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De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

A full connected world is expected to be introduced in the sixth generation mobile network (6G). As a typical fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. Thus, integrating intelligence into edge computing is, no doubt, a promising development trend. In the future of vehicular networks, a massive variety of services need powerful computing resources and higher quality of service (QoS). Existing computing resources are insufficient to match those increasing requirements. Most works on this problem focused on finding the power-delay's trade-off, ignoring QoS and stable load balance. In this study, we found that the computing power and redundancy of vehicles' in IoV is increasing. So, those redundant computing resources are possible to be used to solve the shortage of computing resource. CNN is a typical AI technique. This technology is very suitable for solving the problems in this article. So, we adopted CNN technique of AI to design and algorithm of convolutional long short-term memory (CN_LSTM) based traffic prediction (ACLBTP). ACLBTP was designed to gain the predicted number of vehicles belonging to the edge node. Secondly, according to the problem of insufficient computing resources on remote servers, we found that a large amount of redundant computing resources exist in edge nodes. So, we used edge computing technique to solve the problem of insufficient computing resources on remote servers. ASOBCL was designed to distribute computing tasks to edge nodes. Meanwhile, an intelligent service offloading framework was provided in this article. Based on the framework, an algorithm of improved gradient descent (AIGD) was created to accelerate the speed of iteration. So, the ACLBTP's convergence of convolutional neural network (CNN) based on AIGD was able to be accelerated too. In ASOBCL, a sorting technique was adopted to speed up the offloading work. Simulation results demonstrated the fact that the prediction strategy designed in this paper had high accuracy. The low offloading time and maintaining stable load balance were gained via running ASOBCL. Low offloading time means short response time. Additionally, the QoS was guaranteed. So, these strategies designed in this paper were effective and valuable. [ABSTRACT FROM AUTHOR]

Medical imaging methods, such as CT scans, MRI scans, X-rays, and ultrasound imaging, are widely used for diagnosis in the healthcare domain. However, these methods are often affected by noise, which can lead to incorrect diagnoses. Radiologists used to rely on visual features observed through various imaging techniques to diagnose diseases in patients, but now, intelligent machines and artificial intelligence offer more accurate and early diagnoses. Over the past few decades, the classical problem of image denoising in computer vision has been extensively studied. This survey paper discusses the various techniques applicable which have tried to remove the noise from medical images. A complete overview of the problem hypothesis is stated in the paper, with an in-depth discussion on types and sources of noise and the evaluation metrics deployed, followed by the discussion and implementation of various filtering and image enhancement techniques. The section is succeeded by a comprehensive literature review conducted on leading and state-of-the-art methods in broadly four domains—frequency domain, filtering, CNN-based, Generative Adversarial Networks (GAN)-based and Transformer-based approaches. The conclusion summarises the findings and proposes the importance of image denoising, focusing on Explainable AI (XAI). [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

This paper gives a brief review of the detection of brain tumor in human brain considering the modern AI & ML tools. In recent years, MRI images have proven to be quite beneficial in the investigation of brain tumor identification. The formation of aberrant cell/s in the human brain, some of which may progress towards the cancer disease, is known as a brain tumor. The MRI, often known as the "Magnetic Type of Resonance Imaging" scans are the viable common type of bio-medical imaging software tools for detecting brain tumours. Information on aberrant tissue growth in the brain is identified using MRI imaging. Machine Learning, Deep Learning algorithms, CNN Algorithms, and RELM Algorithms have all been used to detect brain tumors in various study publications. When these algorithms are applied to MRI pictures, they can predict brain tumours quickly and accurately, as well as classify them into different categories. Assists in the delivery of treatment to patients. These forecasts also assist the radiologist in making an informed decision. Tumor detection employs a variety of supervised and unsupervised classification system/s. The paper serves as a ready reckoner to all the researchers who want to pursue their research in this biomedical image tumor detection in the brain. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is a domain that works on various complex applications, such as E-government services. In order to provide government services to the people, an online AI-based Deep Learning (DL) model has been developed to check the availability of government schemes. However, several E-government services are not available to the citizens based on their usage. Many challenges have been identified while using E-Government services. This paper introduces the DL model, Convolutional Neural Networks (CNN), to solve the issues in E-Government services. The system focuses on maintaining E-government data resources, and CNN is primarily used to automate E-Government services. Finally, CNN has developed an innovative E-Government environment to support the design, development, and implementation of applications. [ABSTRACT FROM AUTHOR]

In the last 10 years, the demand for robot-based depalletization systems has constantly increased due to the growth of sectors such as logistics, storage, and supply chains. Since the scenarios are becoming more and more unstructured, characterized by unknown pallet layouts and stock-keeping unit shapes, the classical depalletization systems based on the knowledge of predefined positions within the pallet frame are going to be substituted by innovative and robust solutions based on 2D/3D vision and Deep Learning (DL) methods. In particular, the Convolutional Neural Networks (CNNs) are deep networks that have proven to be effective in processing 2D/3D images, for example in the automatic object detection task, and robust to the possible variability among the data. However, deep neural networks need a big amount of data to be trained. In this context, whenever deep networks are involved in object detection for supporting depalletization systems, the dataset collection represents one of the main bottlenecks during the commissioning phase. The present work aims at comparing different training strategies to customize an object detection model aiming at minimizing the number of images required for model fitting, while ensuring reliable and robust performances. Different approaches based on a CNN for object detection are proposed, evaluated, and compared in terms of the F1-score. The study was conducted considering different starting conditions in terms of the neural network's weights, the datasets, and the training set sizes. The proposed approaches were evaluated on the detection of different kinds of paper boxes placed on an industrial pallet. The outcome of the work validates that the best strategy is based on fine-tuning of a CNN-based model already trained on the detection of paper boxes, with a median F1-score greater than 85.0 % . [ABSTRACT FROM AUTHOR]

Artificial intelligence and blockchain can improve the effectiveness of leadership decision-making in two dimensions. Artificial intelligence technology can improve the scientificity of leadership decision-making, and blockchain technology can guarantee the democracy of leadership decision-making. Society pushes everyone to be gregarious. Group recommendation is thus one of the research focuses in recent years. Prior group recommendation algorithms fail to consider either the influence of group structure on computing scale or the impressions users of higher weights leave on other group members. To address the aforementioned challenges, this paper proposes a group recommendation model based on members’ influence and leader impact. In this paper, a model has been proposed to compute members’ influence on each other based on interactions and presence. The decisions of leaders identified by the proposed model are the basis for further group recommendation, which yields satisfactory recommendations for most group members as leaders’ judgments are more professional. Experimental results on real-world datasets demonstrate better accuracy of the proposed method compared to those of the mainstream group recommendation algorithms. [ABSTRACT FROM AUTHOR]

There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques in varied application domains, it would be intriguing to consider these techniques for automated hate speech detection. In literature, there have been efforts to recognize and categorize hate speech using varied Machine Learning (ML) and Deep Learning (DL) techniques. Hence, considering the need and provocations for hate speech detection we aim to present a comprehensive review that discusses fundamental taxonomy as well as recent advances in the field of online hate speech identification. There is a significant amount of literature related to the initial phases of hate speech detection. The background section provides a detailed explanation of the previous research. The subsequent section that follows is dedicated to examining the recent literature published from the year 2020 onwards. The paper presents some of the hate speech datasets considered for hate speech detection. Furthermore, the paper discusses different data modalities, namely, textual hate speech detection, multi‐modal hate speech detection and multilingual hate speech detection. Apart from systematic review on hate speech detection, the paper also implement several multi‐label models to compare the performance of hate speech detection by employing classic ML technique namely, Logistic Regression and DL technique namely, Long Short‐Term Memory (LSTM) and a multiclass multi‐label architecture. In the implemented architecture, we have derived two new elements to quantify the hatefulness and intensity of hatred to improve the results for hate speech detection using Indonesian tweet dataset. Empirical Analysis of the model reveals that the implemented approach outperforms and is able to achieve improved results for the underlying dataset. [ABSTRACT FROM AUTHOR]

As one of the main methods of information fusion, artificial intelligence class fusion algorithm not only inherits the powerful skills of artificial intelligence, but also inherits many advantages of information fusion. Similarly, as an important sub-field of artificial intelligence class fusion algorithm, deep learning class fusion algorithm also inherits advantages of deep learning and information fusion. Hence, deep learning fusion algorithm has become one of the research hotspots of many scholars. To solve the problem that the existing neural networks are input into multiple channels as a whole and cannot fully learn information of multichannel images, Shan et al. proposed multichannel concat-fusional convolutional neural networks. To mine more multichannel images' information and further explore the performance of different fusion types, the paper proposes new fusional neural networks called multichannel cross-fusion convolutional neural networks (McCfCNNs) with fusion types of "R+G+B/R+G+B/R+G+B" and "R+G/G+B/B+R" based on the tremendous strengths of information fusion. Experiments show that McCfCNNs obtain 0.07-6.09% relative performance improvement in comparison with their corresponding non-fusion convolutional neural networks (CNNs) on diverse datasets (such as CIFAR100, SVHN, CALTECH256, and IMAGENET) under a certain computational complexity. Hence, McCfCNNs with fusion types of "R+G+B/R+G+B/R+G+B" and "R+G/G+B/B+R" can learn more fully multichannel images' information, which provide a method and idea for processing multichannel information fusion, for example, remote sensing satellite images. [ABSTRACT FROM AUTHOR]

Machine learning and deep learning techniques are widely used in various domains to solve different problems. The prediction and recommendation type of problems exists in various domains. Due to the importance of such types of problems, obtaining solutions to such problems becomes a necessity. Researchers have used existing as well as developed various approaches to solving prediction and recommendation types of problems. This study presents a short review of various machine and deep-learning techniques to address the prediction and recommendation problems. The machine and deep learning techniques are reviewed and future research scope is discussed in this paper. [ABSTRACT FROM AUTHOR]

The study of recognizing multifaceted emotions through auditory, visual, and textual cues is a rapidly growing interdisciplinary field, encompassing the domains of psychology, computer science, and artificial intelligence. This paper investigates the spectrum of methodologies utilized to isolate and identify complex emotional states across these modalities, with the objective of delineating advancements and identifying areas for future investigation. Within the realm of sound, we explore progress in signal processing and machine learning techniques that facilitate the extraction of nuanced emotional indicators from vocal inflections and musical arrangements. Visual emotion recognition is evaluated through the effectiveness of facial recognition algorithms, analysis of body language, and integration of contextual environmental information. Text-based emotion recognition is examined using natural language processing techniques to perceive sentiment and emotional connotations from written language. Moreover, the paper considers the amalgamation of these distinct sources of emotional data, contemplating the challenges in constructing coherent models capable of interpreting multimodal inputs. Our methodology encompasses a meta-analysis of recent studies, evaluating the effectiveness and precision of diverse approaches and identifying commonly employed metrics for their assessment. The results suggest a preference towards deep learning and hybrid models that harness the strengths of multiple analytical techniques to enhance recognition rates. However, challenges such as the subjective nature of emotion, cultural disparities in expression, and the necessity for extensive, annotated datasets persist as significant hurdles. In conclusion, this review advocates for more nuanced datasets, enhanced interdisciplinary cooperation, and an ethical framework to govern the implementation of emotion recognition technologies. The potential applications for these technologies are expansive, ranging from healthcare to entertainment, and necessitate a concerted endeavor to refine and ethically integrate emotion recognition into our digital interactions. [ABSTRACT FROM AUTHOR]

In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the deepfake problem as a multiclass task, this research introduces a novel approach, considering the challenges posed by Stable Diffusion and StyleGAN2. The objective is to enhance understanding and efficacy in detecting manipulated content within a multiclass context. Novelty: This research distinguishes itself by approaching the deepfake detection problem as a multiclass task, introducing new challenges associated with Stable Diffusion and StyleGAN2. The study pioneers the exploration of ViTs in this domain, emphasizing their potential to extract global features for enhanced detection accuracy. The novelty lies in addressing the evolving landscape of deepfake creation and manipulation. Results and Conclusion: Through extensive experiments, the proposed method exhibits high effectiveness, achieving impressive detection accuracy, precision, and recall, and an F1 rate of 99.90% on a multiclass-prepared dataset. The results underscore the significant potential of ViTs in contributing to a more secure digital landscape by robustly addressing the challenges posed by deepfake content, particularly in the presence of Stable Diffusion and StyleGAN2. The proposed model outperformed when compared with state-of-the-art CNN-based models, i.e., ResNet-50 and VGG-16. [ABSTRACT FROM AUTHOR]

The displacement of transmission tower feet can seriously affect the safe operation of the tower, and the accuracy of structural health monitoring methods is limited at the present stage. The application of deep learning method provides new ideas for structural health monitoring of towers, but the current amount of tower vibration fault data is restricted to provide adequate training data for Deep Learning (DL). In this paper, we propose a DT-DL based tower foot displacement monitoring method, which firstly simulates the wind-induced vibration response data of the tower under each fault condition by finite element method. Then the vibration signal visualization and Data Transfer (DT) are used to add tower fault data samples to solve the problem of insufficient actual data quantity. Subsequently, the dynamic response test is carried out under different tower fault states, and the tower fault monitoring is carried out by the DL method. Finally, the proposed method is compared with the traditional online monitoring method, and it is found that this method can significantly improve the rate of convergence and recognition accuracy in the recognition process. The results show that the method can effectively identify the tower foot displacement state, which can greatly reduce the accidents that occurred due to the tower foot displacement. [ABSTRACT FROM AUTHOR]

The gold standard for diagnosing disorders of the small bowel is wireless capsule endoscopy (WCE). Capsule endoscopy appears to represent the future of effective diagnostic gastrointestinal (GI) endoscopy. As capsule endoscopy doesn't cause any discomfort, it stands a better chance of being adopted by patients than traditional colonoscopy and gastroscopy, making it a good option for detecting cancer or ulcerations. WCE can be helpful in obtaining images of the GI tract from the inside, but pinpointing exactly where the disease is located is still a major challenge. In this paper, reviewing of the studies dealing with the development of the endoscopy capsule and finding techniques and solutions to provide higher efficiency is presented. Also, the paper showed that the tendency to use artificial intelligence (AI) led to an increase in the accuracy of detecting diseases and a decrease in mistakes caused by physicians' lack of attention or fatigue while reading a video from a capsule, as well as the role of artificial intelligence in shortening the time it takes to read the video. When it comes to WCE, deep learning has shown remarkable success in detecting a wide variety of disorders. [ABSTRACT FROM AUTHOR]

Edge AI accelerators are utilized to accelerate the computation in edge AI devices such as image recognition sensors on robotics, door lockers, drones, and remote sensing satellites. Instead of using a general-purpose processor (GPP) or graphic processing unit (GPU), an edge AI accelerator brings a customized design to meet the requirements of the edge environment. The requirements include real-time processing, low-power consumption, and resource-awareness, including resources on field programmable gate array (FPGA) or limited application-specific integrated circuit (ASIC) area. The system's reliability (e.g., permanent fault tolerance) is essential if the devices target radiation fields such as space and nuclear power stations. This paper proposes a dynamic reconfigurable column streaming-based convolution engine (DycSe) with programmable adder modules for low-power and resource-aware edge AI accelerators to meet the requirements. The proposed DycSe design does not target the FPGA platform only. Instead, it is an intellectual property (IP) core design. The FPGA platform used in this paper is for prototyping the design evaluation. This paper uses the Vivado synthesis tool to evaluate the power consumption and resource usage of DycSe. Since the synthesis tool is limited to giving the final complete system result in the designing stage, we compare DycSe to a commercial edge AI accelerator for cross-reference with other state-of-the-art works. The commercial architecture shares the competitive performance within the low-power ultra-small (LPUS) edge AI scopes. The result shows that DycSe contains 3.56% less power consumption and slight resources (1%) overhead with reconfigurable flexibility. [ABSTRACT FROM AUTHOR]

The use of intelligent machines to work and react like humans is vital in emerging smart cities. Computer-aided analysis of complex and huge MRI (Magnetic Resonance Imaging) scans is very important in healthcare applications. Among AI (Artificial Intelligence) driven healthcare applications, tumor detection is one of the contemporary research fields that have become attractive to researchers. There are several modalities of imaging performed on the brain for the purpose of tumor detection. This paper offers a deep learning approach for detecting brain tumors from MR (Magnetic Resonance) images based on changes in the division of the training and testing data and the structure of the CNN (Convolutional Neural Network) layers. The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy, including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states. The dataset is divided into test, and train subsets with a ratio of the training set to the validation set of 70:30. The main contribution of this paper is introducing an optimum deep learning structure of CNN layers. The simulation results are obtained for 50 epochs in the training phase. The simulation results reveal that the optimum CNN architecture consists of four layers. [ABSTRACT FROM AUTHOR]

Due to the considerable number of deaths and vehicle accidents caused by a driver's inattention, as reported by WHO, automobile manufacturers are aiming to combine advanced driver assistance systems (ADAS) with artificial intelligence algorithms, particularly deep learning and computer vision techniques. One feature that assists drivers is traffic sign recognition, which is a technique that allows vehicles to detect and recognize road signs placed on the road. This can be achieved by the aid of computer vision and Convolutional Neural Networks (CNN). The main aim of this research is to propose and improve a CNN based-model that can be efficiently and accurately applied for embedded applications, this might be accomplished with the help of several preprocessing algorithms. An improved network model called LeNet-5 has been developed for the classification of traffic signs. Furthermore, the proposed model network is trained using both German Traffic Sign Recognition Benchmark (GTSRB) and extended GTSRB (EGTSRB) datasets. According to the test results, the improved LeNet-5 architecture obtained an accuracy of 99.12% on GTSRB and 99.78% on EGTSRB datasets respectively, which has a positive performance compared to other state-of-the-art papers in terms of accuracy. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence, autonomous machines are featured with the ability to diagnose and assess the structural health of different systems. This paper presents a scalable mobile platform employed to autonomously and intelligently detect online small cracks on roads using a live camera feed and Artificial Intelligence (AI) methods. The robotic artifact is equipped with a vision-based localization system to enable autonomous navigation areas where GPS (Global Positioning System) may be poor or intermittent. The proposed approach runs at the edge a model of Convolutional Neuronal Networks (CNN) based on the Resnet 18 architecture to classify the image feed between cracks and those without cracks after training them with a combination of two public data sets and a data set generated in-house. The mobile robotic platform is scalable, depending on the particular context and requirements of the application. As opposed to off-line assessment tools, experimental results show the real-time capabilities of the system to autonomously navigate and detect cracks on a pavement structure with an accuracy of 95%. [ABSTRACT FROM AUTHOR]

Large communications of voice and images over the Internet, which leads to limited space for very large data during the aforementioned correspondence, to overcome this issue to maintain the quality of this technology. The proposal in the efficient algorithm in this work is a method to derive the two new filters from the second and third Chebyshev polynomials by forming the discrete wavelets with the mother wavelet to be used in image processing in order to overcome the problem mentioned above due to the correspondence, The filters that were derived are Filter Discrete Second Chebyshev Wavelets Transform (FDSCWT) and Filter Discrete Third Chebyshev Wavelets Transform (FDTCWT) to process the image by analysis, noise reduction, and image compression. Many of the techniques previously used in the field of image processing do not preserve image information during processing, but when using the new technology proposed in this work, it has been proven to preserve the image with its important information and data through the readings obtained shown in the tables below. These readings are average. Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Bits Per Pixel (BPP), and Compression Ratio (CR) in preprocessing. After the initial processing stage, the deep learning stage begins in the field of artificial intelligence. A (CNN) is trained with the two new filters to be the first Discrete Second Chebyshev Wavelets (DSCWCNN) and the second Discrete Third Chebyshev Wavelets Convolutional Neural Network (DTCWCNN), with the code being generated in the MATLAB program with a network Alex Net to complete the classification process that was added in this work to implement the recognition technology. Faces detection with new filters in deep learning to be a unique experience to reach a high level of accuracy of 98.60 % with the network for the filter DSCWCNN and 98.92 % with the network for the filter DTCWCNN in a very short time, which will be mentioned later in the work. [ABSTRACT FROM AUTHOR]

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In specific areas, notably remote sensing, datasets have increased in extent and resolution to the point that manual interpretation is problematic and the availability of skilled interpreters to undertake such work is limited. Interpretation of the geophysical datasets associated with prehistoric submerged landscapes is particularly challenging. Following the Last Glacial Maximum, sea levels rose by 120 m globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing datasets were provided by the offshore energy sector. In this paper, we provide the results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c. 188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7000 BP, along with the cultural heritage it contained. As part of this project, machine learning tools were applied to detect and interpret features with potential archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes. [ABSTRACT FROM AUTHOR]

Title: Real-Time Voice Cloning System Using Deep Learning, an emerging field in artificial intelligence, has witnessed significant advancements in recent years owing to the rapid progress of deep learning techniques. This survey paper delves into the realm of real-time voice cloning systems that employ deep learning methodologies. The ability to generate highly realistic and naturalsounding speech from limited audio samples has garnered attention due to its potential applications in entertainment, assistive technology, virtual assistants, and more. This survey provides an in-depth analysis of the key components and techniques employed in real-time voice cloning systems. We explore various neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) that have been utilized for voice cloning tasks. Additionally, we investigate the role of different training paradigms, including supervised, semi-supervised, and unsupervised learning, and discuss their implications on cloning accuracy and efficiency. Furthermore, the paper examines datasets used for training and evaluation, ranging from large-scale multilingual corpora to more specialized speech datasets. Framework has the capability to duplicate voices not encountered during training as well as generate speech from previously unseen text. [ABSTRACT FROM AUTHOR]

Mineral particle size is an important parameter in the mineral beneficiation process. In industrial processes, the grinding process produces pulp with qualified particle size for subsequent flotation processes. In this paper, a hierarchical intelligent control method for mineral particle size based on machine learning is proposed. In the machine learning layer, artificial intelligence technologies such as long and short memory neural networks (LSTM) and convolution neural networks (CNN) are used to solve the multi-source ore blending prediction and intelligent classification of dry and rainy season conditions, and then the ore-feeding intelligent expert control system and grinding process intelligent expert system are used to coordinate the production of semi-autogenous mill and Ball mill and Hydrocyclone (SAB) process and intelligently adjust the control parameters of DCS layer. This paper presents the practical application of the method in the SAB production process of an international mine to realize automation and intelligence. The process throughput is increased by 6.05%, the power consumption is reduced by 7.25%, and the annual economic benefit has been significantly improved. [ABSTRACT FROM AUTHOR]

The advancements in the domain of video coding technologies are tremendously fluctuating in recent years. As the public got acquainted with the creation and availability of videos through internet boom and video acquisition devices including mobile phones, camera etc., the necessity of video compression become crucial. The resolution variance (4 K, 2 K etc.), framerate, display is some of the features that glorifies the importance of compression. Improving compression ratio with better efficiency and quality was the focus and it has many stumbling blocks to achieve it. The era of artificial intelligence, neural network, and especially deep learning provided light in the path of video processing area, particularly in compression. The paper mainly focuses on a precise, organized, meticulous review of the impact of deep learning on video compression. The content adaptivity quality of deep learning marks its importance in video compression to traditional signal processing. The development of intelligent and self-trained steps in video compression with deep learning is reviewed in detail. The relevant and noteworthy work that arose in each step of compression is inculcated in this paper. A detailed survey in the development of intra- prediction, inter-prediction, in-loop filtering, quantization, and entropy coding in hand with deep learning techniques are pointed along with envisages ideas in each field. The future scope of enhancement in various stages of compression and relevant research scope to explore with Deep Learning is emphasized. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development. [ABSTRACT FROM AUTHOR]

This paper presents survey on various techniques used to classify plants and its disease. Classification is concerned with classifying each sample into different classes. Classification is a method of separating a healthy and diseased leaf on its morphological features such as texture, color, shape, pattern and so on. Due to resemblance in the visual properties among plants, sorting and classification are complicated to carry out especially in large area. There are various methods based on image processing techniques and computer vision. Choosing the suitable classification technique is quite difficult as the result varies on different input data. Classification of leaf diseases in plants has wide applications in different fields such as agriculture and biological research. This paper provides a general idea of few existing methods, its pros and cons, state of art of different techniques used by several authors in leaf disease identification and classification such as preprocessing techniques, feature extraction and selection techniques, datasets used, classifiers and performance metrics. Apart from these some challenges and research gaps are identified and their probable solutions are pointed out. [ABSTRACT FROM AUTHOR]

Radio signal recognition has a wide application in future communication systems and the vehicular communication, whose core is the extraction of signal features such as electromagnetic fingerprints. With the rapid development of artificial intelligence technology, deep learning has made amazing breakthroughs in image recognition, speech recognition and other fields. Deep learning is applied to electromagnetic fingerprint extraction in this paper. Firstly, thousands of the downlink aircraft communications addressing and reporting system (ACARS) signals used for communication between civil aircraft and airport tower are collected and generated. Then a pre-transformation network suitable for electromagnetic signals is constructed to convert one-dimensional signals into two-dimensional feature maps, and afterwards the feature maps are input into the convolutional neural network (CNN) for classification. By adopting the attention modules, the classification results were improved by a few percentage points over the baseline with a little cost. The method proposed in this paper achieves an accuracy rate of 94.1% and can obtain the aircraft type in a shorter time than traditional method. Moreover, the robustness of the proposed model in response to additive Gaussian white noise (AWGN) and phase deviation is studied and tested. [ABSTRACT FROM AUTHOR]

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With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment. [ABSTRACT FROM AUTHOR]

Affective computing is a multidisciplinary field encompassing artificial intelligence, natural language processing, linguistics, computer science, and social sciences. This field aims to deepen our comprehension and capabilities by deploying inventive algorithms. This article presents a groundbreaking approach, the Cumulative Attribute-Weighted Graph Neural Network, which is innovatively designed to integrate trimodal textual, audio, and visual data from the two multimodal datasets. This method exemplifies its effectiveness in performing comprehensive multimodal sentiment analysis. Our methodology employs vocal inputs to generate speaker embeddings trimodal analysis. Using a weighted graph structure, our model facilitates the efficient integration of these diverse modalities. This approach underscores the interrelated aspects of various emotional indicators. The paper's significant contribution is underscored by its experimental results. Our novel algorithm achieved impressive performance metrics on the CMU-MOSI dataset, with an accuracy of 94% and precision, recall, and F1-scores above 92% for Negative, Neutral, and Positive emotion categories. Similarly, on the IEMOCAP dataset, the algorithm demonstrated its robustness with an overall accuracy of 93%, where exceptionally high precision and recall were noted in the Neutral and Positive categories. These results mark a notable advancement over existing state-of-the-art models, illustrating the potential of our approach in enhancing Sentiment Recognition through the synergistic use of trimodal data. This study's comprehensive analysis and significant results demonstrate the proposed algorithm's effectiveness in nuanced emotional state recognition and pave the way for future advancements in affective computing, emphasizing the value of integrating multimodal data for improved accuracy and robustness. [ABSTRACT FROM AUTHOR]

Melanoma is a highly prevalent and lethal form of skin cancer, which has a significant impact globally. The chances of recovery for melanoma patients substantially improve with early detection. Currently, deep learning (DL) methods are gaining popularity in assisting with the identification of diseases using medical imaging. The paper introduces a computational model for classifying melanoma skin cancer images using convolutional neural networks (CNNs) and vision transformers (ViT) with the HAM10000 dataset. Both approaches utilize mask-guided techniques, employing a specialized U2-Net segmentation module to generate masks. The CNN-based approach utilizes ResNet50, VGG16, and Xception with transfer learning. The training process is enhanced using a Bayesian hyperparameter tuner. Moreover, this study applies gradient-weighted class activation mapping (Grad-CAM) and Grad-CAM++ to generate heatmaps to explain the classification models. These visual heatmaps elucidate the contribution of each input region to the classification outcome. The CNN-based model approach achieved the highest accuracy at 98.37% in the Xception model with a sensitivity and specificity of 95.92% and 99.01%, respectively. The ViT-based model approach achieved high values for accuracy, sensitivity, and specificity, such as 92.79%, 91.09%, and 93.54%, respectively. Furthermore, the performance of the model was assessed through intersection over union (IOU) and other qualitative evaluations. Finally, we developed the proposed model as a web application that can be used as a support tool for medical practitioners in real-time. The system usability study score of 86.87% is reported, which shows the usefulness of the proposed solution. [ABSTRACT FROM AUTHOR]

English has become the most widely used language in the world. Everything we do in study, life and work is closely linked with English. With the continuous development of computer technology, machine translation is becoming more and more mature. The convergence of Artificial Intelligence (AI) and language learning is getting increasingly close, which brings great impact and challenge to the language education industry, but also provides an opportunity for the synchronous promotion of the development of the language education industry. With the further development of AI, machine translation can better meet the needs of most general translation, but in the face of professional, diversified, detailed and complex communication translation tasks containing human emotion, machine translation is still difficult to replace human translation. In order to improve the English translation ability of university students, this paper uses AI to propose the innovative factor based Quantum Particle Swarm Optimization-Convolutional Neural Network (QPSO-CNN) algorithm. Through the experiment, at first, the obtained dataset can ensure the accuracy and diversity of the collected results of English translation feature samples to the maximum extent, and the trained QPSO-CNN can be used to analyze the accuracy of the English translation ability of university students. Then, by comparing the convergence curve of QPSO-CNN and back propagation-CNN (BP-CNN), it is concluded that the proposed QPSO-CNN in this paper has been greatly improved in terms of model accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Pneumonia is a common infectious disease. Currently, the most common method of pneumonia identification is manual diagnosis by professional doctors, but the accuracy and identification efficiency of this method is not satisfactory, and computer-aided diagnosis technology has emerged. With the development of artificial intelligence, deep learning has also been applied to pneumonia diagnosis and can achieve high accuracy. In this paper, we compare five deep learning models in different situations for pneumonia recognition. The objective was to employ five deep learning models to identify pneumonia X-ray images and to compare and analyze them in different cases, thus screening out the optimal model for each type of case to improve the efficiency of pneumonia recognition and further apply it to the computer-aided diagnosis of pneumonia species. In the proposed framework: (1) datasets are collected and processed, (2) five deep learning models for pneumonia recognition are built, (3) the five models are compared, and the optimal model for each case is selected. The results show that the LeNet5 and AlexNet models achieved better pneumonia recognition for small datasets, while the MobileNet and ResNet18 models were more suitable for pneumonia recognition for large datasets. The comparative analysis of each model under different situations can provide a deeper understanding of the efficiency of each model in identifying pneumonia, thus making the practical application and selection of deep learning models for pneumonia recognition more convenient. [ABSTRACT FROM AUTHOR]

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people's emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people's emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a system for drug–target interaction prediction using artificial intelligence which involves development of both machine learning and deep learning-based systems. In this paper, we use a convolutional neural network (CNN) model, to classify drug–target interactions between drug pairs. Applied to the DDI-Corpus dataset, the single CNN model achieve performance with an F1-score of 0.82 ± 0.012 for the single model and 0.81 ± 0.015 for the ensemble model using deep learning-based CNN with an approved accuracy of 96.72% which is an extra-ordinary achievement. This work has also been performed using the machine learning-based classifiers support vector machine (SVM). For machine learning-based implementation, drug-bank dataset was used for the training and testing. The main challenge when using machine learning for this purpose is the availability of negative DTI to train on. Training machine learning model, the SVM achieved an area under the ROC curve (AUC) of 0.753 ± 0.006, which taking the difference in computational resources into consideration compares well to the AUC of 0.886 ± 0.010 network-based state-of-the-art approach. We achieved and best accuracy of 93.76% using SVM after testing several times. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive overview of the applications of game theory in deep learning. Today, deep learning is a fast-evolving area for research in the domain of artificial intelligence. Alternatively, game theory has been showing its multi-dimensional applications in the last few decades. The application of game theory to deep learning includes another dimension in research. Game theory helps to model or solve various deep learning-based problems. Existing research contributions demonstrate that game theory is a potential approach to improve results in deep learning models. The design of deep learning models often involves a game-theoretic approach. Most of the classification problems which popularly employ a deep learning approach can be seen as a Stackelberg game. Generative Adversarial Network (GAN) is a deep learning architecture that has gained popularity in solving complex computer vision problems. GANs have their roots in game theory. The training of the generators and discriminators in GANs is essentially a two-player zero-sum game that allows the model to learn complex functions. This paper will give researchers an extensive account of significant contributions which have taken place in deep learning using game-theoretic concepts thus, giving a clear insight, challenges, and future directions. The current study also details various real-time applications of existing literature, valuable datasets in the field, and the popularity of this research area in recent years of publications and citations. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) combines computer science and robust datasets to mimic natural intelligence demonstrated by human beings to aid in problem-solving and decision-making involving consciousness up to a certain extent. From Apple's virtual personal assistant, Siri, to Tesla's self-driving cars, research and development in the field of AI is progressing rapidly along with privacy concerns surrounding the usage and storage of user data on external servers which has further fueled the need of modern ultra-efficient AI networks and algorithms. The scope of the work presented within this paper focuses on introducing a modern image classifier which is a light-weight and ultra-efficient CNN intended to be deployed on local embedded systems, also known as edge devices, for general-purpose usage. This work is an extension of the award-winning paper entitled 'CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems' published for the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). The proposed neural network dubbed CondenseNeXtV2 utilizes a new self-querying augmentation policy technique on the target dataset along with adaption to the latest version of PyTorch framework and activation functions resulting in improved efficiency in image classification computation and accuracy. Finally, we deploy the trained weights of CondenseNeXtV2 on NXP BlueBox which is an edge device designed to serve as a development platform for self-driving cars, and conclusions will be extrapolated accordingly. [ABSTRACT FROM AUTHOR]

Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients. [ABSTRACT FROM AUTHOR]

A bearing is a key component in rotating machinery. The prompt monitoring of a bearings' condition is critical for the reduction of mechanical accidents. With the rapid development of artificial intelligence technology in recent years, machine learning-based intelligent fault diagnosis (IFD) methods have achieved remarkable success in the field of bearing condition monitoring. However, most algorithms are developed based on computer platforms that focus on analyzing offline, rather than real-time, signals. In this paper, an edge intelligence diagnosis method called S-AlexNet, which is based on a parameter transplantation convolutional neural network (CNN), is proposed. The method deploys the lightweight IFD method in a low-cost embedded system to monitor the bearing status in real time. Firstly, a lightweight IFD algorithm model is designed for embedded systems. The model is trained on a PC to obtain optimal parameters, such as the model's weights and bias. Finally, the optimal parameters are transplanted into the embedded system model to identify the bearing status on the edge side. Two datasets were used to validate the performance of the proposed method. The validation using the CWRU dataset shows that the proposed method achieves an average prediction accuracy of 94.4% on the test set. The validation using self-built data shows that the proposed method can identify bearing operating status in embedded systems with an average prediction accuracy of 99.81%. The results indicate that the proposed method has the advantages of high recognition accuracy, low model complexity, low cost, and high portability, which allow for the simple and effective implementation of the edge IFD of bearings in embedded systems. [ABSTRACT FROM AUTHOR]