PAPR Reduction based on Deep Learning Autoencoder in Coherent Optical OFDM Systems

This paper presents an innovative approach to reducing Peak-to-Average Power Ratio (PAPR) in Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM) systems. The proposed deep learning autoencoder-based model eliminates the computational complexity of existing PAPR reduction techniques, such as Selective Mapping (SLM), by leveraging a novel decoder architecture at the receiver. In addition, No side information is needed in our approach, unlike SLM which requires knowledge of the PAPR distribution. Simulation results demonstrate significant improvements in both PAPR reduction and Bit Error Rate (BER) performance compared to traditional techniques. It achieves error-free transmission with over 10 dB PAPR reduction compared to unmitigated and 1 dB gain over SLM technique. Furthermore, our approach exhibits robustness against noise and nonlinearity effects, enabling reliable transmission over optical channels with varying levels of impairment. The proposed technique has far-reaching implications for next-generation optical communication systems, where efficient PAPR reduction is crucial for ensuring reliable data transfer.

Deep Transfer Learning for Kidney Cancer Diagnosis

Many incurable diseases prevalent across global societies stem from various influences, including lifestyle choices, economic conditions, social factors, and genetics. Research predominantly focuses on these diseases due to their widespread nature, aiming to decrease mortality, enhance treatment options, and improve healthcare standards. Among these, kidney disease stands out as a particularly severe condition affecting men and women worldwide. Nonetheless, there is a pressing need for continued research into innovative, early diagnostic methods to develop more effective treatments for such diseases. Recently, automatic diagnosis of Kidney Cancer has become an important challenge especially when using deep learning (DL) due to the importance of training medical datasets, which in most cases are difficult and expensive to obtain. Furthermore, in most cases, algorithms require data from the same domain and a powerful computer with efficient storage capacity. To overcome this issue, a new type of learning known as transfer learning (TL) has been proposed that can produce impressive results based on other different pre-trained data. This paper presents, to the best of the authors' knowledge, the first comprehensive survey of DL-based TL frameworks for kidney cancer diagnosis. This is a strong contribution to help researchers understand the current challenges and perspectives of this topic. Hence, the main limitations and advantages of each framework are identified and detailed critical analyses are provided. Looking ahead, the article identifies promising directions for future research. Moving on, the discussion is concluded by reflecting on the pivotal role of TL in the development of precision medicine and its effects on clinical practice and research in oncology.

Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data's quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL's background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions.

AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI

Artificial Intelligence (AI) is a pervasive research topic, permeating various sectors and applications. In this study, we harness the power of AI, specifically convolutional neural networks (ConvNets), for segmenting liver tissues. It also focuses on developing a user-friendly graphical user interface (GUI) tool, "AI Radiologist", enabling clinicians to effectively delineate different liver tissues (parenchyma, tumors, and vessels), thereby saving lives. This endeavor bridges the gap between academic research and practical, industrial applications. The GUI is a single-page application and is designed using the PyQt5 Python framework. The offline-available AI Radiologist resorts to three ConvNet models trained to segment all liver tissues. With respect to the Dice metric, the best liver ConvNet scores 98.16%, the best tumor ConvNet scores 65.95%, and the best vessel ConvNet scores 51.94%. It outputs 2D slices of the liver, tumors, and vessels, along with 3D interpolations in .obj and .mtl formats, which can be visualized/printed using any 3D-compatible software. Thus, the AI Radiologist offers a convenient tool for clinicians to perform liver tissue segmentation and 3D interpolation employing state-of-the-art models for tissues segmentation. With the provided capacity to select the volumes and pre-trained models, the clinicians can leave the rest to the AI Radiologist.

Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD

Machine learning has revolutionized the field of agricultural science, particularly in the early detection and management of plant diseases, which are crucial for maintaining crop health and productivity. Leveraging advanced algorithms and imaging technologies, researchers are now able to identify and classify plant diseases with unprecedented accuracy and speed. Effective management of tomato diseases is crucial for enhancing agricultural productivity. The development and application of tomato disease classification methods are central to this objective. This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases, utilizing insights from the latest pre-trained Convolutional Neural Network (CNN) models. We propose a sophisticated approach within the domain of tensor subspace learning, known as Higher-Order Whitened Singular Value Decomposition (HOWSVD), designed to boost the discriminatory power of the system. Our approach to Tensor Subspace Learning is methodically executed in two phases, beginning with HOWSVD and culminating in Multilinear Discriminant Analysis (MDA). The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets, namely PlantVillage and the Taiwan dataset. The findings reveal that HOWSVD-MDA outperforms existing methods, underscoring its capability to markedly enhance the precision and dependability of diagnosing tomato leaf diseases. For instance, up to 98.36\% and 89.39\% accuracy scores have been achieved under PlantVillage and the Taiwan datasets, respectively.

Federated and Transfer Learning for Cancer Detection Based on Image Analysis

This review article discusses the roles of federated learning (FL) and transfer learning (TL) in cancer detection based on image analysis. These two strategies powered by machine learning have drawn a lot of attention due to their potential to increase the precision and effectiveness of cancer diagnosis in light of the growing importance of machine learning techniques in cancer detection. FL enables the training of machine learning models on data distributed across multiple sites without the need for centralized data sharing, while TL allows for the transfer of knowledge from one task to another. A comprehensive assessment of the two methods, including their strengths, and weaknesses is presented. Moving on, their applications in cancer detection are discussed, including potential directions for the future. Finally, this article offers a thorough description of the functions of TL and FL in image-based cancer detection. The authors also make insightful suggestions for additional study in this rapidly developing area.

High-energy physics image classification: A Survey of Jet Applications

In recent times, the fields of high-energy physics (HEP) experimentation and phenomenological studies have seen the integration of machine learning (ML) and its specialized branch, deep learning (DL). This survey offers a comprehensive assessment of these applications within the realm of various DL approaches. The initial segment of the paper introduces the fundamentals encompassing diverse particle physics types and establishes criteria for evaluating particle physics in tandem with learning models. Following this, a comprehensive taxonomy is presented for representing HEP images, encompassing accessible datasets, intricate details of preprocessing techniques, and methods of feature extraction and selection. Subsequently, the focus shifts to an exploration of available artificial intelligence (AI) models tailored to HEP images, along with a concentrated examination of HEP image classification pertaining to Jet particles. Within this review, a profound investigation is undertaken into distinct ML and DL proposed state-of-the art (SOTA) techniques, underscoring their implications for HEP inquiries. The discussion delves into specific applications in substantial detail, including Jet tagging, Jet tracking, particle classification, and more. The survey culminates with an analysis concerning the present status of HEP grounded in DL methodologies, encompassing inherent challenges and prospective avenues for future research endeavors.

Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review

The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable. In particular, the challenge of identifying thyroid cancer (TC) has seen progress with the use of machine learning (ML) and big data analysis, incorporating transformers to evaluate TC prognosis and determine the risk of malignancy in individuals. This review article presents a summary of various studies on AIbased approaches, especially those employing transformers, for diagnosing TC. It introduces a new categorization system for these methods based on artifcial intelligence (AI) algorithms, the goals of the framework, and the computing environments used. Additionally, it scrutinizes and contrasts the available TC datasets by their features. The paper highlights the importance of AI instruments in aiding the diagnosis and treatment of TC through supervised, unsupervised, or mixed approaches, with a special focus on the ongoing importance of transformers in medical diagnostics and disease management. It further discusses the progress made and the continuing obstacles in this area. Lastly, it explores future directions and focuses within this research feld.

Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey

Recent advancements in deep learning (DL) have posed a significant challenge for automatic speech recognition (ASR). ASR relies on extensive training datasets, including confidential ones, and demands substantial computational and storage resources. Enabling adaptive systems improves ASR performance in dynamic environments. DL techniques assume training and testing data originate from the same domain, which is not always true. Advanced DL techniques like deep transfer learning (DTL), federated learning (FL), and reinforcement learning (RL) address these issues. DTL allows high-performance models using small yet related datasets, FL enables training on confidential data without dataset possession, and RL optimizes decision-making in dynamic environments, reducing computation costs. This survey offers a comprehensive review of DTL, FL, and RL-based ASR frameworks, aiming to provide insights into the latest developments and aid researchers and professionals in understanding the current challenges. Additionally, transformers, which are advanced DL techniques heavily used in proposed ASR frameworks, are considered in this survey for their ability to capture extensive dependencies in the input ASR sequence. The paper starts by presenting the background of DTL, FL, RL, and Transformers and then adopts a well-designed taxonomy to outline the state-of-the-art approaches. Subsequently, a critical analysis is conducted to identify the strengths and weaknesses of each framework. Additionally, a comparative study is presented to highlight the existing challenges, paving the way for future research opportunities.

Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities

Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep learning methods for this task, aiming to address the drawbacks and limitations of conventional approaches. Our proposed approach involves feature extraction from the data collected in sport facilities. We present a problem formulation using Deep Feedforward Neural Networks (DFNN) and introduce threshold estimation techniques to identify anomalies effectively. Furthermore, we propose methods to reduce false alarms, ensuring the reliability and accuracy of anomaly detection. To evaluate the effectiveness of our approach, we conduct experiments on aquatic center dataset at Qatar University. The results demonstrate the superiority of our deep learning-based method over conventional techniques, highlighting its potential in real-world applications. Typically, 94.33% accuracy and 92.92% F1-score have been achieved using the proposed scheme.