Reviewerly/exHarmony huggingface dataset:
Grade It: A Quantitative Essay Grading System

Automated writing evaluation system that employs automated essay scoring technologies generates a rating to the writings which helps students with self-assessment. It provides an efficient and easy way for grading essays, which usually takes abundance of time to be evaluated by human graders; this can be greatly useful for educational institutions like schools and colleges. It provides ratings for essays based on the grammatical errors and topic relevancy by using specific tools implemented in the auto grading system. The generated result will help students grading essays which helps students in self-assessment, this will be useful for the student to understand the mistakes that he makes which will be pinpointed by the system in matter of time, not only that the system also displays the strengths of the user, but also the system will improve the accuracy compared to already existing automated grading system. The system uses scores from multiple features (handcraft features, coherence score, prompt-relevant score, and semantic score). These key features illustrated above will provide enough information on the essay for the student.
Metaheuristics Algorithm-Based Minimization of Communication Costs in Federated Learning

The Federated learning (FL) technique resolves the issue of training machine learning (ML) techniques on distributed networks, including the huge volume of modern smart devices. FL clients frequently use Wi-Fi and have to interact in unstable network surroundings. However, as the present FL aggregation approaches receive and send a large number of weights, accuracy can be decreased considerably in unstable network surroundings. Therefore, this study presents a Quantum with Metaheuristics Algorithm Based Minimization of Communication Costs in Federated Learning (QMAMCC-FL) technique. The presented QMAMCC-FL technique is designed a federated hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model with a quantum Aquila optimization (QAO) algorithm. The QMAMCC-FL technique upgrades the global model via weight collection of the learned model, which is commonly used in FL. The proposed model can be employed to increase the performance of network communication and reduce the size of data transmitted from clients to servers such as smartphones and tablets. The experimental analysis of the QMAMCC-FL approach is tested, and the outcomes show better performance over other existing models.
Machine Learning Analysis on Predicting Credit Card Forgery

Occurrences comprising of credit card fraud arise frequently and result in spending a huge amount of fund. Online credit card transactions nowadays consist of a major part of various transactions driven online, that has remarkable expansion. Thus, banks and different financial institutions impart important and desirable credit card fraud detection programs. Fraudulent transactional activities can have various patterns and comprise of various types. The four major fraud schemes in real-life transactions are the subject matter of this investigation. A majority of machine learning algorithms are utilized to handle every scam, and the optimal technique is finally selected after scrutiny. This research suggests an in-depth guide for selecting the superior approach for the type of frauds, and the authors furnish a suitable performance metric to demonstrate the assessment. Real-world credit card fraud detection is a further important subject that the authors represent in this paper. To decide if a certain transaction is legal or deceitful, the authors apply predictive analytics conducted by the unified machine learning approaches and an API package. The authors assess an unparalleled technique which conveniently rectifies the skewed distribution of the data also. In accordance to a confidential declaration treaty, the bank supplied the data for this research.
Attentive Multimodal Fusion for Optical and Scene Flow

This letter presents an investigation into the estimation of optical and scene flow using RGBD information in scenarios where the RGB modality is affected by noise or captured in dark environments. Existing methods typically rely solely on RGB images or fuse the modalities at later stages, which can result in lower accuracy when the RGB information is unreliable. To address this issue, we propose a novel deep neural network approach named FusionRAFT, which enables early-stage information fusion between sensor modalities (RGB and depth). Our approach incorporates self- and cross-attention layers at different network levels to construct informative features that leverage the strengths of both modalities. Through comparative experiments, we demonstrate that our approach outperforms recent methods in terms of performance on the synthetic dataset FlyingThings3D, as well as the generalization on the real-world dataset KITTI. We illustrate that our approach exhibits improved robustness in the presence of noise and low-lighting conditions that affect the RGB images.
Applied Computational Intelligence for Breast Cancer Detection

One of the most widespread illnesses worldwide among women is breast cancer. A quick, proper diagnosis is essential in rehabilitation and recovery. However, because of different difficulties in mammography detection, it is not an easy process. Techniques of applied machine learning can produce early detection tools, significantly improving survival rates. This study analyses three of the most often used machine learning (ML) approaches for identifying breast cancer: support vector machines (SVM), random forests (RF), and Bayesian networks (BN). This study's findings provide a summary of current ML techniques for breast cancer detection. Random forest generated the best performance with 96.4% accuracy when normalisation and feature weighting are considered.
IPSO-SMOTE-AdaBoost: An Optimized Class Imbalance Strategy Using Boosting and PSO Techniques

The class imbalance is challenging issue in machine learning and data mining especially health care, telecom sector, agriculture sector, and many more (Zhu et al. in Pattern Recogn Lett 133:217–223, 2020; Thabtah et al. in Inf Sci 513:429–441, 2020). Imbalance of data samples across classes can arise as a result of human error, improper/unguided data sample selection, and so on (Tarekegn et al. in Pattern Recogn 118:107965, 2021). However, it is observed that applying imbalanced datasets to the data mining and machine learning approaches, it retains the biased in results which leads to the poor decision-making ( Barella et al. in Inf Sci 553:83–109, 2021; Zhang et al. in ISA Trans 119:152–171, 2021; Ahmed and Green in Mach Learn Appl 9:100361, 2022). The primary motivation for this research is to explore and develop novel ensemble approaches for dealing with class imbalance and efficient way of retrieving synthetic data. In this paper, an ensemble method called IPSO-SMOTE-AdaBoost is developed to solve the class imbalance problem by combining the synthetic minority oversampling technique (SMOTE) (Gao et al. in Neurocomputing 74:3456–3466, 2011; Prusty et al. in Prog Nucl Energy 100:355–364, 2017), improved particle swarm optimization (PSO) (Yang et al. in J Electron Inf Technol 38:373–380, 2016), and AdaBoost. AdaBoost combined with SMOTE provides an optimal set of synthetic samples, thereby modifying the updating weights and adjusting for skewed distributions. The typical AdaBoost approach, on the other hand, consumes far too many system resources to avoid redundant or ineffective weak classifiers. With the proposed ensemble framework, IPSO-SMOTE-AdaBoost, parameters can be re-initialized to counter the concept of local optimum as well with the SMOTE that is boosted with AdaBoost method. The proposed method is validated using three datasets on six classifiers: extra tree (ET), naive Bayes (NB), random forest (RF), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). After that, the IPSO-SMOTE-AdaBoost is compared to the existing SMOTE-PSO. The evaluation of proposed work is done with measures, namely accuracy, precision, recall, sensitivity, and F-score, and result shows that the proposed technique outperformed the usual PSO and SMOTE variations.
Facial Recognition System Using Transfer Learning with the Help of VGG16

Facial recognition is one of the most important and widely studied topics in the field of Computer Vision and Artificial Intelligence. The ability of automation to detect and verify a person’s face using photography is a very important factor in a number of domains: monitoring, device access control (laptop and mobiles), security, tracking, law enforcement, biometrics, information security, smart cards, surveillance systems, and so forth. Various applications help to identify specific people in specific areas that help to find intruders. Real-time recognition is necessary for surveillance purposes. This chapter introduces an optimized and efficient facial recognition system with a very advanced face recognition algorithm. This proposed methodology is processed into five main stages: (i) Data Collection, (ii) Data Cleaning, (iii) Fine-tuning of the VGG16 model, (iv) Model Training, and (v) Performance Checking. The first step is collecting face images of different people. In the second step, face images are cropped and saved to remove unnecessary, irrelevant, or meaningless data (noise). In the third step, a very popular image recognition model, VGG16, is fine-tuned for the specific case of facial recognition with the help of a Convolutional Neural Network. The proposed work is implemented using a python module named Keras. Lastly, performance is measured using a testing dataset that is different from the training dataset.
An Optimized Path Selection Algorithm for the Minimum Number of Turns in Path Planning Using a Modified A-Star Algorithm

Robots are becoming more prevalent in everyday life. The most difficult aspect of using a robot is defining its path. Path planning describes the movement of the robot from the starting point to the goal point. This research provides an improved A-star method for a mobile robot’s path-planning finding capabilities in challenging maps. The improved A-star algorithm makes use of the A-star algorithm’s properties. First, the grid surface model is built, and the modified A-star algorithm’s evaluation function is calculated. Second, the acquired multiple least-distance paths were further analyzed using a global minimum number of turns approach, which can speed up convergence and smoothen the global path. In the combinatorial selection of the optimum path from the set of the shortest paths, the path with the minimum diversion of the robot from the initial to the final position is used. It enables the robot to travel the greatest distance possible in a single spin. The straight path enables the robot to jump and cover the most amount of ground in a single leap while keeping a consistent pace. MATLAB was used to run the experiments on a specific grid area.
Recognition of Handwritten Digits Using Convolutional Neural Network in Python and Comparison of Performance for Various Hidden Layers

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic change in the field of machine learning by making it more artificially intelligent. Deep learning is used in various fields because of its wide range of applications such as surveillance, health, medicine, sports, robotics, and drones. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that combine Artificial Neural Network (ANN) and keep updating deep learning strategies. It is used in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the change of accuracies of CNN to classify handwritten digits using various numbers of the hidden layers and epochs and to see the difference between the accuracies. For this performance evaluation of CNN, we performed our experiment using the Modified National Institute of Standards (MNIS) and Technology (MNIST) dataset. Further, the network is trained using the stochastic gradient descent and the backpropagation algorithm. Deep learning is a machine learning technique that teaches the computers to do what comes naturally to the humans.
Trusted Execution Environment

Abstract Trusted execution environments are secure areas of central processors or devices that execute code with higher security than the rest of the device. Security is provided by encrypted memory regions called enclaves. Because the environment is isolated from the rest of the device, it is not affected by infection or compromise of the device. Trusted execution environments have applications for different usages, such as mobile phones, cloud data processing, or cryptocurrencies. Furthermore, since Trusted execution environments are part of a standard chipset, this inexpensive technology can be leveraged across many devices, resulting in increased security, especially in the mobile sector and IoT products.
GUI Integration and Virtual Machine Constructions for Image Processing: Phenomenological and Database Engineering Insights into Computer Vision

This chapter will be focused on image processing and computer vision. We will consider requirements for a hypothetical virtual machine whose intent is consolidation of access to image analysis functionality by exposing sets of computer vision algorithms through a common interface. We anticipate the image-related discussion by reviewing potential virtual machine features that are not unique to image processing but may be especially relevant to that domain, such as recognizing different varieties of number pairs as distinct built-in types (to represent, for instance, image locations/coordinates and/or geometric lengths, or pairs thereof). We also consider functionality that becomes consequential insofar as GUI front ends are engineered alongside image-processing technology—either through user-guided algorithms (such as interactive segmentation) or through interactive visualization wherein users review workflow architectures, intermediate results, color histograms (and other special-purpose GUI components), or comparisons between distinct processing algorithms and similar tools helping users select and fine-tune image analysis techniques for specific image series. By way of providing an example or case study for algorithms that may be integrated with virtual machines or application GUIs, we summarize some facets of a novel database-related image format and explain the mathematical background of its color models and in-memory layout for image data.
Golden Jackal Optimization with Deep Learning-Based Anomaly Detection in Pedestrian Walkways for Road Traffic Safety

Road traffic safety discusses the procedures and measures utilized for preventing road users from being dead or critically injured. Archetypal road users contain horse riders, cyclists, pedestrians, vehicle passengers, motorists, and passengers of on-road public transport (mostly buses and trams). Anomaly detection in pedestrian pathways is a crucial investigation topic, generally utilized for improving pedestrian safety. Because of the varied consumption of video surveillance methods and the improved quantity of captured videos, the typical manual analysis of labeling abnormal proceedings is a tiresome task, thus, an automated surveillance method in which anomaly detection develops important betwixt computer vision researchers. At present, the progress of deep learning (DL) algorithms has obtained important interest in distinct computer vision procedures. Therefore, this article introduces a new Golden Jackal Optimization with Deep Learning-based Anomaly Detection in Pedestrian Walkways (GJODL-ADPW) for road traffic safety. The presented GJODL-ADPW technique aims to effectively recognize the presence of anomalies (such as vehicles, skaters) on pedestrian walkways. In the presented GJODL-ADPW technique, Xception methodology was exploited for effective extraction feature process. For optimal hyperparameter selection, the GJO algorithm is utilized in this study. Finally, bidirectional long short-term memory (BiLSTM) approach was employed for anomaly detection purposes. A widespread experimental analysis is performed to examine the enhanced performance of the GJODL-ADPW system. A detailed comparative analysis demonstrated the enhancements of the GJODL-ADPW technique over other recent approaches.
Optimal and Virtual Multiplexer Resource Provisioning in Multiple Cloud Service Provider System

In today’s digital world, user needs have been diversified in accessing the resources to solve the computing problems. In cloud computing, resource provisioning allocates the resources based on user requirements. Dynamic resource allocation distributes load among virtual machines (VMs). There is an uncertainty in demand for the resources, and resource price must be considered in provisioning VMs. Cloud infrastructure providers are providing a wide range of computing services with different pricing models based on the VM instance types required by the users. Identifying the adaptable resources for the required applications is an optimization problem that can be addressed through provisioning of resources at the offered price. Therefore, there is a need to develop a prediction-based resource management for unpredictable resource demands in terms of both cost and performance. However, we are proposing a multi-cloud broking system that manages the utilization of resources and optimize the cost of resource provisioning and delivery of cloud services cost effectively using stochastic programming and linear regression. It aims to maximize performance and minimize the cost of utilizing the resource. The developed stochastic-based resource provisioning prediction management (SRPPM) increases the performance which is based on linear regression and used to compare the proposed approach optimal and virtual multiplexer resource provisioning (OVMRP). So, based on SRPPM, the proposed OVMRP approach uses linear regression for resource prediction, applies multiplexing on the pool of VMs based on the capacity of VMs, and uses Bayesian theorem to find the least cost VM based on their workload characteristics. The evaluation result shows the proposed algorithm OVRPM accomplishes better performance than the other existing ones.
Secure Positioning and Localization

Abstract Secure positioning and localization refer to the use of technology to accurately determine the location of an object or person in a secure and trustworthy manner. It involves GPS, Bluetooth, Wi-Fi, and other wireless technologies to determine location. Several methods have been proposed to address this problem by securing the content of the signals cryptographically. Distance bounding and timed efficient stream loss-tolerant authentication are two leading examples. Furthermore, with autonomous vehicles becoming increasingly essential and utilized in all domains (land, water, air), secure and robust positioning and navigation capabilities will be crucial.
Underwater Image Enhancement and Restoration Using Cycle GAN

Underwater image augmentation is a technique for recovering low-resolution underwater photographs to produce equivalently high-resolution images. Deep learning-based techniques for enhancing photos commonly use paired data to train the model. Another crucial issue is how to effectively keep the fine details in the improved image. In order to address these problems, providing a revolutionary unpaired underwater picture-enhancing technique that fixes the underwater photos using a cycle generative adversarial network. At last test results on two datasets of unpaired underwater image datasets showed the recommended model's utility by surpassing cutting-edge image enhancement methods. The CycleGAN generator includes a content loss regularizer that retains the important information of one low-resolution image in the corresponding clear image that is generated. The results are given based on mean square error and mean absolute error after the findings have been optimized using the ADAM optimizer. This architecture has a 4 × 4 kernel and a 2 × 2 stride, using Adam as the optimizer and a learning rate of 0.002.
Texture Synthesis Based on Aesthetic Texture Perception Using CNN Style and Content Features

We propose a texture synthesis method that controls the desired visual impressions using CNN style features and content features. Diversifying user needs has led to the personalization of products according to individual needs. In the custom made garment service, users can select and combine fabrics, patterns, and shapes of garments prepared in advance to design garments that meet their tastes and preferences. Furthermore, controlling the visual impressions will enable the service to provide designs that better match the user’s preferences. In image synthesis, controllable texture synthesis have been performed with style and content, however, few previous studies have controlled images based on impressions (including aesthetics). In this study, we aim to synthesize textures with desired visual impressions by using style and content features. For this purpose, we first (1) quantify the affective texture by subjective evaluation experiments and (2) extract style features and content features using VGG-19 from pattern images for which evaluation scores are assigned. The explanatory variables are style and content features, and the objective variables are evaluation scores. We construct an impression estimation model using Lasso regression for each of them. Next, (3) based on impression estimation models, we control the visual impressions and synthesize textures. In (2), we constructed highly accurate visual impression estimation models using style and content features. In (3), we obtained synthesis results that match human intuition.
Sentiment Analysis and Opinion Mining

With the rapid growth of social media, sentiment analysis, also called opinion mining, has become one of most active research areas in natural language processing. Its application is also widespread, from business services to political campaigns. This article gives an introduction to this important area and presents some recent developments.
Secondary School Mathematics Methods

Method is the key to success, and scientific method is the soul of science. Since ancient times, people have attached great importance to the theoretical research of methods, trying to use the correct methods to understand and remold the world.
Spam Detection Using Naïve Bayes and Trigger-Based Filter

Spam messages are irrelevant messages sent by unwanted organizations or individuals. Short message service (SMS) spam is a major problem in mobile phones. This paper works on tackling this problem by filtering spam and ham messages. The author researched different techniques to do spam filtering including support vector machine, K-Nearest Neighbors, random forest, naïve Bayes, rough sets, and deep learning. In this paper, the author tests the Naïve Bayes algorithm to detect SMS spam along with a set of trigger words that helps in determining the category of the message. A dataset has been used to train the system, and a set of keywords has been prepared to assist the task. The accuracy of the model has been feasible. Along with tweaks to the naïve Bayes algorithm, the paper achieved some favorable outcomes. The authors are currently developing a hybrid machine learning algorithm for spam detection in an effort to improve the approach.