Reviewerly/exHarmony huggingface dataset:
A survey on the utilization of Superpixel image for clustering based image segmentation

Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent. However, selection of proper superpixel generation techniques and superpixel image segmentation techniques play a very crucial role in the domain of different kinds of image segmentation. Clustering is a well-accepted image segmentation technique and proved their effective performance over various image segmentation field. Therefore, this study presents an up-to-date survey on the employment of superpixel image in combined with clustering techniques for the various image segmentation. The contribution of the survey has four parts namely (i) overview of superpixel image generation techniques, (ii) clustering techniques especially efficient partitional clustering techniques, their issues and overcoming strategies, (iii) Review of superpixel combined with clustering strategies exist in literature for various image segmentation, (iv) lastly, the comparative study among superpixel combined with partitional clustering techniques has been performed over oral pathology and leaf images to find out the efficacy of the combination of superpixel and partitional clustering approaches. Our evaluations and observation provide in-depth understanding of several superpixel generation strategies and how they apply to the partitional clustering method.
Knowledge graph embedding model with attention-based high-low level features interaction convolutional network

Knowledge graphs are sizeable graph-structured knowledge with both abstract and concrete concepts in the form of entities and relations. Recently, convolutional neural networks have achieved outstanding results for more expressive representations of knowledge graphs. However, existing deep learning-based models exploit semantic information from single-level feature interaction, potentially limiting expressiveness. We propose a knowledge graph embedding model with an attention-based high-low level features interaction convolutional network called ConvHLE to alleviate this issue. This model effectively harvests richer semantic information and generates more expressive representations. Concretely, the multilayer convolutional neural network is utilized to fuse high-low level features. Then, features in fused feature maps interact with other informative neighbors through the criss-cross attention mechanism, which expands the receptive fields and boosts the quality of interactions. Finally, a plausibility score function is proposed for the evaluation of our model. The performance of ConvHLE is experimentally investigated on six benchmark datasets with individual characteristics. Extensive experimental results prove that ConvHLE learns more expressive and discriminative feature representations and has outperformed other state-of-the-art baselines over most metrics when addressing link prediction tasks. Comparing MRR and Hits@1 on FB15K-237, our model outperforms the baseline ConvE by 13.5% and 16.0%, respectively.
Bi-LSTM and Conventional Classifiers for Email Spam Filtering

Nowadays, emails play a major role in everyone’s day-to-day life. The number of people using email is rapidly growing. Because of this, the hackers are taking advantage of the opportunity to use the emails as their secret weapons against the email users. The hackers send a bulk of emails at a time with a single click. The majority of spam mail contains advertisements or promotions for various events, utilities, appliances, and so on. A single Spam email results in a net loss for the user. Even with current technology, there are numerous techniques and methods for distinguishing between spam and ham mail, but spammers are finding ways to get the user's attention and developing themselves due to the decreased effectiveness of techniques. So, our study comes up with effective classifiers in this paper. In this research paper, ML and deep learning algorithms such as Naive Bayes Classifier, Random Forest, Artificial Neural Network, Support Vector Machine, Long Short-Term Memory, and Bidirectional-Long Short-Term Memory are used to identify which model is more accurate to classify the emails as spam or ham.
RMAU-Net: Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images

Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic liver and tumor segmentation are of great value in clinical practice as they can reduce surgeons' workload and increase the probability of success in surgery. Liver and tumor segmentation is a challenging task because of the different sizes, shapes, blurred boundaries of livers and lesions, and low-intensity contrast between organs within patients. To address the problem of fuzzy livers and small tumors, we propose a novel Residual Multi-scale Attention U-Net (RMAU-Net) for liver and tumor segmentation by introducing two modules, i.e., Res-SE-Block and MAB. The Res-SE-Block can mitigate the problem of gradient disappearance by residual connection and enhance the quality of representations by explicitly modeling the interdependencies and feature recalibration between the channels of features. The MAB can exploit rich multi-scale feature information and capture inter-channel and inter-spatial relationships of features simultaneously. In addition, a hybrid loss function, that combines focal loss and dice loss, is designed to improve segmentation accuracy and speed up convergence. We evaluated the proposed method on two publicly available datasets, i.e., LiTS and 3D-IRCADb. Our proposed method achieved better performance than the other state-of-the-art methods, with dice scores of 0.9552 and 0.9697 for LiTS and 3D-IRCABb liver segmentation, and dice scores of 0.7616 and 0.8307 for LiTS and 3D-IRCABb liver tumor segmentation.
Multistage quality control in manufacturing process using blockchain with machine learning technique

Information security has more demand for digital technology. Every industry transfers its data through computer networks for legal communication. The Internet of Things (IoT), sensor-based, is one of the most current advanced tools that can handle appropriate measures to control data operations across manufacturing industries. The demand for predictive machine reliability and quality drives the development of intelligent manufacturing technologies. To this goal, a variety of machine learning algorithms are being studied. Data protection and monitoring is also another concern that is a critical component of the organization. To overcome these issues, the proposed method uses Blockchain Technology (BCT) and Machine Learning to secure the information operations and manage a dataset. Significant data approaches were employed to organize and evaluate the obtained dataset. BCT allows collecting sensor user access data, whereas ML classifiers distinguish between normal and malicious behavior to detect attacks. DoS, DDoS, intrusion, a man in the middle (MitM), brute force, cross-site scripting (XSS), and searching are the attacks detected by BCT. Furthermore, the hybrid prediction technique assessed the fault detection prediction component. The program's quality control was set using non-linear machine learning techniques that represented the complicated world and determined the actual positive rate of the standard control methodology used by the platform. The experimental result shows that the proposed method outperforms empirical metrics such as accuracy, precision, recall, and response time. The proposed method efficiently provides security between innovative manufacturing transactions.
Cyber security and beyond: Detecting malware and concept drift in AI-based sensor data streams using statistical techniques

In the Industrial Internet of Things (IIoT), mobile devices can be used to remotely monitor and control industrial processes, equipment, and machinery. They can also be used to optimize production and maintenance processes, improve safety, and increase efficiency in industries such as manufacturing, energy, and transportation. The adoption of IIoT has the potential to increase production and efficiency, but it also raises new cybersecurity concerns since interconnected industrial systems are more susceptible to malware intrusions. Malware attacks on IIoT systems can have grave consequences, including production delays, data loss, and physical asset damage. To aid this we propose to use statistical drift detection methods to perceive any change in data patterns and train the machine learning classifiers to counter newly developed malware samples then and there. Our results with an accuracy of 95.2% and F1-score of 94% indicate that our approach is highly successful and easy to adopt.
Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis

Climate change alters the amount and spatiotemporal characteristics of solar radiation at the surface. How this affects the stability of solar energy has not yet been explored on a global scale. In this study, we combine ground observations and ERA5 reanalysis to calculate indicators of resource stability and solar intermittency to find evidence of changes in global solar energy stability over the past 20 years. The calculated results of ERA5 are highly consistent with those based on observations, allowing spatially continuous analysis using ERA5 estimates. We find that solar resource availability is on the rise in most regions, with a maximum decadal increase of up to 30 W/m2 and a rate of about 1–2 W/m2/year. The regions of significant downward trend are concentrated in the Northern Hemisphere. In addition, approximately 85% of the world's land is at risk of increasing intermittency, particularly in India, Central and Northern Africa, China, and the United States, which means that adaptation measures to mitigate intermittency should be coordinated in the context of climate change. This study contributes to understanding the climate impacts on solar energy stability and has practical value for future planning and development of solar energy.
Cybersafe Capabilities and Utilities for Smart Cities

From the beginning of the 21st century, the entire world suffers from two critical problems: the growth of the world population and the improvement of life expectancy of people owing to the development of healthcare facilities. This has ultimately led to urbanization where a lot of people migrated to cities in search of better prospects. To facilitate those migrating into these cities and with the purpose of better provision of city services, smart cities have emerged thereby connecting everything within the city with the aid of a complex set of technologies. The Internet of Things (IoT) is the fundamental building block of smart cities applied in a variety of smart city solutions, offering real-time information exchange and facilitating ubiquitous connectivity. As IoT is a novel technology that is still in its infancy age and requiring continuous internet connectivity, it paves way for never-ending cyber-attacks targeting smart city services and ultimately endangering the lives of city residents. On the other hand, the security of smart city solutions has always been neglected during the development phase which also endangers the entire city's ecosystem resulting into cyber-attacks from multiple attack vectors. In this chapter, we are providing a brief review of security enhancement capabilities and utilities that can cope with smart cities for the purpose of improving their security against cyber-attacks and safeguarding the privacy of city dwellers.
Swin-Transformer-Based YOLOv5 for Small-Object Detection in Remote Sensing Images

This study aimed to address the problems of low detection accuracy and inaccurate positioning of small-object detection in remote sensing images. An improved architecture based on the Swin Transformer and YOLOv5 is proposed. First, Complete-IOU (CIOU) was introduced to improve the K-means clustering algorithm, and then an anchor of appropriate size for the dataset was generated. Second, a modified CSPDarknet53 structure combined with Swin Transformer was proposed to retain sufficient global context information and extract more differentiated features through multi-head self-attention. Regarding the path-aggregation neck, a simple and efficient weighted bidirectional feature pyramid network was proposed for effective cross-scale feature fusion. In addition, extra prediction head and new feature fusion layers were added for small objects. Finally, Coordinate Attention (CA) was introduced to the YOLOv5 network to improve the accuracy of small-object features in remote sensing images. Moreover, the effectiveness of the proposed method was demonstrated by several kinds of experiments on the DOTA (Dataset for Object detection in Aerial images). The mean average precision on the DOTA dataset reached 74.7%. Compared with YOLOv5, the proposed method improved the mean average precision (mAP) by 8.9%, which can achieve a higher accuracy of small-object detection in remote sensing images.
Computational Time Complexity for Sorting Algorithm amalgamated with Quantum Search

In the pursuit of knowledge, data is a distinct set of values that convey information, describing quantity, quality, facts, statistics, or simple units of meaning, better known as datum. Now, handling data, in the latest world is really a tedious job that includes all the principles related to data management as a valuable resource. Amidst all data management techniques, Sorting is the most important one. Sorting refers to arranging data in ascending or descending order according to linear relationships between data items. It involves mathematical rigour that causes data units to be arranged in a monotonic order by it’s magnitude. In modern world of informatics, Quantum Computing have caught great heights. So, have the algorithms fused to it. Quantum computing is a type of computation whose operation can exploit quantum mechanical phenomena, such as superposition, interference, and entanglement. In this work, we would try to define a lower bound for the Computational Complexity of Sorting Algorithm, being heterogeneity by the lower bound for Quantum Search of an ordered list. Further, we would also develop a upper bound on the Sorting integrated using Grover’s Search- a real time unstructured Quantum Search.
Human Computer Interface Application for Emotion Detection Using Facial Recognition

An essential tool for describing human emotion is facial expression. From morning tonight, people go through a broad spectrum of emotions that may be caused by their mental or physical health. The six major facial expressions of happy, sorrow, surprise, fear, disgust, and rage are now classified by psychology as six universal emotions, despite the fact that humans experience a broad variety of emotions. Facial muscle movements may be used to determine humane motions. The brow, lips, nose, and eyes are the four primary facial characteristics Facial recognition is a topic that is now the focus of much investigation. Finding a match between the input image and the images to red in the database is the aim of face recognition. Human- computer interaction and the solution to the problem of criminal identification depend on facial recognition. The data in face recognition tasks has a high intrinsic complexity, making the dimensionality reduction strategy essential. Picture space's dimensionality is decreased using PCA. In the domains of computer vision, image processing, pattern recognition and other areas, the modelling of the face using Gabor features has generated a lot of attention. The major justification for using the Gabor filter is its biological significance. Mammal primary visual cortex neurons have an accessible field profile that is oriented toward spatial frequency features. Additionally, Gabor filters may make advantage of well-known visual properties as spatial localization, orientation selectivity and spatial frequency feature. So, we are proposing this computational model approach for the detection of emotional changes using dataset from EMOTIC database by recognition system. In this approach we achieved around 88% accuracy which efficient and better when it's been compared with computer modeling Methodologies.
Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles

Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.
Analysis of the Performance Impact of Fine-Tuned Machine Learning Model for Phishing URL Detection

Phishing leverages people’s tendency to share personal information online. Phishing attacks often begin with an email and can be used for a variety of purposes. The cybercriminal will employ social engineering techniques to get the target to click on the link in the phishing email, which will take them to the infected website. These attacks become more complex as hackers personalize their fraud and provide convincing messages. Phishing with a malicious URL is an advanced kind of cybercrime. It might be challenging even for cautious users to spot phishing URLs. The researchers displayed different techniques to address this challenge. Machine learning models improve detection by using URLs, web page content and external features. This article presents the findings of an experimental study that attempted to enhance the performance of machine learning models to obtain improved accuracy for the two phishing datasets that are used the most commonly. Three distinct types of tuning factors are utilized, including data balancing, hyper-parameter optimization and feature selection. The experiment utilizes the eight most prevalent machine learning methods and two distinct datasets obtained from online sources, such as the UCI repository and the Mendeley repository. The result demonstrates that data balance improves accuracy marginally, whereas hyperparameter adjustment and feature selection improve accuracy significantly. The performance of machine learning algorithms is improved by combining all fine-tuned factors, outperforming existing research works. The result shows that tuning factors enhance the efficiency of machine learning algorithms. For Dataset-1, Random Forest (RF) and Gradient Boosting (XGB) achieve accuracy rates of 97.44% and 97.47%, respectively. Gradient Boosting (GB) and Extreme Gradient Boosting (XGB) achieve accuracy values of 98.27% and 98.21%, respectively, for Dataset-2.
ETAM: Ensemble transformer with attention modules for detection of small objects

Detecting small objects is critical to many submissions, such as automatic drive and lung nodule detection. However, small object detection is challenging with low-resolution features. Therefore, the linchpin of small object detection is to design an effective encoder that can extract subtle features. In this paper, we present a powerful encoder, called Ensemble Transformer with Attention Modules (ETAM) encoder, for abstracting the subtle small object features without sacrificing the capability of larger object detection. In ETAM, a Magnifying Glass (MG) module is proposed to focus on representative features of small objects. Then, the Quadruple Attention (QA) is designed to enrich the small object features with width and height in addition to channel and position. To accommodate both small and large objects, we use ensemble learning in our ETAM encoder, which has two branches. Experimental results show that ETAM significantly improves small object detection based on PASCAL VOC, MS-COCO, VisDrone2019, and LIDC-IDRI. With ETAM, the mAP for small objects is improved up to 91.7% based on the four datasets.
A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges

Optical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR.