. News classification is the process of sorting text documents into one or more predefined categories or classes of similar documents. The correct classification of electronic documents, online News, blogs, electronic letters and digital libraries requires intellectual analysis of the text, the use of machine learning and methods of a natural language processing to obtain significant information. The article aims to analyze the methods and methodologies that are used in the classification of text documents. The purpose of the work is to classify news in social media that need to be organized and retrieved information using machine learning methods. In this paper, Experiments were done on Myanmar local and international news dataset using TF-IDF feature extraction techniques and the results are compared with performance of Support Vector Machine (SVM) machine learning algorithm and long short-term memory (LSTM) recurrent neural network.
Hand Gesture Based Food Ordering System

The COVID-19 pandemic has emphasized concerns regarding the presence of microorganism contamination in public environments. Measures aimed at diminishing the transmission of viruses have encompassed strategies like movement limitations, the enforcement of social distancing, mask mandates, and the promotion of hand hygiene. Nevertheless, the challenge of preventing indirect virus transmission through surface contact persists, particularly in locations where individuals interact with potentially contaminated surfaces, such as touchscreen menus in restaurants. Both businesses and the general populace are actively exploring methods to alleviate the spread of germs through surface contact. The primary objective of this paper is to implement hybrid model which uses Convolutional neural network prowess in handling spatial data along with the decision tree is used for attaining efficacy in managing structured data related to food ordering system. Hand gesture-based ordering systems eliminate the necessity to touch shared surfaces physically, such as menus or touchscreen kiosks, thus diminishing the potential for germ transmission.
FedDHr: Improved Adaptive Learning Strategy Using Federated Learning for Image Processing

FL (Federated learning) has grown in popularity as a field of research that allows for the training of an algorithm over many decentralised servers that are having local data samples without requiring data exchange. Numerous application domains (like medical image analysis diagnosis made by doctors) lack a significant amount of properly labeled and full data that can be found in one place. Due to the prevalence of artificial intelligence in new application sectors, there are few rising issues on privacy of data and user. New algorithms of various research works are analysed in this study and it introduces a new technique that address the data heterogeneity and adaptive learning rate of the model to perform Image processing, Deep Learning.
Breast Cancer Prediction Using a Deep Learning Algorithm on the Cloud Medical Data

Recent strides in data generation have given rise to an unprecedented surge in information accumulation, marking the advent of the big data era. Unfortunately, conventional machine learning algorithms find it challenging to adeptly manage the distinctive characteristics presented by big data. This study is centered on the task of breast cancer prediction in the midst of this data-rich landscape. Specifically, we delve into two pivotal data types: gene expression (GE) and DNA methylation (DM).Our objective revolves around unlocking the potential of Deep Learning algorithms, both when applied individually to each dataset and when they come together in synergy. For this purpose, we've elected MATLAB as our primary platform. At the heart of breast cancer prediction, we employ the Convolutional Neural Network (CNN) algorithm, renowned for its proficiency in pattern recognition and feature extraction. Our study is underpinned by a comprehensive analysis that encompasses GE, DM, and the amalgamated GE and DM datasets.Furthermore, we gauge the performance of the CNN algorithm by placing it head-to-head with the Random Forest (RF) algorithm, a widely adopted choice in machine learning for classification tasks. The outcomes of our experiments vividly underscore the superiority of our approach, especially when we harness the potential of the GE dataset. This particular configuration yielded the highest accuracy and the most cost-effective results among all the classifiers considered. These findings serve as a beacon, illuminating the remarkable efficacy of Deep Learning techniques in the domain of breast cancer prediction, particularly when set against the backdrop of big data. This study underscores the remarkable potential of Deep Learning algorithms as they navigate the complex healthcare landscape and promises more accurate, cost-efficient diagnostic tools on the horizon.
A Hybrid Transformer Ensemble Approach for Phishing Website Detection

The internet has developed into a veritable informational gold mine in the era of the digital age, and URLs (Uniform Resource Locator) act as access points to enormous volumes of material. For a variety of applications, including web content classification, fraud detection, and information retrieval, it is essential to extract useful properties from URLs. The proposed approach investigates the potential for extracting useful information from URLs using transformer-based models. Transformers can be configured to decode the underlying patterns and semantics in URLs, which frequently display complicated structures and variations. Due to their exceptional abilities in natural language processing, they are used to effectively encode and represent the properties of the provided URLs. The approach uses pre-trained transformer models like BERT (Bidirectional Encoder Representation from Transformers), optimizing their feature extraction performance on unique URLs. Later, a stacking strategy is used that combines machine learning models to further improve accuracy. Stacking is a potent ensemble approach and makes use of the diverse strengths of various models to provide predictions that are more reliable and accurate. By providing a quick and accurate method to identify phishing websites, the proposed approach makes contribution to field of internet security and protects consumers from potential cyber risks.
Improved Color Attenuation Prior for Kelud Crater Image Dehazing

Images that contain disturbances, such as haze, are very disturbing in the visibility of the image because they experience a reduction in contrast and color. In this study, we implement a system that can remove the effects of haze or noise. Therefore, the method used is expected to remove noise efficiently. The Improved Color Attenuation Prior method uses the Color Attenuation Prior principle. The output obtained is a better visibility quality image.
The Development of FPGA Implementation on Convolutional Neural Network

In this article, we discuss the multifaceted aspects of implementing Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). In view of the rising demands for realtime processing and low-latency machine learning applications, the deployment of CNNs on FPGA presents a promising solution. We scrutinize several existing implementations, focusing on three key components: architectural optimization, memory management techniques, and the computation enhancement technniques. The advantages and limitations of each method are discussed in detail. Furthermore, we explore the unique opportunity provided by FPGAs to locally conduct both feedforward and training processes, promising notable enhancements in data security and privacy. A flexible and robust general structure for FPGA implementation on any CNN is proposed, providing a foundation for future research and development. This article serves as a comprehensive resource, aiming to deepen the understanding of FPGA-based CNN implementations and spur further explorations in this area.
Measuring the Level of e-Commerce Usability in the Electrical and Informatics Engineering Department Using the System Usability Scale

In this study, Tech Zone Plaza conducted a testing phase using the system usability scale method, which is used with the aim of measuring how good a user experience is from software. The results of this test is used as a reference for improving and adjusting the user interface based on the result and input from the test. Testing Tech Zone Plaza e-commerce uses System Usability Scale (SUS) Methods. There are several steps that must be followed in conducting a system usability scale. The steps are divided into 3 parts, Preparation, Execution, and Analysis The overall average SUS score is 74, on a scale of 0 to 100, indicating a moderately positive perception of the system's usability. According to the findings of this study, SUS ratings and individual responder feedback provide essential information for evaluating the system's usability. They serve as critical indications of strengths and shortcomings, highlighting areas for improvement and informing decision-making to improve the user experience. A full knowledge of the system's usability may be obtained by supplementing quantitative data from SUS scores with qualitative input from users, resulting in a system that better matches user expectations and requirements.
Leukemia Cell Image Classification Using CNN: AlexNet and GoogLeNet

Leukemia is a type of blood cancer that often causes worry and uncertainty for sufferers. This study aims to investigate the latest developments in the use of convolutional neural network (CNN) models, especially GoogLeNet and AlexNet, in classifying leukemia cell images. In this study, both models were trained and tested using previously trained leukemia image datasets. The results showed that both GoogLeNet and AlexNet had very good performance in classifying leukemia cell images. Both models achieved 100% accuracy in each training run, demonstrating outstanding ability to identify and differentiate the images. In addition, both models also show equally good results in the assessment using the confusion matrix, including precision, sensitivity, specificity, and F-score. In addition to accuracy, a comparison was also made of the time parameters required in model training. In this case, AlexNet is proven to be more efficient with a shorter training time than GoogLeNet. This shows that AlexNet can be a better choice in terms of time efficiency in model training. This research provides hope in dealing with leukemia by using deep learning technology and convolutional neural network models. The results obtained indicate the possibility of using this model in the diagnosis and classification of leukemia images accurately and efficiently. However, further research is needed to validate and test this model on a larger dataset and consider other factors that may affect model performance.
Text Document Clustering Using the Hybrid Fruit Fly Optimization Algorithm

The Internet produces vast amounts of text data, expanding quickly. The abundance of text data in the unstructured format makes it difficult to extract and analyze the pertinent data. A text-mining technique called text document clustering separates text-based documents into completely independent groupings; consider likenesses between documents inside a group while simultaneously considering fluctuations in happiness between documents from various clusters. Partitioning the variety of text data by evaluating the practicality of the data in the documents is one of the issues tested in text clustering.
Managing the Artificial Intelligence Technologies for the Higher Education Sphere

The article’s main purpose is to identify the principles for the artificial intelligence technologies’ implementation in higher education. The article is largely devoted to the selection of individual algorithms and mechanisms for the transition and implementation of artificial intelligent technologies in the educational environment, taking into account the existing international experience. The basic method for analyzing the identified problem is the synthesis of an algorithm for introducing artificial intelligence technologies into the educational environment, taking into account maintaining the competitiveness of universities. The Beijing Consensus, which is based on the principles of sustainable development of the United Nations, was used as the main reference point for the analysis. Among the general scientific methods used were: analysis, synthesis, generalization, problematization, and the authors also conducted a study using the case study method. The article results in a step-by-step algorithm for introducing artificial intelligence technologies into the educational environment, taking into account the requirements of modern technologies.