Reviewerly/exHarmony huggingface dataset:
Compression of Medical Images using Progressive Coders

We have considered, in this paper, two compression methods based on wavelet transform and progressive coding, called EZW (Embedded Zerotree Wavelet) and SPIHT (Set partitioning in hierarchical trees). These techniques provide the opportunity to significantly increase compression ratios while preserving high qualities of reconstructed medical images. Unlike the EZW, the SPIHT approach takes advantage of subband correlations at near and far resolution levels (father-son relationships). A partial ordering by amplitude of the wavelet coefficients of the DWT (Discrete Wavelet Transform), partitioning in hierarchical trees, and scheduling of the transmission of the refinement bits are the three ideas used by SPIHT (the amplitude of each significant coefficient is progressively refined). The goal is to create a digital tool with the required main compression rate and PSNR (Peak Signal to Noise Ratio) limitations for compressing medical images. A development of the two cited algorithms has been carried out. For their evaluation, several medical images from two databases were used. The results prove that these algorithms are very efficient. Also, it has been found that the obtained results with SPIHT method greatly exceed those provided by EZW approach. SPIHT approach is very efficient compared to EZW from the point of view of the compression ratio and the image quality.
Performance Analysis of Twin-Support Vector Machine in Breast Cancer Prediction

Breast cancer has become a major leading cause of death and incapacity worldwide. Recently, breast cancer is being responsible for a huge number of deaths of the female gender. In this study, we have implemented the Twin-Support Vector Machine (TW-SVM) to illustrate the power of machine learning techniques. TW-SVM is a recently developed algorithm and yet it is very powerful. For performance measurement, a competitive comparison between the proposed TW-SVM and SVM classifiers has been done based on the WDBC dataset. The results showed that TW-SVM can provide promising performance rates. It outperformed the SVM algorithm as well as other existing works by achieving the highest accuracy of 99.11% for predicting the considered disease.
Performances Evaluation of Automatic Authorship Attribution on Ancient Arabic Documents

Authorship Attribution is a research area concerned with the automatic classification of text documents based on their authors. The principal objective of this investigation area is to discover the author of a given text is. This task becomes very tough as far as old text documents are concerned. In this paper, we attempted to broach the Authorship Attribution problem as it applies to old text documents. For this purpose, several experiments are conducted and their results are commented. In order to validate the performances of our system, we constructed a special dataset that we called ''10AAP '' (10 Ancient Arabic Philosophers), by quoting texts from the works of 10 ancient Arabic philosophers, where the topic of the different texts is the same. Moreover, the genre of the authors is also the same.
A framework for predicting course time duration using mapreduce and K-means

With the upcoming of new innovations, new developed patterns are brought into the educational framework which brings about enormous information which is unordered, and it is likewise, a test for understudies to allude to those scholastic courses which are useful in their modern preparation and expands their vocation possibilities. Another test will help to change over the unregulated information into organized and significant data there is a requirement for Data Mining Techniques. The appropriate file system is utilized to hold a large amount of information and allows to reduce the over storage of unregulated data. Information extricated utilizing MapReduce is useful in decision making for understudies for deciding courses picked to undergo modern preparation. In this paper, with the help of MapReduce and K-means algorithms we sort the data into their respective time durations from which the users can choose their courses based on the required time duration in which they are willing to complete the courses. Here, utilizing Distributed File System (DFS), errands run over MapReduce, and the result is obtained after aggregation. Our implementation allows users to choose their subject of interest with the best possible time duration they require to complete the course.
Iterative Association of Isolated Homogeneous Image Regions using the Minimum Distance between Them

The proposed algorithm iteratively combining isolated homogeneous regions based on the search for the minimum distance between the centers of these regions. The algorithm is works to combine isolated regions in bigger region and still combines regions until match the ending conditions of the algorithm, reached the maximum number of loops or a segment region and the minimum distance between the segments, or the number of segments. It has been established that the image size from 10000 to 250000 pixels searches the minimum distance between the segments based on the gradual increase in the search radius it provides a win in the performance of the segmentation algorithm.
Quantum Machine Learning and Quantum Communication Networks: The 2030s and the Future

The two emerging technologies are machine learning and artificial intelligence, and as we all know, quantum computing is one of the most revolutionary developments in technology. Researchers are considering combining machine learning and quantum computing with the advancements in both fields. As a result, quantum machine learning-a fusion of these two fields-has evolved. It has the capacity to effectively address a wide range of real-world issues. Both fields will surely benefit from the combined results of the two fields. When the potential of quantum principles and its peculiarities is employed with machine learning, quantum machine learning reaches a very advanced level. Now that quantum concepts are being incorporated, traditional machine learning algorithms like SVM, PCA, and KNN are being reviewed as QSVM, Q-KNN, and QPCA, which are the most effective and powerful techniques for quantum machine learning. Quantum machine learning is a boom for the future; it will soon vastly surpass even the most advanced neural networks, deep learning systems, and machine learning systems of today. Modern machine learning is quicker than classical computing thanks to quantum machine learning, which is concerned with quantum software. Quantum data from an artificial quantum system is what QML is based on. In this paper, we will also study about the future network that is quantum network, which will be use by replacing classical networking. By enabling quantum communication between any two sites on Earth, a quantum internet is intended to fundamentally advance internet technology.
Online Evasive Strategy for Aerial Survey using Sierpinski curve

This paper deals with the aerial survey of a closed region using the Space-Filling curve, particularly Sierpinski curve. The specified region is triangulated, and the Sierpinski curve is used to explore each triangular region. The entire region may have one or more obstacles. An algorithm is presented which suggests evasive maneuver if an obstacle is detected. The algorithm is online; that is, it does not require prior knowledge of the location of obstacles and can be used while the robotic system traverses the designated path. The fractal nature of the Sierpinski curve and simple geometric observations were used to formulate and validate the algorithm. The non-uniform coverage and multiple obstacle problems are also dealt with towards the end.
Flight Guidance for Vision-Augmented Automatic Landing of Electric Vertical Take-off and Landing Vehicles

The rising interest in urban air mobility and electric vertical take-off and landing (eVTOL) vehicles brings both new possibilities and challenges to the aviation industry. To increase efficiency and reduce costs for urban air mobility operations, the aircraft will operate in a highly automated environment. In this paper, we present an experimental flight guidance system developed for conducting flight tests with a prototype eVTOL vehicle. The flight guidance logic compromises three hierarchical levels and processes input data from an external tactical collision avoidance system and a vision-based integrity monitor, which ensures that performance-based navigation requirements are satisfied. The trajectory selection module finds an energy-optimal path via pre-defined trajectories during flight. The presented system is developed according to an in-house requirements- and model-based process.
Lattice Constant Prediction of Complex Cubic Peroveskite A<sub>2</sub>BCO<sub>6</sub> using Extreme Learning Machine

Double perovskite oxides have received a lot of interest in the last ten years because of their distinctive and adaptable material properties. Among the six parameters in the cubic structure, the lattice constant is the sole changeable parameter, which plays an important role in developing materials for particular technological applications and distinctively identifies the crystal structure of the material. In this paper, the extreme learning machine (ELM) is used to correlate the lattice constant of $A_2^{ + 2}BC{O_6}$ cubic perovskite compounds, such as their ionic radii, electronegativity, oxidation state, and lattice constant. We investigated 147 compounds with lattice constants between 7.700 and 8.890Å. The prediction method has a high level of accuracy and stability and provides accurate estimates of lattice constants.
Flash LiDAR-Based Super-Resolution Mapping for Small Solar System Body Exploration

Safe spacecraft navigation on small solar system bodies requires an accurate and high-resolution representation of the surface. This work develops a super-resolution mapping algorithm based on flash LiDAR measurements for unstructured terrain. It integrates a novel approach of processing multiple range values per pixel per measurement to increase the measurement redundancy and thus the map accuracy. The proposed algorithm is thus able to overcome the limitations of the low flash LiDAR resolution. Micro- and macro-motion strategies are recommended to guarantee subpixel displacements crucial for the super-resolution algorithm, and measurement constraints are introduced to ensure high measurement quality. Computational experiments based on an advanced Flash LiDAR model in a realistic simulation environment of comet 67P/Churyumov-Gerasimenko compare the resulting digital elevation model (DEM) with a baseline DEM and demonstrate the advantages of our algorithm.
Geometric Collision Avoidance Algorithm for Uncrewed Flight Vehicle

Uncrewed Flight Vehicle (UFV) for a particular mission should move from an initial point to a target point along a precogitated nominal trajectory following some waypoints. Sometimes, it may face some obstructions during the journey. There is a probability of the collision between obstacles and vehicles along their trajectory. It should have some mechanism to avert this kind of unintentional collision. All the sensing and avoidance methodologies on board should be autonomous as the information of the intruder is not available a priori to the vehicle. From the practical point of view, obstacles are available in different geometrical shapes (square, triangular, circular, and polygonal), kinematics (static and dynamic), and numbers (single and multiple). The proposed geometric collision avoidance approach described in this paper encompasses the avoidance of all types of obstacles mentioned above.
Waypoint Navigation of Quadrotor using Deep Reinforcement Learning

This paper proposes a Reinforcement Learning (RL) based technique to develop a simple neural network controller for the task of waypoint navigation in quadrotors. In this paper, the application of Twin Delayed Deep Deterministic (TD3) Policy Gradient algorithm for high and low-level control implementation for quadrotors is discussed. The proposed methods are tested on high fidelity Gym-Pybullet-Drones simulator. The effectiveness of the methods developed is validated through numerical simulations. The simulation results indicate that both control policies are successful in navigating through the assigned waypoint, with the low-level controller being accurate in the nominal flight conditions. In the presence of disturbance inputs, the high-level controller performs better when compared to the low-level controller.
Boundary Surveillance Using a Novel Target Switching Technique

The paper focuses on the analysis of different kinds of patterns that have been generated using an autonomous unicycle agent. This unicycle agent considers only range information for controlling its trajectory. Using this range information the proposed controller controls the angular speed of the agent while keeping its forward speed constant. Based on the revolution of the agent around a stationary target point the paper discusses two different types of annular patterns. This paper also implements a novel target switching strategy called mid-point switching for both types of described annular patterns. The proposed switching strategy found application in a wide range of areas such as landmine de-mining, exploration, area coverage, etc. One of the key applications is surveillance around regular/irregular-shaped boundaries. We have explored this application to demonstrate the utility of the proposed switching technique. We have simulated the implemented approach of switching targets and its application in boundary surveillance to verify the results that we have found.
Geofencing for Optionally Piloted Aircraft Through Automatic Execution of Smooth Evasive Maneuvers

The paper proposes a method for generating and automatically executing smooth evasive maneuvers which allow to enforce a geofence for an optionally piloted fixed-wing aircraft. Feasible trajectories are planned online based on the current aircraft state and performance limitations, including finite roll rates. Collision checks are continuously carried out in the background to take over control from the pilot before violating the geofence becomes inevitable. The system automatically executes an evasive maneuver before giving back control to the pilot. The approach can readily be applied to the general collision avoidance problem for convex obstacles. We validate the feasibility and geofencing accuracy of the proposed framework by showcasing results of flight tests of an unmanned, fixed-wing aircraft.
Airborne Multi Target Track to Track Fusion of Radar and IRST for Advanced Multi Role Combat Aircrafts

This paper presents multi sensor fusion architecture to fuse multi target tracks from the Radar and an Infrared Search & Track (IRST) system. The proposed architecture uses global nearest neighborhood (GNN) algorithm for data association between target tracks of Radar and IRST. Further a GNN based central track manager is proposed to maintain the fused tracks from Radar and IRST. The Radar track outputs are modelled in Cartesian coordinates, providing the target position and velocity states with their corresponding covariance, while the radar detections are in polar coordinates. The IRST track output provides angle only information of the targets with their corresponding covariance. Simulations are carried out in single aircraft - multi target scenario and it is observed that the fused track output has improved angle accuracy with lesser variance.
Implementation Platforms and Strategy for the Knowledge Discovery from the Data

Information Management and Information Engineering are two important concepts in recent years. Knowledge Engineering is part of a system engineering program that addresses the needs of an uncertain process by emphasizing the acquisition of Information about a particular process and representing this information in an information-based system. The use of such technologies enhances and helps to analyze the various statements and feelings made by different services or institutions in order to better recognize the needs of the recipients, which can also be referred to as feedback from the recipient. The purpose of this paper is to discuss how and where to use these programs.
Blind Video Inpainting Quality Assessment

To our best knowledge, there are no objective methods for Blind Video Inpainting Quality Assessment BVINQA. Given the cost of subjective quality assessment, it is essential to provide objective assessment methods. Existing methods dedicated to still images could not be used for video images, since the artifacts depending on the movement of objects are not taken into account. Therefore, we propose to develop a BVIN-QA by considering both spatial and temporal aspects of videos and by extracting frame-specific spatial and temporal quality scores. The overall score is provided by using a support vector regression SVR, fed by frame score statistics and trained using subjective MOS scores.The proposed method has been tested on a set of inpainting videos using several kinds of masks and the most known inpainting methods. The obtained results are very promising.
Underwater Image Dehazing using Dark Channel Prior and Filtering Techniques

The enhancement of underwater imaging is essential for ocean engineering research and technology. Due to the increasing number of vision applications underwater, haze reduction methods have become increasingly significant. Scientists have recently been particularly eager to investigate the strange undersea environment. Haze in an image is problematic for many computer vision and graphics applications because it reduces scene visibility and visual quality. Absorption and air light cause a haze to appear. Attenuation lessens sharpness, and air light makes the landscape seems whiter. This study examines a method for haze removal from underwater images in order to enhance them and raise their quality. The method suggested makes underwater pictures clearer, which is important for research and improvement in the underwater world.
Mitosis Detection in Breast Cancer with Deep Learning: A New Approach

In this work, we propose a new approach for spotting mitoses in breast cancer histology images. This new approach involves integrating two publicly accessible datasets following a normalization procedure of color. The mitotic samples are then enhanced by preserving the context to address class imbalance. After this, the candidate mitotic cells are classified into the target classes using a ResNet classifier. Through this method, we were able to accurately identify mitosis in the combined dataset while attempting to identify it in the images. We demonstrate that our method outperforms all current methods by comparing it to state-of-the-art methods using a public dataset. Our results indicate that the proposed technique can be used to automatically identify mitotic cells in the images of histopathology of breast cancer.
Analyzing Textual Documents Indexes by Applying Key-Phrases Extraction in Fuzzy Logic Domain Based on A Graphical Indexing Methodology

Identifying and retrieving key phrases from a given corpus of textual documents is one of the fundamental problems of natural language processing, it is a vital subtask in the text summarization and comparison domain; through which we can obtain the most relevant set of phrases that approximately describe the content of a given document. In our manuscript, our principal objective will be introducing the approach of fuzzy-logic in the key phrase's indexing domain by proposing a new fuzzy logic-based methodology which evaluates the generated key phrases. We conduct a thorough empirical study of this methodology on a new textual document's corpus by identifying the degree of relevance of a key phrase linked to another one whether it is in the same corpus of textual documents or in two further fully distinct textual documents in the identical corpus.
Predicting examinee performance based on a fuzzy cloud cognitive diagnosis framework in e-learning environment

The score profiles could be used to measure learners' skills proficiency via cognitive diagnosis models (CDMs) for predicting their performance in the future examination. The prediction results could provide important decision-making supports for personalized e-learning instruction. However, facing the possible complexity of skills, the uncertainty of learners' skill proficiency and the large-scale volume of score profiles, the existing CDMs have limitations in the measurement mechanisms and diagnostic efficiency. In this paper, we proposed an approach based on a fuzzy cloud cognitive diagnosis framework (FC-CDF) to predicting examinees' performance in e-learning environment. In this approach, the normal cloud models (NCMs) are utilized innovatively to measure the expectation, degree of variation and variation frequency of learners' skill proficiency, and each NCM is transformed into an interval fuzzy number to characterize the uncertainty of the skill proficiency for every learner. Combining the educational psychology hypothesis with the parameter estimation method, we could obtain the learners' skill proficiency level and the slip and guess factors relevant to each test item, based on which the learners' scores could be predicted in a future test. Finally, the experiments demonstrate that the proposed approach provides good accuracy and significantly reduces execution time for predicting examinee performance, compared with the existing methods.
Efficiently Learning Relative Similarity Embeddings with Crowdsourcing

Social scientists often investigate human reasoning by collecting relative similarity judgements with crowdsourcing services.However, this often requires too many human responses to be practical for large experiments.To address this problem, we introduce software called Salmon, which makes intelligent choices on query selection (aka active machine learning or adaptive sampling) while collecting relative similarity judgments from crowdsourcing participants.Salmon is usable by experimentalists because it requires little to no programming experience and only requires an Amazon AWS account for launching (though a local install is available).Extensive simulations and experiments suggest that Salmon requires 2 to 3 times fewer response than random sampling.
Computational simulation of organic pollutant removal from wastewater by different porous boron nitride (PBN) nanoclusters

A computational modeling framework was developed to evaluate the adsorption behavior of boron nitride (BN) for separation of a solute from solution. Adsorption of an organic dye onto the surface of different porous BNs at two different temperatures was computed using different machine learning models. The amount of dye concentration changes in the solution based on the time parameter for different materials has been modeled and validated in this research. An attempt was made to construct the kinetics of adsorption using the developed models rather than the conventional kinetic models. A decision tree (DT), Multilayer Perceptron (MLP), and Linear Regression boosted with Adaboost (ADA-LR) are selected models that are optimized by their hyper-parameters using a grid-search. Models were developed separately for low and high temperatures for three different materials with different chemistry and structures. Using the MSE parameter, DT has an error of 1.44E-02 for low temperatures and 1.48E-02 for high temperatures. It is 1.01E-03 for low and 7.44E-03 for high temperatures using MLP method of simulation. However, in terms of MSE, the best model is ADA-LR, with 4.60E-03 for low temperature and 1.81E-03 for high temperature. With other metrics also, ADA-LR is the best model, so it is selected as the main model of this research. The R2-score of the final model is 0.940 for low and 0.973 for high temperature values.