Reviewerly/exHarmony huggingface dataset:
A Comprehensive Analysis of Satellite Image Denoising using Earth Observation Data

Satellite images can be affected by noise during image acquisition, transmission, or poor environmental conditions. More precise images are increasingly necessary in both everyday activities and scientific research. Important information is also lost when noise in the image is reduced by attenuating the high-frequency image components. Noise, on the other hand, distorts images, lowering their quality of visual image. Due to this reason, investigations focus on removal of noise that can be done on images to improve image quality. To date, quite a lot of different approaches have been suggested to reduce noise, and each one has a unique set of benefits. To preserve the images' crucial information and enhance their visual appeal, various methods are offered as an efficient denoising technique. This analysis focuses on satellite noise removal using different methodology. As a performance metric, Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are used as metrics.
Advancements in Security of Internet of Things Using Blockchains

Security in the Internet of Things (IoT) is a major challenge due to the diversity of their applications and underlying architectures. There is a lot of research and development related to the security of IoT. Blockchain technology has been an enabler for various decentralized applications and one of its main aspects is in securing the IoT. Many researchers have proposed different schemes to improve IoT security using blockchains. In this paper, we present a survey of the research related to IoT security using blockchain. We focus on schemes, methods, and architectures pertaining to the security of IoTs using blockchains. We compare different schemes based on their features, applications, and underlying techniques.
Deep Neural Networks (DNN) based Sports Balls Classification

In this research study,DNN are used to classify the images.With 30 various varieties of Balls used for this paper, the input data is mostly concentrated on the Balls category. Deep neural networks were chosen as the best alternative due to their high percentage of accuracy during the training phase. The percentage accuracy of image classification is used to describe the results. When the accuracy is around 89%, fine-tuning is used to increase the accuracy to 99% We spent a lot of time understanding the dataset and determining the shape and accuracy of each feature. collected information on 30 different feature types. 5 shapes were trained on each of the 3495 images in the 30 subdirectories for each type of feature.
A Statistical Machine Learning Approach to Optimize Workload in Cloud Data Centre

The modern data centre (DC) is a perplexing amalgamation of various mechanical, electrical, and control frameworks. The increasing number of possible working configurations and nonlinear interdependencies make it difficult to comprehend. To improve Data Center performance and execution, the neural network scheme can benefit from the genuinely active data. Machine Learning (ML) is emerging as the most suitable method of demonstrating DC execution and improving productivity by utilizing the existing sensor data. The data centre infrastructure, the tasks to be completed, and the desired profits are framed as a mathematical programming model, which can then be overcome by using alternative methods of searching for smart task programming. This paper proposes a novel method for maximizing the load on cloud infrastructure. This article has utilized the machine learning techniques to enhance the load balancing. The new proposed algorithm is evaluated and then compared against existing algorithms for analyzing the make span time, time taken to execute tasks, overall load on the virtual machines and total cluster utilization.
Rapid Chaotic-based Image Encryption by Combining Chaotic and Non-Chaotic Scrambling Techniques

Image encryption has been an appealing and exciting area for researchers recently. Many techniques attempt to enhance the security of images for storing in social media. Chaos theory is frequently used for image encryption due to its unpredictable nature. The classical chaotic based encryption methods are complex in structure and difficult to implement for providing high level securities. In order to solve these issues, anew method for encrypting images using Chaotic Maps (CMs) is proposed in this paper. Initially, a Logistic-Sine-Cosine (LSC)based CM is employed to generate various scrambling functions, such as Zigzag transform, Magic confusion, and Row confusion, and then these scrambling functions are used to modify pixel values to identify a linearity element. Then, these adjusted values are mixed with additional arbitrary sequence generated using the Logistic Cubic Cosine (LCC) based CM. Finally, the pre-encrypted images are combined together so that the generated randomness is dispersed uniformly throughout them. The combination of CM with scrambling functions not only improves security but also accelerates the speed of encryption.
Privacy Preservation Using Random Forest For Healthcare Data In Case Of Smart Cities

The healthcare sector all over the world is going through an amount of change that may be considered to be typical. There will be a large and profound shift in the clinical, operational, and financial paradigms, as well as in the economy on a worldwide scale, as a result of the digitization of patient data and health information. In this paper, ML is used as a base aggregator to improve the privacy of healthcare users in smart cities
A Hybrid Approach for Forecasting the Technical Anomalies in Sensor-based Water Quality Distribution Data

The detection of anomalous data from sensor data is the intractable problem in today’s hydrological data monitoring. Hardware malfunctions, power issues, battery life, and efficiency are challenges encountered while employing sensor devices to collect data. In such situations, data that is inconsistent may be recorded. By applying these types of dataset, inaccurate results may be produced when performing classification or other data analytic methods. In order to discover anomalous data from a huge dataset, this paper suggests a hybrid mechanism. Three unsupervised machine learning techniques are used to construct this mechanism. First, this study reduces superfluous data by using Principal Component Analysis (PCA). Isolation Forest (IF) is then used to find outlier scores. Finally, K-means clustering is used to distinguish between abnormal (anomalies) and regular data using a visual representation of cluster assignments. The cluster assessment indices’ criteria were used to evaluate this hybrid approach. According to the findings, this hybrid technique would be suitable for identifying anomalous data inside each data index of the dataset, depending on the target value.
Impact of Electronic Health Record Use on Length of Stay Management in a General Hospital in Saudi Arabia

The implementation of Electronic Health Records (EHR) in hospitals has many benefits and this healthcare technology (HCT) impact the providers, patients and payers in various ways. One of the great benefits is the analysis and investigation of the length of stay (LOS) in hospital based on EHR system. LOS is an important indicator for testing and evaluating the performances of the hospitals. The present study targeted to analyze and investigate LOS in Yanbu General Hospital based on the data retrieved from the database of its Electronic Health Record between the periods of 2018 to 2021. We determined the average LOS in the hospital per month, the average LOS in the hospital per department over the 4 years to define the long-term hospitalization patients per department including LOS analysis in terms of transfer time and pattern. On an average a 65 percent (27,914) of hospitalized patients was discharged in around 4.5 days, and approximately 20 percent (8589) left the hospital within less 3 days, and 15 percent (6441) of patients stayed longer than 4.5 days. The results showed the year of 2018 had the highest average LOS of 7 days. Intensive Care Unit (ICU) had highest average LOS of 16 days and Female Surgical Ward (FSW) had the lowest average LOS of 4 days. We also examined the impact of discharge delay time, transfer time, restored medical history of patients, age, gender, and severity wards on average LOS. Parameters are identified for developing future predictive model based on obtained data using the regression analysis approach and machine learning techniques which is in progress. A successful control of LOS for inpatients would result in care cost reduction, improvement in the quality of treatment, more efficient bed management and lastly control the risk of infection for inpatients.
Comparative Performance Analysis of Mamdani and Sugeno Fuzzy Controller using Application on 2DoF Ball Balancer System

This paper presents the design of Mamdani Fuzzy Inference System and Sugeno Fuzzy Inference System for controlling the ball position of two degree of freedom ball balancer system which is a nonlinear and unstable system with a complex transfer function. Intelligent control technique such as fuzzy helps in efficient control and position tracking of complex nonlinear systems. A comparative analysis has been performed to test the performance of the Mamdani FIS and Sugeno FIS on the ball balancer system for various characteristics such as ball position, ball plate angle and actuation voltage. The time domain specifications such as rise time, peak time, settling time, delay time and peak overshoot have been used as parameters to compare the two fuzzy systems. The simulations are performed using MATLAB/ Simulink environment.
Investigating the Impact of Atmospheric Factors on Solar PV Panel Performance

This paper investigates the impact of atmospheric conditions on the performance of solar photovoltaic (PV) panels. The study includes an analysis of two case studies, in which the voltage, current and power of clean and dusty panels were compared, and the effect of different particulates on the panel efficiency of monocrystalline and polycrystalline solar panels was examined. The effect of varying panel temperature and irradiance was also observed through Current-Voltage and Power-Voltage characteristics. To further demonstrate the impact of atmospheric factors on PV panel performance, Monthly data from a 3.3MWp grid-tied, ground-installed solar photovoltaic plant at Aligarh Muslim University in North India was analyzed. The research also investigates the challenges faced by the PV panels installed in the National Capital Region (NCR) of Delhi, another city located in the northern region of India, which is known for its high levels of pollution. Overall, this study highlights the importance of considering atmospheric factors when evaluating PV panel performance and the need to understand how these factors can impact panel efficiency in order to maximize the output and effectiveness of solar energy systems.
Case Study Regarding Qualified Professional Education

This case study presents an interpretative reflection on a pedagogical experience developed in a public teaching institution, in the capital of São Paulo, Brazil, in the first half of 2022.The unit of analysis is composed of a group of students from a professional course in Marketing, an area of Communication that belongs to the set of Applied Social Sciences.We present as a corpus a set of authorial narratives written by students and published on blogs in cyberspace, along with other communicational works produced during the technical-professional training course.The corpus will be analyzed as content that records individual and collective daily life from the acquisition of knowledge.The question of the problem asks whether the students of the professional qualification course in the field of Communication, which requires the development of writing competence, describe their participation and activity as narrators of the sociocultural context experienced during the course.The objective of the study is to reflect on this student production as part of a knowledge construction process.The content analysis of the authorial narratives revealed the development of writing, reading and oral expression skills, a fact that could facilitate social interaction and presentation of professional projects, as well as, we can observe that the students improved the communication process between them, within and outside of school, enabling an inclusive coexistence that can contribute significantly to the social future of young people.
Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis

In machine learning, multiple instance learning is a method evolved from supervised learning algorithms, which defines a "bag" as a collection of multiple examples with a wide range of applications. In this paper, we propose a novel deep multiple instance learning model for medical image analysis, called triple-kernel gated attention-based multiple instance learning with contrastive learning. It can be used to overcome the limitations of the existing multiple instance learning approaches to medical image analysis. Our model consists of four steps. i) Extracting the representations by a simple convolutional neural network using contrastive learning for training. ii) Using three different kernel functions to obtain the importance of each instance from the entire image and forming an attention map. iii) Based on the attention map, aggregating the entire image together by attention-based MIL pooling. iv) Feeding the results into the classifier for prediction. The results on different datasets demonstrate that the proposed model outperforms state-of-the-art methods on binary and weakly supervised classification tasks. It can provide more efficient classification results for various disease models and additional explanatory information.
Simulation of IoT Smart Home Network with Decision Making Based on MajorDoMo Platform

The purpose of the article is to model a smart home with decision-making and the use of the MajorDoMo IoT platform instead of a cloud platform. The main features of the article are: modeling of the IoT network for controlling the control unit on the MajorDoMo platform, the use of the MajorDoMo IoT platform in the Windows system for making decisions when controlling household appliances (fan or air conditioner) based on the temperature and humidity values received from a smartphone modeling sensors, transmitting the results of controlled data to the user's home page. The structure of the MajorDoMo platform is given, its functions are described, and commands for decision support are considered. The use of product rules is proposed as a decision-making model. An example of such a description for data regulation by the household appliances is given. The user can log into the IoT network and view changes in temperature and humidity in real time, as well as check the regulation of the equipment. The possibilities of the PHP language for the implementation of the control process are presented, in which the household device begins to adjust the parameters when the environmental monitoring system detects that the corresponding data exceeds the set threshold.
Opportunity Cost and Missed Chances in Optimizing Cybersecurity

Opportunity cost should not be an afterthought when making security decisions. One way to ease into considering complex alternatives is to consider the null baseline of doing nothing instead of the choice at hand. Opportunity cost can feel abstract, elusive, and imprecise, but it can be understood by everyone, given the right introduction and framing. Using the approach presented here will make it natural and accessible.
An Answer Verification Approach to Objective Questions in Intelligent Tutoring Systems

An approach to develop a problem solver for automatic answer verification to objective questions in intelligent tutoring systems constructed using OSTIS (Open Semantic Technology for Intelligent Systems) technology is сonsidered. The problem solver is developed based on multi-agent technology within OSTIS. The proposed problem solver automatically verifies the correctness and completeness of the user’s answer at the semantic level by starting the corresponding sc-agent sets according to the type of the questions. The objective question answer verification is one of the main purposes of the developed problem solver, it can also perform the calculation of the similarity between any two semantic graphs.
Ontology-Based Knowledge Acquisition Method for Natural Language Texts

The main task of knowledge acquisition (also named knowledge extraction) from natural language texts is to extract knowledge from natural language texts into fragment of knowledge base of intelligent system. Through the induction of the related literature about knowledge acquisition at a home country and abroad, this paper analyses the strengths and weaknesses of the classical approach. After emphatically researching the rulebased knowledge extraction technology and the method of building ontology of linguistics, this article proposes a solution to the implementation of knowledge acquisition based on the OSTIS technology. The main feature of this solution is to construct a unified semantic model that is able to utilize ontologies of linguistics (mainly, syntactic and semantic aspect) and integrate various problem-solving models (e. g., rule-based models, neural network models) for solving knowledge extraction process from natural language texts.
Deep learning based web service recommendation methods: A survey

Web service recommender systems have a fundamental role in the selection, composition and substitution of services. Indeed, they are used in several application areas such as Web APIs and Cloud Computing. Likewise, Deep Learning techniques have brought undeniable advantages and solutions to the challenges faced by recommendations in all areas. Unfortunately, the field of Web services has not yet benefited well from these deep methods, moreover, the works using these methods for Web services domain are very recent compared to the works of other fields. Thus, the objective of this paper is to study and analyze state-of-the-art work on Web services recommender systems based on Deep Learning techniques. This analysis will help readers wishing to work in this field, and allows us to direct our future work concerning the Web services recommendation by exploiting the advantages of Deep Learning techniques.
THE PROCESSING OF CHILDREN'S PERSONAL DATA IN THE GAMES APPLICATIONS AVAILABLE ON THE GOOGLE PLAY STORE PLATFORM

New technologies allow the intense capture and processing of personal data, a worrying practice especially when users are children and adolescents.In this context of great interconnection, this article aims to analyze the protection of children's personal data in children's game applications, available on the Google Play Store Platform, an analysis that aimed to verify whether developers are following what determines Law 13.709/2018.The research aimed to answer the question: did the companies that develop games aimed at children available on the Google Play Store Platform comply with the General Data Protection Law?The answer to the research problem resulted from the application of the deductive method of approach, combined with a case study, focusing on the Google Play Store Platform.It was concluded that technology can be a great ally in teaching children and, despite the risks arising from its use, the inclusion of devices in the LGPD proved to be an important initiative, evidencing the concern with the protection of personal data.It is necessary to go beyond the normative analysis and the investigation of the game applications showed that among all the applications examined, only one is not fully adequate to what the LGPD determines regarding the processing of children's personal data.
An adaptive incremental TSK fuzzy system based on stochastic configuration and its approximation capability analysis

The aim of this study is to improve randomized methods for designing a Takagi-Sugeno-Kang (TSK) fuzzy system. A novel adaptive incremental TSK fuzzy system based on stochastic configuration, named stochastic configuration fuzzy system (SCFS), is proposed in this paper. The proposed SCFS determines the appropriate number of fuzzy rules in TSK fuzzy system by incremental learning approach. From the initial system, new fuzzy rules are added incrementally to improve the system performance until the specified performance is achieved. In the process of generation of fuzzy rules, the stochastic configuration supervision mechanism is applied to ensure that the addition of fuzzy rules can continuously improve the performance. The premise parameters of new adding fuzzy rules are randomly assigned adaptively under the supervisory mechanism, and the consequent parameters are evaluated by Moore-Penrose generalized inverse. It has been proved theoretically that the supervisory mechanism can help to ensure the universal approximation of SCFS. The proposed SCFS can reach any predetermined tolerance level when there are enough fuzzy rules, and the training process is finite. A series of synthetic data and benchmark datasets are used to verify SCFS’s performance. According to the experimental results, SCFS achieves satisfactory prediction accuracy compared to other models.
Research and improvement of C-means clustering algorithm based on Image segmentation application

The traditional fuzzy C-means clustering technology only considers one performance Angle of image segmentation process when processing data, resulting in low accuracy of image segmentation. In this paper, the traditional FCM algorithm is analyzed, and the low clustering accuracy, noise interference and lack of flexibility and other problems are fully considered from the relationship between parameter components, non-local spatial information elements and noise sensitivity. Firstly, a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably. Secondly, based on the extreme learning machine theory, the non-local spatial information coefficient is introduced to improve the identification ability of the influence factors. This method not only guarantees the anti-noise performance of the algorithm, but also preserves the image data, improving the iteration efficiency and segmentation accuracy of the algorithm. The test results show that the accuracy of the improved C-means clustering algorithm for image segmentation is 95.5%, which is compared with the traditional C-means clustering technique and other optimization algorithms.