Reviewerly/exHarmony huggingface dataset:
Learning-driven lossy image compression: A comprehensive survey

In the field of image processing and computer vision (CV), machine learning (ML) architectures are widely used. Image compression problems can be solved using convolutional neural networks (CNNs). As a result of bandwidth and memory constraints, compression of images is a necessity. There are three types of information found in images: useful, redundant, and irrelevant. In this survey, we will discuss how ML is used to compress lossy images. Firstly, we describe the background of lossy image compression. Next, we classify ML-based image compression frameworks into subgroups based on their architectures. Auto-encoders (AEs), variational auto-encoders (VAEs), CNNs, recurrent neural networks (RNNs), long short-term memories (LSTMs), gated recurrent units (GRUs), generative adversarial networks (GANs), transformers, principal component analysis (PCA) and fuzzy means clustering are among these subgroups. By analyzing learning-driven image compression frameworks, we present pros and cons of each subgroup. Lastly, we outline several research gaps and future research directions in the field of ML-based image compression.
Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems

Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates Archimedes principles. AOA has been used in a variety of real-world applications because of potential properties such as a limited number of control parameters, adaptability, and changing the set of solutions to prevent being trapped in local optima. Despite the wide acceptance of AOA, it has some drawbacks, such as the assumption that individuals modify their locations depending on altered densities, volumes, and accelerations. This causes various shortcomings such as stagnation into local optimal regions, low diversity of the population, weakness of exploitation phase, and slow convergence curve. Thus, the exploitation of a specific local region in the conventional AOA may be examined to achieve a balance between exploitation and exploration capabilities in the AOA. The bird Swarm Algorithm (BSA) has an efficient exploitation strategy and a strong ability of search process. In this study, a hybrid optimizer called AOA-BSA is proposed to overcome the limitations of AOA by replacing its exploitation phase with a BSA exploitation one. Moreover, a transition operator is used to have a high balance between exploration and exploitation. To test and examine the AOA-BSA performance, in the first experimental series, 29 unconstrained functions from CEC2017 have been used whereas the series of the second experiments use seven constrained engineering problems to test the AOA-BSA’s ability in handling unconstrained issues. The performance of the suggested algorithm is compared with 10 optimizers. These are the original algorithms and 8 other algorithms. The first experiment’s results show the effectiveness of the AOA-BSA in optimizing the functions of the CEC2017 test suite. AOABSA outperforms the other metaheuristic algorithms compared with it across 16 functions. The results of AOABSA are statically validated using Wilcoxon Rank sum. The AOABSA shows superior convergence capability. This is due to the added power to the AOA by the integration with BSA to balance exploration and exploitation. This is not only seen in the faster convergence achieved by the AOABSA, but also in the optimal solutions found by the search process. For further validation of the AOABSA, an extensive statistical analysis is performed during the search process by recording the ratios of the exploration and exploitation. For engineering problems, AOABSA achieves competitive results compared with other algorithms. the convergence curve of the AOABSA reaches the lowest values of the problem. It also has the minimum standard deviation which indicates the robustness of the algorithm in solving these problems. Also, it obtained competitive results compared with other counterparts algorithms regarding the values of the problem variables and convergence behavior that reaches the best minimum values.
Artificial neural networks for data mining in animal sciences

Abstract Background With the advancement in technology the amount of data generated, in almost every sphere of life, is increasing exponentially. This enormous amount of data needs new powerful tools for analysis and inference drawing. One such process is data mining which is the automated extraction of hidden, previously unknown, and useful knowledge from big data. Data mining is crucial as conventional strategies cannot keep up with the analysis of rapidly accumulating data and they are also inflexible in the wake of new challenges. Animal sciences are no exception to the changing scenario, especially when animal farms are quickly becoming more data intensive. Main body of the abstract The amount of data generated on the farms is also growing exponentially as farms become more intensive and mechanized. There is thus a need to utilize the knowledge of multidisciplinary fields like advanced statistics, artificial intelligence, machine learning, and database management, for revamping animal sciences. Artificial neural networks (ANNs) offer a lot of promise in this direction since they are motivated by the distributed, massively parallel computation in the brain. ANNs are powerful machine learning tools that offer multiple advantages for data mining over traditional techniques in being fast, accurate, self-organizing, robust, and highly accepting of noisy and imprecise data. Neural networks are being applied successfully for a myriad of supervised and unsupervised learning applications to draw useful hitherto unknown inferences, patterns, and relationships. Neural networks have been used successfully for pattern recognition, clustering, forecasting, prediction, and classification in animal sciences due to their capacity to learn from data, their nonparametric nature, and their ability to generalize well. Today ANN computing is a major element within any data mining tool kit. Popular methods used for neural network computing include feed-forward networks, feedback networks, and self-organization networks. ANN also offers powerful and distributed computing architecture, especially under a scenario where the data are readily available in significant quantity. Short conclusion This paper gives an overview of ANN and their applications in animal sciences and reviews major research conducted in this new and exciting area of artificial intelligence. Research in many aspects of ANN in Animal Sciences has been conducted globally although there is scope for more research in aspects of animal health, monitoring, breeding as well as nutrition .
Detection of anomaly in surveillance videos using quantum convolutional neural networks

Anomalous behavior identification is the process of detecting behavior that differs from its normal. These incidents will vary from violence to war, road crashes to kidnapping, and so on in a surveillance model. Video anomaly detection from video surveillance is a difficult research activity due to the frequency of anomalous cases. Since certain devices need manual evaluation for the detection of violent or criminal situations at the same time video monitoring of security cameras is also a challenging task and is unreliable. When the data or model dimension is sufficiently large, convolutional neural networks have the limitation of learning inefficiently. Quantum Convolutional Neural Network (QCNN) is the name given to a technology that combines CNN and quantum computing. Quantum computation and CNN are combined to create a more efficient and outperforming solution for solving complicated machine-learning problems. To analyze the anomalies in a sequence of video frames, two models are proposed in this research. In this research 07 layers of Javeria deep convolutional neural network (DCNN) are proposed on the selected hyperparameters named J. DCNN which is also different from the existing models to analyze the abnormal behavior in a video segment. Furthermore, for a comprehensive analysis of the abnormal video frames a model is proposed which is the combination of Javeria quantum and convolutional neural networks (J. QCNN). In this model 04-qubit quantum neural network is used with five layers and an optimal loss rate named J. QCNN. The proposed J. QCNN model is different from the existing deep learning architectures. The proposed models are trained from the scratch for the detection of anomalous from top challenging publicly available video surveillance datasets such as UNI-Crime and UCF Crime. The proposed J. QCNN model classifies the number of violent robberies such as armed thefts containing handguns or knives, and robberies displaying varying levels of viciousness with 0.99 accuracy while J. DCNN model gives 0.97 accuracy. The obtained results are superior in comparison with recent existing cutting-edge published work for real-time anomaly detection in video CCTV.
AR/VR Teaching-Learning Experiences in Higher Education Institutions (HEI): A Systematic Literature Review

During the last few years, learning techniques have changed, both in basic education and in higher education. This change has been accompanied by new technologies such as Augmented Reality (AR) and Virtual Reality (AR). The combination of these technologies in education has allowed a greater immersion, positively affecting the learning and teaching processes. In addition, since the COVID-19 pandemic, this trend has been growing due to the diversity of the different fields of application of these technologies, such as heterogeneity in their combination and their different experiences. It is necessary to review the state of the art to determine the effectiveness of the application of these technologies in the field of university higher education. In the present paper, this aim is achieved by performing a systematic literature review from 2012 to 2022. A total of 129 papers were analyzed. Studies in our review concluded that the application of AR/VR improves learning immersion, especially in hospitality, medicine, and science studies. However, there are also negative effects of using these technologies, such as visual exhaustion and mental fatigue.
Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of standardization. In this paper, we investigate the use of artificial intelligence (AI) to improve SC security. We provide an overview of Smart Contracts (SCs) and blockchain technology, as well as a discussion of possible SC-based attacks. Then, we introduce various AI categories and their applications in cybersecurity, followed by a thorough analysis of how AI can be used to enhance SC security. We also highlight the open questions and future directions of research in this field. Our research demonstrates that AI can provide an effective defense against assaults on SCs and contribute to their security and dependability. This article lays the groundwork for future research in the field of AI for SC security.
Energy-efficient distributed federated learning offloading and scheduling healthcare system in blockchain based networks

Many disease detection and prevention applications in digital healthcare systems are widely used but often focus only on prediction and classification, ignoring processing performance and data privacy issues. The study investigates the Energy-Efficient Distributed Federated Learning Offloading and Scheduling Healthcare Systems in Blockchain-Based Networks problem for healthcare applications. In order to solve the problem, the study presents the Energy-Efficient Distributed Federated Learning Offloading and Scheduling (EDFOS) system in blockchain based networks. EDFOS consisted of different schemes such as energy efficient offloading and scheduling and meet the quality of services (QoS) of applications during performing in the system. Simulation results show that EDFOS reduces power consumption by 39%, training and testing time by 29%, and resource leakage and deadlines by 36% compared to existing healthcare systems. The EDFOS platform is an effective solution for addressing the issues of power consumption and data privacy in healthcare applications.
A novel approach to predicting the stability of the smart grid utilizing MLP-ELM technique

Evaluating and forecasting stability across different conditions is essential since smart grid stabilization is among the most significant characteristics that could be employed to assess the functionality of smart grid design. Some intelligent methods to foresee stability are required to mitigate unintended instability in a smart grid design. This is due to the rise in domestic and commercial constructions and the incorporation of green energy into smart grids. It is currently hard to forecast the stability of the smart grid. In this framework, a smart grid with reliable mechanisms is being implemented to meet the fluctuating energy demands as well as providing more availability. The involvement of consumers and producers is one of the many factors influencing the grid's stability. This study suggested a novel approach for locating stability statistics in smart grid systems utilizing machine learning frameworks was presented. This paper outlined the multi-layer perceptron-Extreme Learning Machine (MLP-ELM) methodology to predict the sustainability of the smart grid. Additionally, this utilized the principal component analysis (PCA) approach for extracting features. In addition to an empirical assessment and a comparison to various approaches, this article presents an implementation result for smart grid stability. Simulation findings demonstrate that the suggested MLP-ELM approach outperforms traditional machine learning techniques, with accuracy reaching up to 95.8%, precision at 90%, recall at 88%, and F-measure at 89%.
Demonstration of Algorithmic Quantum Speedup

Despite the development of increasingly capable quantum computers, an experimental demonstration of a provable algorithmic quantum speedup employing today's non-fault-tolerant devices has remained elusive. Here, we unequivocally demonstrate such a speedup within the oracular model, quantified in terms of the scaling with the problem size of the time-to-solution metric. We implement the single-shot Bernstein-Vazirani algorithm, which solves the problem of identifying a hidden bitstring that changes after every oracle query, using two different 27-qubit IBM Quantum superconducting processors. The speedup is observed on only one of the two processors when the quantum computation is protected by dynamical decoupling but not without it. The quantum speedup reported here does not rely on any additional assumptions or complexity-theoretic conjectures and solves a bona fide computational problem in the setting of a game with an oracle and a verifier.
Global-local hierarchical path planning scheme for unmanned surface vehicles under dynamically unforeseen environments

Unmanned surface vehicles (USVs) should be allowed to respond to dynamic ocean environments and self-adjust their paths safely and efficiently. In this paper, considering the challenges posed by time-varying, partially unknown complex environments, a novel hierarchical motion planning framework is elaborately designed for a USV, which includes a global trajectory optimization and a local reactive collision avoidance strategy. By encapsulating the intricate nature of ocean environment, a global optimization path planning problem is developed to systematically strengthen the model's adaptability to the complex engineering problem. Incorporating adaptive elite selection and fuzzy probability set, an adaptive-elite GA with fuzzy inference (AEGAfi) is devised to fully exploit the underlying optimization problem, providing high-quality global paths. By applying virtual sensory vector onto the USV's sensing module, the COLREG-compliant local-reaction is achieved by governing feasible actions of USVs under dynamically unforeseen environments. Seamlessly bridged by the transition Clothoid path, the linkage between global optimization and dynamic-avoidance is strengthened by softening the replanning time restriction and maintaining path continuity. Eventually, the motion planning framework merits autonomous global-planning and local-reaction in an organically modular manner. Comprehensive simulations and comparisons in various ocean scenarios demonstrate the effectiveness and superiority of the proposed framework.
Single Image Dehazing Using Saturation Line Prior

Saturation information in hazy images is conducive to effective haze removal, However, existing saturation-based dehazing methods just focus on the saturation value of each pixel itself, while the higher-level distribution characteristic between pixels regarding saturation remains to be harnessed. In this paper, we observe that the pixels, which share the same surface reflectance coefficient in the local patches of haze-free images, exhibit a linear relationship between their saturation component and the reciprocal of their brightness component in the corresponding hazy images normalized by atmospheric light. Furthermore, the intercept of the line described by this linear relationship on the saturation axis is exactly the saturation value of these pixels in the haze-free images. Using this characteristic of saturation, termed saturation line prior (SLP), the transmission estimation is translated into the construction of saturation lines. Accordingly, a new dehazing framework using SLP is proposed, which employs the intrinsic relevance between pixels to achieve a reliable saturation line construction for transmission estimation. This approach can recover the fine details and attain realistic colors from hazy scenes, resulting in a remarkable visibility improvement. Extensive experiments in real-world and synthetic hazy images show that the proposed method performs favorably against state-of-the-art dehazing methods. Code is available on https://github.com/LPengYang/Saturation-Line-Prior.
Sequential attention mechanism for weakly supervised video anomaly detection

Surveillance cameras are installed across various sectors of a smart city in order to capture ongoing events for monitoring purposes. The analysis of these surveillance videos is an important research topic that involves activity recognition, object detection, anomaly recognition, and other problems. However, anomaly recognition is the most common task in a smart city, and has received significant attention with the aim of ensuring public safety and security. Many works have been published in this field, but these schemes have not been able to provide the desired detection outcomes. Mainstream anomaly recognition methods are heavily dependent on strong supervision to achieve satisfactory performance, which is time-consuming and impractical. With a particular focus on this problem, this article presents a deep convolution neural network (CNN)-based novel anomaly recognition model, in which deep features are extracted from surveillance video frames. These features are forwarded to the proposed temporal convolution network (TCN) that includes a multi-head attention module to enable it to recognise anomalies from these videos. The multi-head temporal attention mechanism enables the model to obtain more key temporal information about the complex surveillance environment. Experiments conducted on standard datasets and a comparison with state-of-the-art approaches demonstrate the effectiveness and superiority of the proposed framework, which achieves increases in accuracy of 0.9%, 1.9%, 0.65%, 0.27%, and 1.5% on the UCF-Crime2local, LAD-2000, RWF-2000, RLVS, and Crowd Violence datasets, respectively. These outcomes indicate the suitability of our method for deployment in real-time surveillance schemes.
A Reliable Transmission and Extraction of Textual Information Using Keyless Encryption, Steganography, and Deep Algorithm with Cuckoo Optimization

The entire work postulates a fusion of an innovative keyless image encoding approach based on a chaotic map and steganography. Nearly every single picture element has been encrypted by rearranging pixel values that are then assessed by such an adapted cat map as well as steganography has been used in the suggested methodology to transmit keyless encoded textual information utilizing a cover image, with encoded textual image information inserted into cover images. But on the other hand, retrieving textual images embedded in the receiver has indeed been regarded as the most challenging process. Furthermore, a sender will indeed transfer reasoning rather than a key to the recipient, ignoring the fact that acquiring engrained textual information hidden inside the cover image to enhance efficiency would have been challenging. Steganography was easily accomplished by placing data bits of secret textual image information into the cover image’s least significant bit of picture elements. The image quality, on the other hand, suffers greatly from the inclusion of an encrypted message. Images could indeed cause some disturbances from time to time. As a consequence, non-smooth areas of the image have been regarded as smooth. Therefore, the weighted Naive Bayes algorithm (NBA) method is being used in the illustration to retrieve the appropriate textual image information from complicated deteriorated images. Normally, images comprise a small amount of noise, so the introduction of the guided filter (GF) during the initial preprocessing phase is required. A very important feature in the textual image information extraction procedure was obtained utilizing a variety of techniques, including Gabor transform (GT), weighted Naive Bayes algorithms (WNBA), and a high-quality learning procedure for standard procedure. Eventually, textual content identification and recognition are performed using a combination of DNN and cuckoo algorithms. Again, for the proposed system, the set of data IIIT5K is being used, as well as high performance has been gained with parameters such as mean square error and peak signal-to-noise ratio.
An Invasive Ductal Carcinomas Breast Cancer Grade Classification Using an Ensemble of Convolutional Neural Networks

Invasive Ductal Carcinoma Breast Cancer (IDC-BC) is the most common type of cancer and its asymptomatic nature has led to an increased mortality rate globally. Advancements in artificial intelligence and machine learning have revolutionized the medical field with the development of AI-enabled computer-aided diagnosis (CAD) systems, which help in determining diseases at an early stage. CAD systems assist pathologists in their decision-making process to produce more reliable outcomes in order to treat patients well. In this work, the potential of pre-trained convolutional neural networks (CNNs) (i.e., EfficientNetV2L, ResNet152V2, DenseNet201), singly or as an ensemble, was thoroughly explored. The performances of these models were evaluated for IDC-BC grade classification using the DataBiox dataset. Data augmentation was used to avoid the issues of data scarcity and data imbalances. The performance of the best model was compared to three different balanced datasets of Databiox (i.e., 1200, 1400, and 1600 images) to determine the implications of this data augmentation. Furthermore, the effects of the number of epochs were analysed to ensure the coherency of the most optimal model. The experimental results analysis revealed that the proposed ensemble model outperformed the existing state-of-the-art techniques in relation to classifying the IDC-BC grades of the Databiox dataset. The proposed ensemble model of the CNNs achieved a 94% classification accuracy and attained a significant area under the ROC curves for grades 1, 2, and 3, i.e., 96%, 94%, and 96%, respectively.