Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization

Detection of breast cancer remains an important global health issue, particularly in patients with dense breast tissue. Despite wide use of machine learning for breast cancer diagnosis, many existing studies rely on single-model evaluations and lack rigorous statistical validation, while transformer-based models remain insufficiently explored for structured tabular biomedical data. Using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, this study presents a comparative analysis of machine learning, deep learning, and transformer-based models for binary classification of breast masses as benign or malignant. A robust preprocessing and feature engineering pipeline was applied, and Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and BERT-based models were evaluated using a stratified 66–34% train–test split and stratified 5-fold cross-validation. Performance was assessed using standard classification metrics, Jaccard Index, AUC-ROC, false negative rate, and statistical significance testing (paired t-tests and Bayesian analysis). Three-layer RNN and LSTM models achieved the highest accuracy of 98.25%, showing statistically significant improvement over SVM (p = 0.042), while the optimized SVM demonstrated strong performance with superior computational efficiency. BERT-based models yielded lower accuracy (92.98%), reflecting domain mismatch between language-pretrained transformers and numerical tabular data. These findings indicate that deeper recurrent architectures provide superior diagnostic performance, while SVM remains a practical and efficient alternative for clinical deployment.

Area under the curve may hide poor generalisation to external datasets

This research work focuses on detecting cyberthreats, including Malware, Man-in-the-Middle, and Distributed Denial-of-Service. Given the escalating complexity and frequency of cyber threats, traditional detection approaches struggle to provide adequate protection, underscoring the need for deep learning and machine learning strategies. This study evaluates and compares the performance of six prominent models: XGBoost, Random Forest, Support Vector Machine, Dense Neural Network, Convolutional Neural Network, and Long Short-Term Memory using the NF-UQ-NIDS-v2 Network Intrusion Detection Dataset. The dataset was meticulously preprocessed using the scaling technique and feature selection with the Chi-square method (selecting 20 features out of 43), as well as Random Oversampling to address class imbalance. Experimental evaluations utilize a group of classification metrics to evaluate the effectiveness of models. The received scores demonstrated that ensemble models, specifically Random Forest and XGBoost, significantly outperformed other classifiers, achieving nearly perfect metrics of 0.999, which reflects their exceptional capability to generalize across diverse attack types. In contrast, the Support Vector Machine demonstrated limited effectiveness, with an accuracy as low as 0.794, indicating its relative inefficiency in this scenario. Deep learning models have consistently shown strong performance with metrics ranging from 0.924 to 0.949. This study highlights the effectiveness of ensemble techniques in cybersecurity threat classification, offering valuable insights for enhancing proactive defense mechanisms in network security systems

This research article proposes a deep learning framework that addresses two major hurdles in adopting deep learning techniques for solving physics-based problems. One is the requirement of a large data set for training the deep learning (DL) model and another is the consistency of a DL model with the physics of a phenomenon. The framework is generic that can be applied to model a phenomenon in physics if its behavior is known. A semi-supervised physics guided neural network (SPGNN) has been developed based on our framework to demonstrate the concept. SPGNN models the I–V characteristics of gallium nitride based high electron mobility transistors (GaN HEMTs). A two-stage method has been proposed to train a DL model. In the first stage, the DL model is trained via an unsupervised learning method using the analytical physics-based model of a field-effect transistor (FET) as a loss function of the DL model that incorporates the physics of the FET in the DL model. Later, the DL model is fine-tuned with a small set of experimental data in the second stage. Performance of SPGNN has been assessed on various sizes of the data set with 100, 500, 1000, 1500, and 2000 samples. SPGNN significantly reduces the training data requirement by more than 80% and provides better performance than a traditionally trained neural network (TTNN), even for the unseen test data set. SPGNN predicts 32.4% of the unseen test data with less than 1% of error and only 0.4% of the unseen test data with more than 10% of error.

Statistical analysis of high throughput genomic data, such as gene expressions, copy number alterations (CNAs) and single nucleotide polymorphisms (SNPs), has become very popular in cancer studies in recent decades because such analysis can be very helpful to predict whether a patient has a certain cancer or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small numbers of samples, traditional machine learning approaches, such as Support Vector Machines (SVMs) and Random Forests (RFs), cannot analyze these data efficiently. To overcome this issue, we propose a deep neural network model to predict molecular subtypes of breast cancer using somatic CNAs. Experiments show that our deep model works much better than traditional SVM and RF.

Accurate rainfall forecasting is crucial in activities such as agricultural planning, water resource management, and flood preparedness, so failure to accurately forecast rainfall might undermine these activities. This study aimed to improve the accuracy of daily rainfall prediction in Hai district, Kilimanjaro region, Tanzania, a region characterised by a bimodal rainfall pattern, by comparing rainfall prediction accuracies of Traditional Machine Learning (TML) and Deep Learning (DL) models. The 25-year daily rainfall dataset for Hai district was first pre-processed by cleaning, normalisation, and splitting into training (first 60%), validation (next 20%), and test (last 20%) sets. TML models (K-Nearest Neighbours (KNN) through its variant KNN Regressor and Support Vector Machine (SVM) through its variant Support Vector Regressor (SVR)) were developed, trained, and their performances compared with developed and trained DL models (Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)). Each model’s test performance was evaluated using the Root Mean Squared Error (RMSE) metric. The findings revealed that the DL models, LSTM and GRU, achieved test RMSE scores of 6.35 mm and 6.49 mm, respectively, while the TML models, KNN and SVM, achieved test RMSE scores of 6.61 mm and 10.42 mm, respectively. The study demonstrated that DL models are more effective for accurate rainfall forecasting than TML models in regions with complex rainfall patterns. The insights gained will guide the development of more reliable, location-specific weather prediction systems, ultimately supporting informed decision-making in agriculture, environmental management, and flood risk reduction in Tanzania and other regions facing similar climatic challenges

Evaluation of multispectral imaging for freeze damage assessment in strawberries using AI-based computer vision technology

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.

Automated Systems.

In the field of machine learning, support vector machine is a supervised learning model which can analyze the data and identify patterns, and this theory is used for classification and regression analysis which is related to the learning algorithm.Support vector machine is a new machine learning method based on statistical learning theory, and has become a hot research topic in the machine learning field because of its excellent learning performance.Support vector machine is also a learning machine based on kernel function, and its generalization ability depends on the chosen kernel function to a great extent.Nevertheless, traditional support vector machine cannot achieve the desired results when training large-scale data set.Therefore, in order to improve training efficiency and generalization performance of support vector machine, we have to improve this algorithm.Although support vector machine has great advantages in theory, the research on its application is comparatively delayed.In this paper, we focus on novel support vector machine generated from multi-subjects amalgamation, such as fuzzy support vector machine, granular support vector machine, twin support vector machine.We analyze a large number of literature review about development trend of support vector machine and its applications in computer field, laying foundation for the systematic research on the correlation algorithm based on support vector machine.

Though large amounts of work with artificial intelligence are used in typhoon rainfall forecasting, the predictive skills of existing models are unsatisfactory. To address this problem, this study aims to propose an improved cluster‐wise typhoon rainfall forecasting model that integrates the grid‐search cross‐validation method with machine learning and deep learning (DL) models including support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), convolutional neural network (CNN), and long short‐term memory (LSTM). Grid‐search cross‐validation is a modified parameterization technique that helps to find the best parameters for machine/DL models. In the first stage, a second‐order polynomial regression model was used to cluster the typhoon track; and in the second stage, cluster‐wise typhoon rainfall was recognized within a 500 km radius from each typhoon center. After that, a modified cluster‐wise typhoon rainfall forecasting model was proposed using cluster‐wise antecedent hourly typhoon rainfall within this distance for 1–6 hr lead time. Results show that the proposed model based on the SVM, RF, AdaBoost, CNN, and LSTM is capable of providing more accurate forecasts (the efficiency of the forecast is increased by 45%–90%) than the existing typhoon rainfall forecasting models that are based on SVM with a genetic algorithm, RF, artificial neural network, multilayer perceptron network, and deep neural network. Therefore, the current study recommends using cluster‐wise typhoon rainfall forecasting model with a grid‐search cross‐validation method for disaster prevention and mitigation.

Chapter 5 - Drug Targets, Target identification, Validation and Screening

Introduction: The condition of prostate cancer continues to represent a substantial medical issue because it necessitates precise predictive models that support healthcare decisions. A research study investigates how Extreme Gradient Boosting (XGBoost) performs when identifying significant biomarkers to anticipate prostate cancer risks. The research compares XGBoost to Sequential Minimal Optimization (SMO) in Support Vector Machines (SVM) to evaluate its better performance in classification. Prostate-Specific Antigen (PSA) levels together with Gleason scores and molecular markers form the clinical parameters within the dataset. Process data correctly by cleansing it and normalizing it while extracting important features to achieve the optimal model performance. Objectives: This research investigates how well Extreme Gradient Boosting (XGBoost) predicts prostate cancer through an examination of its function to discover complex non-linear data patterns in clinical datasets. The research compares XGBoost to SMO algorithm of Support Vector Machines through a performance assessment of classification metrics including accuracy, precision, recall and F1-score. The research explores two important biomarkers Prostate-Specific Antigen (PSA) levels along with Gleason scores which affect how prostate cancer is expected to progress. The research applies machine learning advanced methods to develop improved early identification systems that lead to better clinical decisions which subsequently enhance patient health results. Methods: The research method relies on a structured machine learning process where multiple steps start with detailed data preprocessing for inconsistent data elimination and variable normalization. Feature extraction technologies increase model interpretability by being applied to the analysis. The available dataset contains two well-separated sections for training and testing purposes which aims to establish reliable validation procedures. Both XGBoost and SVM with SMO operate for classification tasks while their achievement levels are assessed through standard measurement criteria which include accuracy, precision, recall and F1-score. A logistic regression analysis validates whether PSA levels and Gleason scores are significant factors in predictive modeling. Results: XGBoost achieves superior results over SVM with SMO according to every mitigation evaluation indicator. The classification metric for XGBoost displays 0.95 accuracy whereas SVM with SMO achieves 0.87 accuracy. The evaluation of XGBoost produced precision measurements of 0.92 while recall reached 0.93 and F1-score achieved 0.92. These evaluation scores surpassed SVM with SMO which generated precision of 0.85, recall of 0.80 and F1-score of 0.82. XGBoost manages to lower the chances of overfitting while improving biomarker interpretation so it shows clear potential for clinical prostate cancer prediction applications. Conclusions: The conducted research demonstrates XGBoost's effectiveness for prostate cancer classification through better prediction accuracy along with stronger resistance than SVM with SMO. The research demonstrates AI-powered models should become an integral part of clinical practice to improve the detection and treatment preparation for patients.

In order to prevent people from troubling by the sensitive information in the Internet, it is urgently compulsory to fortify the supervision upon Internet culture. Our works mainly focus on the online-live and internet short videos realtime invigilation. In this paper we leverage a novel deep learning (DL) model which is constructed by using Late Fusion method, also combining frame sampling to detect the sensitive information in stream mode. Plus, we use computational complexity theory to optimize the classification accuracy of specific samples. Our experiment shows that it is feasible to apply the theory into the DL model. After the optimization, the result has a certain improvement in classification accuracy compared with the naïve form.

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.