Reviewerly/exHarmony huggingface dataset:
Phishing Website Detection with Ensemble Learning Approach Using Artificial Neural Network and AdaBoost

Phishing is one of the most serious security threats. Most phishing attacks occur on online transaction websites such as banking, commercial businesses, e-commerce, and more. This type of attack is increasing every day. More than 90% of data breaches are done by phishing. In addition, ransomware viruses can also be delivered through phishing. Phishing websites have a similar appearance and URL to official and popular websites, making them very difficult to identify. Almost 75% of phishing websites use Secure Sockets Layer certificates (SSL) so the SSL protocol does not guarantee the website is legitimate. This research performs phishing detection with a URL-based approach, where URL data that has been extracted features will be analyzed and learned with machine learning techniques. Artificial Neural Network and AdaBoost are implemented to learn patterns in the data. To maximize the modeling results, some tests are also conducted on the ANN parameters. Based on the results of the ANN and AdaBoost implementation in detecting phishing websites, the hybrid ANN-AdaBoost model is the best with an accuracy of 98.60%, precision of 98.95%, and recall of 99.31%. This shows that the use of a combination technique of ANN and AdaBoost is the right choice to detect and increase accuracy and effectiveness in avoiding phishing threats.
Grey Wolf Optimization algorithm based on Hybrid Multi-strategy

In order to overcome the problems of standard grey wolf optimization algorithm, such as poor optimization accuracy, slow convergence speed and falling into local extreme point, a hybrid multi-strategy grey wolf optimization (Grey Wolf Optimization algorithm based on Circle chaotic Map and Levy Flight—CLGWO) algorithm is proposed. The algorithm first introduces Circle chaotic map to initialize the population, then uses nonlinear convergence factor and adaptive adjustment strategy to balance local and global search capabilities, and finally uses Levy flight strategy to improve the position of gray wolves to improve the optimization accuracy and convergence speed of the algorithm. Six benchmark functions are selected to verify the CLGWO algorithm, and the results show that the improved algorithm is better in optimization accuracy and convergence speed, so the CLGWO algorithm proposed in this paper has good application value in solving the optimal parameters.
A power consumption prediction algorithm based on job similarity judgment

Facing the high energy consumption problem of High Performance Computing(HPC) clusters, power consumption prediction of HPC clusters can help data centers improve energy efficiency and formulate energy-saving strategies. However, the existing power prediction methods predict the power consumption of many different jobs in an HPC cluster, and due to the variety of job types in the cluster, the power consumption of different types of jobs varies, and even the same job may change due to different parameters, thus reducing the accuracy of the prediction. To address the current problem of low accuracy of power consumption prediction for multiple jobs in HPC clusters, we propose a power consumption prediction method(PCSSJ). based on the similarity judgment of user jobs The algorithm consists of two modules: similarity judgment and power consumption prediction. The algorithm can select a set of jobs similar to the target job from the historical jobs executed by this user for training and predict the target job. To verify the effectiveness of the PCPSJ algorithm, we train on five different types of datasets and compare them with the baseline model, and the results show the superiority of the PCPSJ algorithm.
Multi-View 3D Human Pose and Shape Estimation with Epipolar Geometry and Mix-Graphormer

This paper focuses on the estimation of 3D human pose and shape from multi-view images. Despite significant progress in monocular 3D human pose and shape estimation, monocular methods still suffer from limitations such as occlusion, pose ambiguity, and depth blur. Multi-view images, which contain depth information, can be used to address these issues. We utilize Epipolar Geometry to establish point correspondences across different views, thus enabling multi-view feature fusion. We propose a Mix-Graphormer encoder that uses graph convolutional networks to establish fine-grained local interactions and obtain the correlation between key points and mesh vertices. To better utilize the features of the multi-view fusion heatmap, we propose a mix multi-head self-attention mechanism (Mix-MSA), which extracts features from the target human body mesh template and the fused multi-view heatmaps while integrating their interaction information. Experiments are conducted on the Human3.6M dataset and the MPI-INF-3DHP dataset. The results demonstrate that our model is competitive with state-of-the-art 3D shape and pose models.
Breast Cancer Detection Framework using Evolutionary Search and SVM Classifier

Because breast cancer is a common and potentially dangerous disease, early and correct detection is essential for effective treatment. The present research paper, we propose a breast cancer detection framework that combines evolutionary search algorithms and Support Vector Machine (SVM) classifiers help to increase the accuracy and effectiveness of diagnosis.The framework utilizes evolutionary search algorithms to optimize the selection of relevant features from medical imaging data, followed by classification using SVM classifiers. The evolutionary search algorithms aid in identifying the most discriminative features, while the SVM classifiers provide efficient and accurate classification based on these features. By integrating these techniques, the proposed framework offers a comprehensive and automated approach to breast cancer detection. The usefulness of the proposed framework is demonstrated by experimental findings on benchmark datasets, achieving high classification accuracy and outperforming existing methods. The proposed framework has an opportunity to advance development of advanced breast cancer diagnostic tools, Enabling early detection and prompt intervention, increasing patient outcomes is the end goal and reducing mortality rates.
Rainfall Estimation Using Convolutional Neural Networks in Various Environmental Backgrounds

Recently, rainfall estimation systems using surveillance cameras and Convolution Neural Networks are one of the exciting topics to discuss. This system is proposed to solve the limited number of rainfall instruments. Unfortunately, the performance of the existing method hasn't got satisfactory results. This study focuses on the surveillance camera background effect in estimating rainfall using various Convolution Neural Networks. The image background was collected from four different environments: a building, a tree, an asphalt road, and a combination of such areas. This paper used two Convolution Neural Networks (CNN) architectures, VGG16 and Resnet32. An actual RGB image was extracted from standard commercial surveillance cameras used as input for the model with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$360\times 240$</tex> pixels resolution. The rainfall target is collected from an automatic rain gauge and classified into six classes. The total image sample of all backgrounds is 29880, used to train and test the model. Based on the experiment, the roads and trees background have the highest accuracy consistently for both architectures. This result shows the contrast between the rain streak and the image background is needed to increase the model performance.
A Comparative Study For Breast Cancer Prediction Methods Using Deep Learning Technique

Mammography is common most dominant imaging techniques helps todetect and diagnose the breast cancer. A computer-aided automatic classifier using machine learning techniques is make use of it to increase the accuracy and effectiveness of the diagnosing system. Using this approach, mammography patterns can be spontaneously classified as benign or malignant. Deep learning algorithms have become very much approachable in recent years. Convolutional Neural Networks have recently offered an option for image processing that includes a mammogram. This study summarizes the benefaction to the field of breast mammography analysis and reviews several deep learning principles. We provide an analysis of recent advancements, engage in a discussion about better methods for mammography analysis using CNN, and outline potential directions for future study.
Secure Cross-domain Medical Data Sharing based on Distributed Cloud and Blockchain Services

Electronic Health Record (EHR) provides critical medical data. Sharing EHRs can add services and value for hospitals and patients and effectively improve medical services’ quality. In the process of cross-hospital diagnosis, EHRs cannot be shared across domains because they are separately stored in various hospitals, which may prevent timely diagnosis and lead to data isolation in medical data. Insecure storage and sharing also endanger the privacy of patients’ data, which likely leads to the disclosure of privacy-sensitive information or unauthorized access. To address these challenges, we propose a secure, cross-domain medical data-sharing scheme with data confidence protection. Specifically, we use a distributed cloud to realize cross-domain sharing of medical data, addressing the data isolation caused by the difference in hospital geographical locations. We combine a symmetric encryption algorithm with attribute encryption based on ciphertext policy (CP-ABE) to ensure the confidentiality and privacy of shared data, delivering fine-grained access control of shared medical data. Further, we introduce a key blockchain to store the ciphertext of the decryption key, the storage index of the data ciphertext, and store the ciphertext of the original data off-blockchain. Experiments show that the proposed scheme can perform better than existing cloud server-based data-sharing schemes.
EXTRA: Integrating External Knowledge into Multimodal Hashtag Recommendation System

As microblogs have become commonplace, recommending relevant hashtags for microblog posts has become increasingly important. However, recommending appropriate hashtags for a post is challenging because it requires a high-level understanding of the context and relationships of the information in the post. In this paper, we propose a novel hashtag recommendation framework that incorporates external knowledge to enrich the context of posts. Using an image of the post, we obtain the hierarchical external knowledge extracted by the Open Directory Project (ODP)-based classifier. Experimental results show that our framework performs better than the baselines on a multimodal hashtag recommendation benchmark dataset. It outperformed the existing state-of-the-art model by providing a 39.86% increase in the average F1-score.
Computer Vision Based Shopping Assistant

A field that is constantly gaining popularity and has an increasing number of users is online shopping. The ease of shopping from the comfort of our homes along with the wide variety of choices at our disposal makes it the preferred choice of most shoppers. However, online shopping has a big problem that has not been addressed completely. A major problem in online shopping is the fact that picking the correct size or deciding if a particular item would suit a person. This makes online shopping a big hassle especially when we have to return certain items. The Computer Vision based shopping assistant helps provide a virtual tool to view the demos of the clothes that we wish to buy. It has an extensive catalogue of clothes to shop from along with virtual demo options so that when we buy a piece of clothing, we know exactly what we are getting and its size and fitting.
Research on Image Style Transfer Algorithm Based on Structural Consistency

The algorithm proposed in this paper aims to address the issue of structural content distortions in images that occur after applying image style transfer. It introduces a structural consistency-based approach called the PoolNet algorithm. This algorithm incorporates saliency regions to perform style transfer at the block level. By employing a saliency detection network, a saliency map is generated for both the synthetic and content images. During the training process, two losses are computed to ensure that the salient regions in the synthetic image remain consistent with those in the content image. Additionally, local affine constraints in the color space are employed to enhance the stylized images by constraining the image transformation. Experimental results demonstrate that the stylized images produced by this style transfer model exhibit improved visual effects and preserve the semantic information of the content images. This improvement is particularly noticeable when dealing with distorted migration images.
Local Ratio Sum Discriminant Analysis based on Adaptive Subspace

With the rapid development of science and technology and the sharp increase of data dimensions, it is difficult for traditional dimensionality reduction algorithms to find the optimal subspace of the data, which seriously affects the performance of the classifier. This paper proposes a local ratio sum discriminant analysis based on adaptive subspace graph. The ratio sum discriminant analysis considering the local structure is proposed; the alternative iterative optimization method is used to avoid the suboptimal solution found by the existing ratio sum optimization methods; the nearest neighbor similarity graph is learned in the optimal subspace instead of the original space, so as to avoid it. Influenced by the original spatial noise points; the Shannon entropy constraint is introduced to avoid trivial solutions; finally, the samples are projected to the optimal subspace. On synthetic datasets and face datasets, the proposed algorithm is tested with a large number of SOTA discriminant analysis algorithms for classification tasks. A large number of experimental results show that the proposed algorithm can learn a projection subspace with better discriminant performance and has better classification effect.
Real-time Lightweight Detection Model on Optimized YOLOv7: Applied to the UAVs in Urban Environments

Considering the conditional constraints in the urban environment, the traditional method of detecting drones is not applicable, and combining target detection with it is an effective solution. We propose a lightweight object detection model based on YOLOv7. The model replaces the Backbone based on the lightweight network MobileViTv3, which reduces the number of parameters of the model; Secondly, the SPD-Conv module is introduced to increase the detection accuracy of small-sized drones; In addition, we integrate the SimAM Module (Simple, Parameter-Free Attention Module) to find important regions in the scene. The experimental results show that the mAP50 of the model on the data set is 92.75%, the accuracy is 90.63%, and the number of parameters is 24.93M. It has the characteristics of high recognition accuracy and low memory usage, and the recognition effect is also better than the original model.
Building of Computationally Effective Deep Learning Models using Attention-Guided Knowledge Distillation

CNNs and Edge computing have proliferated in the last decade. While these technologies are stupendous in their own right, new horizons can be opened up if they were to be combined. Deep and complex CNNs, which are ones that produce groundbreaking accuracies, are not feasible for deployment in a resource constraint environment. The vanguard in model compression is an innovative technique known as Knowledge Distillation. This paper proposes a way to perform knowledge distillation by means of Attention Maps. The generation of attention maps from features is achieved through the use of ATCam. Not only does this method effectively distills knowledge it also nullifies another flaw of CNNs-black-boxiness. This may lead to leapfrogs in Mobile Machine Aided Medical Diagnosis by deploying interpretable complex CNN in small devices. Three ways to combine the feature maps were experimented with in this research work, which are Feature Map Accumulation(FMA), Maximum Intensity Projection(MIPs) and Saliency Maps. The performance of the proposed approaches are tested on PADUFES-20 dataset and all the three approaches provided significant boost in the performance over the traditional transfer learning methods. The best of these methods - saliency maps - gains a accuracy boost of 9.56% over the generic methods of knowledge transfer. Moreover, to one’s surprise, the resultant model not only matches the teacher model in accuracy, it exceeds it by 1.52% while being only 43.2% of original teacher model’s size.
Cross-modal Pedestrian Re-Identification Based On Fine-Grained Information And Non-Local Attention Mechanism

Cross-modal pedestrian re-identification is used to match pedestrian images in different modes (infrared mode and visible light mode).For example,there is a pedestrian picture collected in the visible light mode during the day.The goal is to determine whether the same pedestrian has appeared in the picture collected by the infrared camera at night,and vice versa.Cross-modal pedestrian re-identification mainly solves the problem of pedestrian re-identification in weak light and night.This paper aims to improve the feature representation ability of the network and design appropriate loss functions to improve the similarity of the same pedestrian in two modes.In order to improve the performance of feature representation of feature learning module,a deep neural network structure combining triple loss function and nonlocal mechanism is introduced.On the other hand,it improves the cross-modal similarity within the class.In order to extract the fine-grained information of pedestrians,the pedestrian image is horizontally divided into four part,fine-grained feature learning further enhances the discriminant of network feature representation from both local and global aspects.The method of data enhancement by random erasure is added to improve the generalization of the test.The network achieves good performance on SYSU-MM01 and RegDB data sets.
Group-Scanning by Face Identification and Real time Emotion detection using Faster R-CNN

The investigation of face recognition systems carries great significance in the realm of computer vision, as they possess the capability to automatically identify and verify individual faces. These systems are extensively utilized for security purposes in various public and private areas, including banks, malls, hospitals, roads, offices, houses, and government buildings. Several deep learning-based algorithms for facial recognition, real-time emotion detection, and gender classification have been developed in recent years, with most approaches depending on CNN or RCNN. However, these approaches have limitations in processing speed and prediction outputs. This research explores the use of the Faster R-CNN approach for human face detection and realtime emotion recognition to identify individuals and rate their present state. Faster R-CNN has become the leading technology in facial recognition, surpassing other methods in various applications including facial recognition, emotion recognition, and gender classification. Recent advancements in the field of face recognition systems have been achieved by training Faster RCNN models with an accuracy of 93 percent on extensive datasets such as the wider face dataset.
Traffic light detection based on improved YOLOV5

Traffic light detection is a part of modern intelligent driving, which affects the driving safety of smart cars. However, the object of traffic lights is small and the actual road conditions are complex. The classic object detection algorithm cannot achieve a good detection effect on small signal lights. For this situation, This paper proposes an improved yolov5 model. Because the object of the traffic light is small, the feature extracted by the P2 layer with a small receptive field is introduced. Inspired by the MOAT model, a C3_MOAT structure is proposed. At the same time, a structural re-parameterized RepConv module is added to the Head part of YOLOV5 to improve the feature extraction of the model. ability. Through the above improvements, the ability of the model to detect signal light objects is improved. Experiments show that the improved algorithm improves the map@.5 and map@.5:.95 indicators in the Bosch dataset by 2.6% and 2.7% respectively, and in the S2TLD dataset map@.5 and map@.5:.95 Indicators were increased by 2.2% and 1.6%. Compared with the YOLOV5 network, it has a good improvement.
An Effective Approach for Reducing Time and Memory Requirements for Sign Language Detection

Sign language serves as a crucial means of communication for individuals who are hard of hearing or deaf, but it is often misunderstood or unrecognized by the hearing population, leading to communication barriers and social exclusion. To address this issue, this research work introduces an approach that utilizes Mediapipe data and neural networks to accurately detect individual sign language gestures. By preprocessing the data, the size is significantly reduced by focusing solely on landmark points rather than entire videos. Furthermore, the preprocessing techniques decrease the training data size by approximately four times. The proposed method employs simple fully connected layers, avoiding computationally intensive CNN or LSTM architectures typically used in existing sign language models. Consequently, the training time is significantly reduced, from 7 hours to just 22 minutes, making the process more efficient and accessible. The effectiveness of the approach is evaluated across a range of 10 to 250 signs, surpassing the limited number of signs explored in most existing research. The achieved accuracies range from 84% for 10 signs to 73% for 250 signs. These results show the efficacy of the proposed approach in facilitating daily communication for individuals with hearing impairments. By employing Mediapipe data and leveraging neural networks with simplified architectures, this approach offers a promising solution for accurate sign language gesture detection, providing an avenue for improved understanding and inclusion between the deaf and hearing communities.
Extreme-learning-machine-based architecture to assess the reliability of the integrated energy systems

The global energy crisis has contributed to the development of integrated energy systems (IES). IES enables the coupling, conversion, and utilisation of multiple energy, such as electricity, heat and cooling. As renewable energy penetration increases, the precariousness of renewable energy in IES causes risks to the reliability of energy supply. Traditional optimal energy flow (OEF) models become complex and computationally intensive. To solve this problem, a machine-learning-based method is proposed instead of the traditional OEF model. The method obtains the model optimal coding parameters by extreme learning machine (ELM) model, establishes the nonlinear mapping relationship between the system operation state and the optimal load reduction, and realizes the high accuracy and fast solution of the reliability indices. In this article, simulation experiments are conducted on the integrated community energy system (ICES) to test the advantage and practicality of the approach in this paper. Compared with the model-driven approach, the method in this paper greatly improves the efficiency of reliability assessment and has an accuracy rate of over 95%, which is better than random forest, back propagation neural network and autoencoder.