Machine Deep Learning Techniques Research Articles

Early Breast Cancer Prediction using Machine Learning and Deep Learning Techniques

Breast Cancer (BC) is a considered as one of the utmost lethal diseases across the globe that has a very high morbidity and mortality rate. Accurate and early prediction along with diagnosis is one of the most crucial characteristics for the treatment of Breast Cancer. Doctors can have an edge over Breast cancer if they are able to predict it in its early stages using deep learning and machine learning techniques. This paper proposed consists of comparison between the and accuracy of various machine learning models like Support vector machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), XGB Classifier and deep learning model of Artificial neural networks (ANN) for the precise detection of breast cancer.
 The most crucial properties from the database have been chosen using one feature-selection technique. Correlation is also used to choose the most correlated features from the data. Implementing the ANN model consists of one input layer, two hidden layers, and one output layer. All Machine Learning models and ANN model are then applied to selected features. The results demonstrated that the SVM classifier achieved the highest performance with an accuracy of ~98.24%.

The Transformative Power of Artificial Intelligence in Dentistry

Artificial Intelligence (AI) has emerged as a disruptive force in the field of dentistry, reshaping traditional practices and revolutionizing patient care. This abstract explores the transformative power of AI in dentistry, highlighting its potential to enhance diagnostics, treatment planning, and patient outcomes. AI-driven algorithms, powered by deep learning and machine learning techniques, have shown remarkable capabilities in the analysis of dental images, such as radiographs and intraoral scans, leading to more accurate and efficient disease detection. Moreover, AI systems have enabled personalized treatment planning by considering individual patient characteristics and historical data, leading to optimized treatment regimens. This abstract also delves into the role of AI in improving patient engagement and telemedicine services, making dental care more accessible and convenient. Furthermore, the integration of AI in dental practice management has streamlined administrative tasks, allowing dental professionals to focus more on patient care. AI-driven chatbots and virtual assistants have enhanced patient communication and appointment scheduling. However, the adoption of AI in dentistry is not without its challenges, including concerns about data privacy, ethical considerations, and the need for continuous training and education for dental professionals. In conclusion, the transformative power of AI in dentistry is undeniable, offering the potential to improve diagnostics, treatment planning, patient engagement, and practice management. Embracing AI technologies in a responsible and ethical manner has the potential to elevate the quality of dental care, making it more efficient, accurate, and patient-centric. This abstract underscores the need for continued research, regulation, and collaboration among dental professionals, technologists, and policymakers to unlock the full potential of AI in dentistry while addressing its associated challenges.

Application of deep learning and machine learning models to improve healthcare in sub-Saharan Africa: Emerging opportunities, trends and implications

Deep learning and machine learning techniques present unmatched opportunities to improve healthcare in sub-Saharan Africa (SSA). However, there is a paucity of literature on AI-based applications deployed to improve care in SSA, which makes it challenging to organise the research contributions in the present and to highlight obstacles and emerging research areas that need to be explored in the future. This study applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) model to conduct a comprehensive review of deep learning and machine learning models deployed in SSA to improve access to care while exploring emerging opportunities, trends and implications for integrating AI-based models in SSA healthcare. This study reveals that AI models can analyse and derive inferences from massive health data for early detection, diagnosis, monitoring for chronic disorders, prediction of diseases, monitoring large-scale public health patterns and help limit exposure in contagious environments. AI can facilitate the development of targeted health interventions and improve patient outcomes in all stages of diagnosis, treatment, drug development and monitoring, personalised medicine, patient control and care. Integrating AI models with health applications can tremendously assist health professionals and policymakers in disease diagnosis and making informed decisions. AI algorithms bias, poor access to health data and formats, and lack of policies and frameworks supporting the integration of data-driven AI-based solutions into health systems hinder the integration of AI-based models into health systems. There is a need for transparency and ethical use of AI and crafting policies that support the use of AI in SSA health systems. Utilising AI-based models in healthcare can also assist researchers and healthcare workers to move towards smart care and better comprehend future research needs of AI in smart care.

Application of artificial neural networks for personality traits prediction based on handwriting

The automatization of personality traits prediction still brings considerable research these days, especially when the detection could be achieved without administrating personality assessment instruments. With the evolution of computational intelligence, a variety of deep machine learning techniques were developed and proposed for that purpose. Nevertheless, proposing robust and rapid systems to solve this problem remains a challenging task. The process of feature extraction is the main key. This paper presents an effective method for extracting five graphological features from handwriting ensuring the prediction of personality traits based on the big five personality traits model. We started by collecting both handwriting samples and big five questionnaires, then the feature extraction process, after that the data preparation and finished with the application of several popular deep machine learning models to achieve the prediction. Experimental results indicate the remarkable performance of the multi-layer perceptron (MLP) compared to other classifiers, the model was 100% precise and classification accuracy attained 100% for trained data and 72.73% for new tested data. With only 100 participants, we strongly believe that our proposed method is simple and promising, and better results will be attained with a larger dataset.

Development of a deep learning-based model to diagnose mixed-type gastric cancer accurately.

The accurate diagnosis of mixed-type gastric cancer from pathology images presents a formidable challenge for pathologists, given its intricate features and resemblance to other subtypes of gastric cancer. Artificial Intelligence has the potential to overcome this hurdle. This study aimed to leverage deep machine learning techniques to establish a precise and efficient diagnostic approach for this cancer type which can also predict the metastatic risk using two software, U-Net and QuPath, which have not been trialled in gastric cancers. A U-Net neural network was trained to recognise, and segment differentiated componentsfrom 186 pathology images of mixed-type gastric cancer.Undifferentiated components in the same images were annotated using the open-source pathology imaging software QuPath. The outcomes from U-Net and QuPath were used to calculate the ratios of differentiation/undifferentiated components which were correlated to lymph node metastasis. The models established by U-Net recognised ∼91% of the regions of interest, with precision, recall, and F1 values of 90.2%, 90.9% and 94.6%, respectively, indicating a high level of accuracy and reliability. Furthermore, the receiver operating characteristic curve analysis showed an area under the cure of 91%, indicating good performance. A bell-curve correlation between the differentiated/undifferentiated ratio and lymphatic metastasis was found (highest risk between 0.683 and 1.03), which is paradigm-shifting. U-Net and QuPath exhibit promising accuracy in the identification of differentiated and undifferentiated components in mixed-type gastric cancer, as well as paradigm-shifting prediction of metastasis. These findings bring us one step closer to their potential clinical application.

A deep unsupervised saliency model for lung segmentation in chest X-ray images

Medical image segmentation is a critical and laborious step in the diagnosis workflow by separating regions of organs or anomalies to be analyzed further by a physician, or classified automatically by an intelligent tool. Deep machine learning techniques have achieved impressive results in image segmentation tasks whenever immense amounts of labeled data are provided. However, having big amounts of manually supervised image data is labor intensive, and costly impractical. Unsupervised learning, contrariwise, can provide results using unlabeled images by reaching decisions, clustering, and deriving insights from the data only. We propose a deep unsupervised learning approach to perform lung segmentation in chest X-ray images, which is guided by heuristic saliency maps as a noisy labeling framework, which through a composite loss of reconstruction and regularization reach saliency estimation as final segmentation. Tests were performed using public chest X-ray images from the Japanese Society of Radiological Technology (JSRT), and from Montgomery County (MC) datasets. The proposed model based on saliency scored a dice coefficient of 0.87, against a state-of-the-art unsupervised method, which scored 0.68, on JSRT, and on the MC dataset 0.88, and 0.64, respectively. Qualitatively comparing, the saliency model worked better in reducing background noise. Considering that unsupervised models can be used as robust preparation steps, and as baselines for further different tasks, either guided by constant feedback from physicians, or as input to semi-supervised learning, the results have shown this to be a promise line of research to medical image segmentation problems.

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

<span lang="EN-US">Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25<sup>th</sup>, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21<sup>st</sup>). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.</span>

Hand-Written Analysis and Recognition Based on Deep Learning and Machine Learning Techniques

Abstract: This paper presents a comprehensive comparative analysis of Support Vector Machines (SVM), along with other machine learning models, and Convolutional Neural Networks (CNN) for Handwritten Analysis and Recognition. We investigate the performance of these models on a multi-class pairwise classification task using a large-scale handwritten dataset. Evaluation metrics such as accuracy, precision, and recall are utilized to measure their performance and compare them against the CNN model. The study aims to provide insights into the strengths and weaknesses of SVM, as well as other machine learning models, in comparison to CNN for handwritten analysis and recognition. SVM, known for its ability to handle complex non-linear relationships, offers good generalization and interpretability. However, it may face challenges in capturing intricate patterns in handwritten data. Other machine learning models, such as k-Nearest Neighbors (k-NN) and decision trees, also offer different advantages and limitations in this context. Through extensive experimentation, we compare the performance of SVM, k-NN, decision trees, and other relevant machine learning models with CNN on the same handwritten dataset. We analyze their accuracies, precision, and recall rates to evaluate their effectiveness in multi-class pairwise recognition of handwritten samples. Additionally, we discuss the computational requirements, training times, and interpretability associated with each model. The findings of this study provide valuable insights into the suitability of SVM and other machine learning models when compared to CNN for handwritten analysis and recognition tasks. The results shed light on the trade-offs between traditional machine learning approaches and deep learning architectures in this domain. These insights can guide researchers and practitioners in choosing the most appropriate model based on their specific requirements, computational resources, and interpretability needs.

Enhancing Photovoltaic Module Fault Diagnosis with Unmanned Aerial Vehicles and Deep Learning-Based Image Analysis

Artificial intelligence (AI) has evolved into a powerful tool that has wide-spread application in computer vision such as computer-aided inspection, industrial control systems, and navigation of robots. Monitoring the condition of machineries and mechanical components for the presence of faults with the aid of image-based automated analysis is one major application of computer vision. Diagnosing machinery faults from images can be made feasible with the adoption of deep learning and machine learning techniques. The primary objective of this study is to detect malfunctions in photovoltaic (PV) modules by utilizing a combination of deep learning and machine learning methodologies, with the assistance of RGB images captured via unmanned aerial vehicles. Six test conditions of PV modules such as good panel, snail trail, delamination, glass breakage, discoloration, and burn marks were considered in the study. The overall experimentation was carried out in two phases: (i) deep learning phase and (ii) machine learning phase. In the initial deep learning phase, the final fully connected layer of six pretrained networks, namely, DenseNet-201, VGG19, ResNet-50, GoogLeNet, VGG16, and AlexNet, was utilized to extract PVM image features. During the machine learning phase, feature selection from the extracted features was carried out using the J48 decision tree algorithm. Post selection of features, three families of classifiers such as tree, Bayes, and lazy were applied to determine the best feature extractor-classifier pair. The combination of DenseNet-201 features with k-nearest neighbour (IBK) classifier produced the overall classification accuracy of 100.00% among all other pretrained network features and classifiers considered.

A Comparative Study of Deep Learning and Machine Learning Techniques in Credit Score Classification

The field of credit score classification has experienced notable progress through the introduction of deep learning (DL) and machine learning (ML) techniques, empowering financial institutions to make well-informed decisions about assessing creditworthiness. However, existing research often focuses on just a few classifiers pertaining to either ML or DL techniques, lacking a comprehensive comparative analysis between the two. This gap calls for a thorough study that evaluates and compares a wide range of ML classifiers and DL models in the context of credit scoring. Our work aims to address this limitation by presenting an extensive comparative analysis between different ML and DL approaches. We provide novel insights into the strengths and weaknesses of each model, enabling financial institutions to select the most suitable approach for their specific needs. Through conducting extensive experiments on a credit records dataset, we evaluated the accuracy, precision, recall, and F1 score of various ML classifiers, such as logistic regression, decision trees, and random forests. Additionally, we delved into the capabilities of DL models, which included multi-layer perceptron (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), and hybrid models. Our findings revealed that Random Forest achieved the highest test accuracy of 90.27, while MLP and CNN closely followed with the second-highest accuracies at 87.08 and 87.16, respectively. These results also demonstrated the potential of both MLP and CNN in credit scoring assessment. MLP's strength lies in its capacity to handle non-linear relationships between features, providing a viable alternative to decision tree-based models. On the other hand, CNN excels in capturing spatial patterns and dependencies among features, presenting a distinct advantage in credit score classification. Overall, our study presents a broad-spectrum overview of the analysis, encompassing each model's performance and effectiveness in credit score classification. The findings empower financial institutions to leverage the benefits of DL and ML techniques, optimizing their decision-making processes and enhancing risk management strategies. By selecting the most suitable credit score classification model based on the insights gained from this comparative analysis, institutions can make informed choices and effectively evaluate creditworthiness, leading to improved risk assessment and lending decisions.

Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition

Emotion recognition is a vital part of human functioning. textcolorredIt enables individuals to respond suitably to environmental events and develop self-awareness. The fast-paced developments in brain–computer interfacing (BCI) technology necessitate that intelligent machines of the future be able to digitize and recognize human emotions. To achieve this, both humans and machines have relied on facial expressions, in addition to other visual cues. While facial expressions are effective in recognizing emotions, they can be artificially replicated and require constant monitoring. In recent years, the use of Electroencephalography (EEG) signals has become a popular method for emotion recognition, thanks to advances in deep learning and machine learning techniques. EEG-based systems for recognizing emotions involve measuring electrical activity in the brain of a subject who is exposed to emotional stimuli such as images, sounds, or videos. Machine learning algorithms are then used to extract features from the electrical activity data that correspond to specific emotional states. The quality of the extracted EEG signal is crucial, as it affects the overall complexity of the system and the accuracy of the machine learning algorithm. This article presents an approach to improve the accuracy of EEG-based emotion recognition systems while reducing their complexity. The approach involves optimizing the number of EEG channels, their placement on the human scalp, and the target frequency band of the measured signal to maximize the difference between high and low arousal levels. The optimization method, called the simplicial homology global optimization (SHGO), is used for this purpose. Experimental results demonstrate that a six-electrode configuration optimally placed can achieve a better level of accuracy than a 14-electrode configuration, resulting in an over 60% reduction in complexity in terms of the number of electrodes. This method demonstrates promising results in improving the efficiency and accuracy of EEG-based emotion recognition systems, which could have implications for various fields, including healthcare, psychology, and human–computer interfacing.

One-Class Classification for Radar-Based Human Fall Event Detection

Existing deep learning and machine learning techniques for radar-based fall event detection require a large number of fall activity instances at the training stage to produce reliable results. However, to replicate real-life scenarios, falls should be treated as rare events. This work pioneers the application of one-class classification (OCC) technique for radar-based fall event detection. A comparative analysis of three different types of OCC techniques using abstract features extracted from six different pre-trained models is shown. An improved novel feature fused OCC model is proposed that achieves an area under the curve value of 0.912 for fall event detection. All evaluations are done on a publicly available dataset of human radar signatures.

A Comparative Study for SDN Security Based on Machine Learning

In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN). The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM). The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.

Diabetic Foot Ulcer Identification: A Review

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal.

Using deep machine learning techniques to detect and track athletes in the video data stream

Using deep machine learning techniques to detect and track athletes in the video data stream

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning

Increased screen time may cause significant health impacts, including harmful effects on mental health. Studies on the association between technological obsessions and their influence on health have been conducted using Deep Learning (DL) and Machine Learning (ML) techniques. The deployment of chatbots in different industries has been proven as a game-changer. We study conversational Artificial Intelligence (AI) systems enabling operators to conduct conversations with machines that resemble those with humans. We design and develop two retrieval-based and generative-based chatbots, each with six designs. Among the retrieval-based chatbots, Vanilla Recurrent Neural Network (RNN) has an accuracy of 83.22%, Long Short Term Memory (LSTM) is 89.87% accurate, Bidirectional LSTM (Bi-LSTM) is 91.57% accurate, Gated Recurrent Unit (GRU) is 65.57% accurate, and Convolution Neural Network (CNN) is 82.33% accurate. In comparison, generative-based chatbots have encoder–decoder designs that are 94.45% accurate. The most significant distinction is that while generative-based chatbots can generate new text, retrieval-based chatbots are restricted to responding to inputs that match the best of the outputs they already know.

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment.

Brief Cognizance into Tampered Image Detection

Abstract: This paper is a testament to the research carried out to detect tampered images using deep learning and machine learning techniques, including the implementation of various standard and hybrid convolution neural networks, thus creating a comparative study for the neural networks. Tampered images can be found on various social media networks and sites thus creating a false impression in the minds of people consuming the information from them. Hence, to filter out these fake images the following research work and implementation are carried out.

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Electronic commerce has a strong connection with recommendation processes. There are various forms of recommendations, ranging from virtual assistants to online suggestions made in real time. Different algorithms and technologies are utilized for each form, and the choice of technique is dependent on the task at hand. For instance, artificial intelligence may utilize deep learning or machine learning techniques. The type of data also plays a role in determining the techniques used. Predictive modeling is applied to textual data, while image data requires image processing followed by AI algorithms for prediction. This study aimed to investigate the extent to which artificial intelligence is utilized in recommender systems for electronic commerce, as well as the current and future trends in the field. This was achieved through a systematic literature review of scientific articles from the past decade, using WosViewer for data collection and the Bibliometrix R package for analysis. The findings demonstrate that artificial intelligence works in conjunction with other technologies, such as blockchain, virtual reality, and augmented reality, to enhance the consumer experience throughout the e-commerce process.