A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis.

Leukemia is a serious problem affecting both children and adults, leading to death if left untreated. Leukemia is a kind of blood cancer described by the rapid proliferation of abnormal blood cells. An early, trustworthy, and precise identification of leukemia is important to treating and saving patients' lives. Acute and myelogenous lymphocytic, chronic and myelogenous leukemia are the four kinds of leukemia. Manual inspection of microscopic images is frequently used to identify these malignant growth cells. Leukemia symptoms include fatigue, a lack of enthusiasm, a dull appearance, recurring illnesses, and easy blood loss. Identifying subtypes of leukemia for specialized therapy is one of the hurdles in this area. The suggested work predicts and classifies leukemia subtypes in gene data CuMiDa (GSE9476) using feature selection and ML techniques. The Curated Microarray Database (CuMiDa) collected 64 samples representing five classes of leukemia genes out of 22283 genes. The proposed approach utilizes the 25 most differentiating selected features for classification using machine and deep learning techniques. This study has a classification accuracy of 96.15% using Random Fores, 92.30 using Linear Regression, 96.15% using SVM, and 100% using LSTM. Deep learning methods have been shown to outperform traditional methods in leukemia gene classification by utilizing specific features.

Chapter 10 - Contemporary survey on effectiveness of machine and deep learning techniques for cyber security

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

A comprehensive review on detection of plant disease using machine learning and deep learning approaches

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

Rapid advancements in communication technology have supported the invention of various internet-based devices. These devices communicate with one another and provide data from the physical world. Nowadays, the internet connected devices are used in various fields to make things easier. A great number of devices has been used, depending upon requirements. At the same time, the data produced by such devices is gradually increasing. To process the collected data, machine learning and deep learning techniques are applied. The Internet of Things (IoT) produces big datasets with multiple modalities but also a range of data with different quality standards. It is an important but also a challenging task to process all of the data within a certain time-frame. In this scenario, cloud computing gives us the optimal solution since the data generated is sent to distant cloud infrastructures. In addition to the cloud technology, machine learning (ML) and deep learning (DL) techniques are integrated with cloud computing to improve the effectiveness. In ML technique, the training data is given for learning to generate a set of rules from inferences on the data. Huge amounts of data that has been stored in the cloud gives input to DL techniques. DL architecture has been derived from the Artificial Neural Network (ANN) that uses multiple layers of nonlinear processing and transformation. The deep learning approach uses unknown elements in the input data to group objects, generate features and find new data patterns to build the model.

The rise of internet connectivity across the globe increases the count of IoT (internet of things)/IIoT (industrial internet of things) devices exponentially. The objects/devices which are connected to the internet are always prone to malicious attacks at various levels, such as physical, network, fog, and applications, which exist in the IoT architecture. Many researchers have addressed this issue and designed their own solutions based on machine and deep learning techniques. It is undeniable that deep learning outperforms machine learning (ML), but it necessitates a massive amount of datasets with appropriate labels. In this work, the deep transfer learning (TL) technique has been adapted for gated recurrent unit (GRU). Each model is trained using a dataset that belongs to one source IoT device (source domain), and this trained model is used to classify the malicious traffic in another dataset that belongs to some other IoT device (target domain). This approach is used for binary classification. These transfer learning models have been evaluated using an IoT/IIoT telemetry dataset called ToN IoT which comprises the sensor data generated from the seven different types of IoT devices. The highest accuracy achieved by IoT garage door was upto 99.76% as a source domain by fixing IoT thermostat as target domain. These models were also evaluated using some more metrics such as precision, recall, F1-measure, training time and testing time. By implementing transfer learning based GRU model, the accuracy of the model is improved from 69.20% to 99.76%. Moreover, to prove the efficiency of the proposed model, it is compared with state of art deep learning model and its results were analyzed in a detailed manner.

Acute Lymphoblastic Leukemia (ALL) is a life-threatening blood cancer that mostly alters the white blood cells, impairing the immune system's ability to fight infections. Early and accurate detection of ALL is critical for prompt treatment and improving patient outcomes. However, traditional diagnostic methods mainly depends on manual examination of blood cells, which is time-consuming, prone to error, and dependent on specialized expertise. To address these challenges, this study probes the applications of deep learning (DL) and machine learning (ML) techniques for automating and enhancing the detection of ALL from blood cell images. Using a convolutional neural network (CNN) architecture with pretrained weights, this project leverages transfer learning to efficiently classify blood cells as cancerous or non-cancerous. The methodology involves systematic data preprocessing, augmentation, and splitting into training, validation, and test sets to guarantee strong model performance. Data augmentation techniques such as blurring, noise addition, and flipping are applied to improve the diversity of training samples, increasing the model's ability to generalize across different datasets. The results demonstrate the efficacy of using ML models for ALL detection, achieving high classification accuracy and offering significant improvements over traditional methods in terms of speed and scalability. The model's performance is evaluated using confusion matrices and visual plots to identify areas for optimization. The study also highlights the potential for integrating these tools into real-world clinical workflows, particularly in resource-constrained settings.

Deep learning (DL) is now generally acknowledged as the benchmark and evolution in machine learning (ML) fields. Further more, it has steadily suited the most extensively in computational techniques for ML, delivering excellent outcomes in several challenging intellectual works that have equal or still outperformed human ability. DL has the advantage of learning from enormous amounts of data. Here, the DL approach has been applied to classify thunderstorms and non-thunderstorms with a meteorological dataset. Daily observational hourly data sets from 2016 to 2021 have been used to classify the incidence of thunderstorms and non-thunderstorms. Three DL approaches are used: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) to classify thunderstorm incidences. These DL techniques are compared with conventional ML techniques, showing that DL approaches outperformed ML. The CNN method provides the highest performance with 92.01% accuracy among all DL and ML approaches.

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.

The problems of plant leaf disease are rather serious in the world agricultural industry, leading to a significant decrease in crop quantity and quality, consequently, resulting in a huge loss in the economy and food insecurity. Detection and successful classification of plant diseases at the initial stage is essential to further agricultural output and the quality production of food. The recent improvements in the field of artificial intelligence (AI), specifically, machine learning (ML) and deep learning (DL), have shown significant prospects in automating and enhancing methods of diagnosing plant leaf diseases by using a wide variety of ML and DL algorithms. This review article presents an in-depth analysis of thirty novel methods created by researchers to diagnose and classify plant leaf diseases. They are such conventional classifiers as Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN), along with some advanced DL architectures, such as Convolutional Neural Networks (CNN), VGG16, ResNet50, InceptionResNetV2, EfficientNet, and various hybrids. The review analysis takes into consideration the methodologies applied, performance metrics, and insights in practice, as well as the strengths and weaknesses of both. The most crucial findings of the review show that deep learning models, and CNNs in particular, tend to be more accurate, robust, and feature extractors than traditional models of MLs. The performance of classification is also enhanced by numerous hybrid models that will use ML together with DL, and transfer learning has been an effective method to enhance the generalization using small datasets. Nevertheless, with all this progress, the issues of diversity of datasets, computational resource requirements and model interpretability are still to be explored in the future.

The Internet of Things (IoT) has transformed many aspects of modern life, from healthcare and transportation to home automation and industrial control systems. However, the increasing number of connected devices has also led to an increase in security threats, particularly from botnets. To mitigate these threats, various machine learning (ML) and deep learning (DL) techniques have been proposed for IoT botnet attack detection. This systematic review aims to identify the most effective ML and DL techniques for detecting IoT botnets by delving into benchmark datasets, evaluation metrics, and data pre-processing techniques in detail. A comprehensive search was conducted in multiple databases for primary studies published between 2018 and 2023. Studies were included if they reported the use of ML or DL techniques for IoT botnet detection. After screening 1,567 records, 25 studies were included in the final review. The findings suggest that ML and DL techniques show promising results in detecting IoT botnet attacks, outperforming traditional signature-based methods. However, the effectiveness of the techniques varied depending on the dataset, features, and evaluation metrics used. Based on the synthesis of the findings, this review proposes a taxonomy for ML and DL techniques in IoT botnet attack detection and provides recommendations for future research in this area. This review illuminates the considerable potential of ML and DL approaches in bolstering the detection of IoT botnet attacks, thereby offering valuable insights to researchers involved in the domain of IoT security.

Cardiovascular diseases like arrhythmia are a significant health concern worldwide, affecting both elderly and young population due to lifestlye changes. Early diagnosis of cardiac arrhythmia using Electrocardiogram (ECG) by trained cardiologists is vital to prevent heart ailments and save lives. With the growth of wearable and standard ECG monitoring devices and a dearth of qualified cardiologists required to analyse the vast amounts of data collected, automated arrhythmia detection by Machine Learning (ML) and Deep Learning (DL) techniques have become very popular in recent years. In this study, we have reviewed the literature and described standard ML and DL studies in ECG arrhythmia classification. While ML techniques do demonstrate very good metrics, ML classifiers like SVM, knearest-neighbours, Decision Trees, etc. need preprocessing and hand-crafted feature extraction. DL methods which use networks like Convolutional Neural Networks (CNN), Long-Short-Term-Memory (LSTM) do not need any feature extraction as they automatically learn the features by themselves. Recent studies in DL have demonstrated very high performance metrics without the need for feature extraction. While some DL techniques do need noise filtering and determination of other features like the QRS complex, many of them can work with raw ECG signals and hence are ideally suited over their ML counterparts for real time ECG classification. DL networks can also be used as feature extractors and combined with ML classifiers. We thus conclude that state-of-the-art DL methods offer inherent advantages and flexibility over ML methods for automated arrhythmia classification. This review aggregates the niche features of leading ML and DL studies in this field which interested researchers can benefit from.

Special Issue on Machine Learning for Single Cell Data.