Traditional ML Research Articles

Swin-chart: An efficient approach for chart classification

Charts are a visualization tool used in scientific documents to facilitate easy comprehension of complex relationships underlying data and experiments. Researchers use various chart types to convey scientific information, so the problem of data extraction and subsequent chart understanding becomes very challenging. Many studies have been taken up in the literature to address the problem of chart mining, whose motivation is to facilitate the editing of existing charts, carry out extrapolative studies, and provide a deeper understanding of the underlying data. The first step towards chart understanding is chart classification, for which traditional ML and CNN-based deep learning models have been used in the literature. In this paper, we propose Swin-Chart, a Swin transformer-based deep learning approach for chart classification, which generalizes well across multiple datasets with a wide range of chart categories. Swin-Chart comprises a pre-trained Swin Transformer, a finetuning component, and a weight averaging component. The proposed approach is tested on a five-chart image benchmark dataset. We observed that the Swin-Chart model outperformers existing state-of-the-art models on all the datasets. Furthermore, we also provide an ablation study of the Swin-Chart model with all five datasets to understand the importance of various sub-parts such as the back-bone Swin transformer model, the value of several best weights selected for the weight averaging component, and the presence of the weight averaging component itself.The Swin-Chart model also received first position in the chart classification task on the latest dataset in the CHART Infographics competition at ICDAR 2023 - chartinfo.github.io.

A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions of composition and temperature, is essential for understanding alloy properties and screening desirable materials. Traditional methods like CALPHAD are computationally intensive for exploring high-dimensional compositional spaces. To address such a challenge, this study explored and compared the effectiveness of random forests (RF) and deep neural networks (DNN) for accelerating materials discovery by building surrogate models of phase stability prediction. For interpolation scenarios (testing on the same order of system as trained), RF models generally produce smaller errors than DNN models. However, for extrapolation scenarios (training on lower-order systems and testing on higher order systems), DNNs generalize more effectively than traditional ML models. DNN demonstrate the potential to predict topologically relevant phase composition when data were missing, making it a powerful predictive tool in materials discovery frameworks. The study uses a CALPHAD dataset of 480 million data points generated from a custom database, available for further model development and benchmarking. Experiments show that DNN models are data-efficient, achieving similar performance with a fraction of the dataset. This work highlights the potential of DNNs in materials discovery, providing a powerful tool for predicting phase stability in HEAs, particularly within the Cr-Hf-Mo-Nb-Ta-Ti-V-W-Zr composition space.

Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.

LLM for Data Management

Machine learning techniques have been verified to be effective in optimizing data management systems and are widely researched in recent years. However, traditional small-sized ML models often struggle to generalize to new scenarios, and have limited context understanding ability (e.g., inputting discrete features only). The emergence of LLMs offers a promising solution to these challenges. LLMs have been trained over a vast number of scenarios and tasks and acquire human-competitive capabilities like context understanding and summarization, which can be highly beneficial for data management tasks (e.g., natural language based data analytics). In this tutorial, we present how to utilize LLMs to optimize data management systems and review new techniques for addressing these technical challenges, including hallucination of LLMs, high cost of interacting with LLMs, and low accuracy for processing complicated tasks. First, we discuss retrieval augmented generation (RAG) techniques to address the hallucination problem. Second, we present vector database techniques to improve the latency. Third, we present LLM agent techniques for processing complicated tasks by generating multi-round pipelines. We also showcase some real-world data management scenarios that can be well optimized by LLMs, including query rewrite, database diagnosis and data analytics. Finally, we summarize some open research challenges.

Modeling river flow for flood forecasting: A case study on the Ter river

Floods affect chronically many communities around the world. Their socioeconomic impact increases year-by-year, boosted by global warming and climate change. Combined with long-term preemptive measures, preparatory actions are crucial when floods are imminent. In the last decade, machine learning models have been used to anticipate these hazards. In this work, we model the Ter river (NE Spain), which has historically suffered from floods, using traditional ML models such as K-nearest neighbors, Random forests, XGBoost and Linear regressors. Publicly available river flow and precipitation data was collected from year 2009 to 2021. Our analysis measures the time elapsed between observing a flow rise event at different stations (or heavy rain, for rainfall stations), and use the measured time lags to align the data from the different stations. This information provides increased interpretability to our river flow models and flood forecasters. A thorough evaluation reveals that ML techniques can be used to make short-term predictions of the river flow, even during heavy rain and large flow rise events. Moreover, our flood forecasting system provides valuable interpretable information for setting up timely preparatory actions.

LLM is All You Need : How Do LLMs Perform On Prediction and Classification Using Historical Data

This study investigates the utility of large language models (LLMs) in performing traditional machine learning tasks such as prediction, and explores the potential of refinement architectures to enhance their effectiveness in these roles. Utilizing the Titanic survival dataset, we conducted a com- parative analysis using both conventional machine learning tools and LLM-based approaches. Our findings indicate that while LLMs differ fundamentally from traditional ML models in prediction tasks, there exist specific architectural modifications, termed Thought Refinement Architectures, which can significantly improve their performance. These results highlight the potential for inte- grating LLMs into traditional ML workflows, thereby expanding their applicability and enhancing predictive accuracy.

Artificial intelligence (AI) –powered H&E whole-slide image (WSI) analysis to predict recurrence in hormone receptor positive (HR+) early breast cancer (EBC).

571 Background: The recurrence risk (RS) based on transcriptomic profiles or Ki-67 level of HR+ EBC has been used for adjuvant treatment decisions and may also aid selecting patients who could benefit from novel treatments including CDK4/6 inhibitor or oral selective estrogen receptor degraders (SERDs). Artificial intelligence-powered H&E whole slide image (WSI) models can be a practical approach for this biomarker without the need of additional tissue samples. We developed ML-based models of histopathologic features from an H&E WSI to approximate RS of 21-gene assay (OncotypeDx) using various algorithms of different complexities. Methods: The development dataset was composed of 1,009 cases from TCGA-BRCA with gene expression profiles and 483 EBC cases from Samsung Medical Center with RS results. The prediction model was developed in two-stages. First, Lunit SCOPE, an AI-powered H&E WSI analyzer, detects various cell types and segments tissue areas resulting in various cell densities in tissue regions of interest. Next, four traditional ML models were applied on top of the first stage results. In addition, a deep learning model based on multiple instance learning (DL-MIL) was developed using three features: a supervised convolutional neural network, a supervised cell detection model, and an AI-based pathology profiling analyzer, for extracting semantic contents. The model performance was validated using two independent EBC test sets: 248 cases for comparison with the RS results (set 1), and 708 cases with DFS (set 2). Results: The cross-validation performance of the four traditional ML algorithms had area under the curve of receiver operating characteristics curve (AUROC) ranging from 0.728 to 0.779, where mitotic cell density, tumor cell density, and fibroblast density in cancer area were the variables with the highest importance for all four models. The DL-MIL model had cross validation performance of AUROC 0.831. In test set 1, DL-MIL model showed the highest discrimination with AUROC of 0.828 compared to traditional ML models where logistic regression showed the highest discrimination with AUROC of 0.786 (Table). The DL-MIL model showed the highest performance among the models to predict DFS with hazard ratio (HR) of 2.48 (1.47-4.18). Conclusions: Histopathomic models can accurately predict the RS of high risk as well as poor DFS from only H&E WSIs. These AI models can be a practical tool for treatment selection including emerging drugs such as SERDs. [Table: see text]

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks

Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs’ powerful feature extraction capabilities. The study employs the widely used benchmark mutagenicity dataset from Hansen et al. for model development and evaluation. Comparative analyses with traditional ML models on different molecular features reveal substantial performance enhancements. AMPred-CNN outshines these models, demonstrating superior accuracy, AUC, F1 score, MCC, sensitivity, and specificity on the test set. Notably, AMPred-CNN is further benchmarked against seven recent ML and DL models, consistently showcasing superior performance with an impressive AUC of 0.954. Our study highlights the effectiveness of CNNs in advancing mutagenicity prediction, paving the way for broader applications in toxicology and drug development.

Повышение эффективности методов подбора персонала на основе глубоких нейронных сетей

The recruitment industry is at an inflection point: the integration of artificial intelligence has already made its impact on traditional recruitment processes and has the potential to revolutionize it. This article presents an approach to classify resumes into job categories, using semantic similarity search to improve the candidate selection mechanism in recruiting. Our method differs from traditional keyword-based systems and is a deep learning framework that understands and processes the complex semantics of work-related documents. The purpose of the study is to develop a method for classifying resume texts with a complex organizational structure. This study solves several problems at once: increasing the accuracy of resume classification and finding the most stable model for solving the problem of resume classification. We compared standard machine learning methods with neural network ones and showed the effectiveness of the latter. The results indicate an improvement over traditional ML models, suggesting an approach that can be used for pre-screening artificial intelligence recruiting that selects suitable candidates from other applicants. Further, we discovered problems with instability of results when retraining large language models, when the model, even with the same values of the hyperparameters, gives different results. To better understand this phenomenon, we conducted a series of experiments with the main BERT models, varying two parameters – learning rate and seed. As a result, we find a significant increase in performance at a certain threshold parameter, and we quantify which of the found models perform better.

Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts

Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian languages, which are both low-resource languages, with the latter being an extremely low-resource language. In the study, we used both traditional baseline machine learning methods, as well as a wide suite of deep learning methods especially focusing on hybrid networks and transformer-based multilingual models. For the data, we collected over 5000 both Bangla and Chittagonian text samples from social media. Krippendorff’s alpha and Cohen’s kappa were used to measure the reliability of the dataset annotations. Traditional machine learning methods used in this research achieved accuracies ranging from 0.63 to 0.711, with SVM emerging as the top performer. Furthermore, employing ensemble models such as Bagging with 0.70 accuracy, Boosting with 0.69 accuracy, and Voting with 0.72 accuracy yielded promising results. In contrast, deep learning models, notably CNN, achieved accuracies ranging from 0.69 to 0.811, thus outperforming traditional ML approaches, with CNN exhibiting the highest accuracy. We also proposed a series of hybrid network-based models, including BiLSTM+GRU with an accuracy of 0.799, CNN+LSTM with 0.801 accuracy, CNN+BiLSTM with 0.78 accuracy, and CNN+GRU with 0.804 accuracy. Notably, the most complex model, (CNN+LSTM)+BiLSTM, attained an accuracy of 0.82, thus showcasing the efficacy of hybrid architectures. Furthermore, we explored transformer-based models, such as XLM-Roberta with 0.841 accuracy, Bangla BERT with 0.822 accuracy, Multilingual BERT with 0.821 accuracy, BERT with 0.82 accuracy, and Bangla ELECTRA with 0.785 accuracy, which showed significantly enhanced accuracy levels. Our analysis demonstrates that deep learning methods can be highly effective in addressing the pervasive issue of cyberbullying in several different linguistic contexts. We show that transformer models can efficiently circumvent the language dependence problem that plagues conventional transfer learning methods. Our findings suggest that hybrid approaches and transformer-based embeddings can effectively tackle the problem of cyberbullying across online platforms.

HyperFast: Instant Classification for Tabular Data

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to an unseen dataset that can be directly used for classification inference, removing the need for training a model. We report extensive experiments with OpenML and genomic data, comparing HyperFast to competing tabular data neural networks, traditional ML methods, AutoML systems, and boosting machines. HyperFast shows highly competitive results, while being significantly faster. Additionally, our approach demonstrates robust adaptability across a variety of classification tasks with little to no fine-tuning, positioning HyperFast as a strong solution for numerous applications and rapid model deployment. HyperFast introduces a promising paradigm for fast classification, with the potential to substantially decrease the computational burden of deep learning. Our code, which offers a scikit-learn-like interface, along with the trained HyperFast model, can be found at https://github.com/AI-sandbox/HyperFast.

Deep learning enables early stage prediction of preterm birth using vaginal microbiota

Graphical abstract Abstract Objective Preterm birth (PTB) is one of the leading issues concerning infant health and is a problem that plagues all parts of the world. Vaginal microbial communities have recently garnered attention in the context of PTB; however, the vaginal microbiome varies greatly from individual to individual, and this variation is more pronounced in racially, ethnically, and geographically diverse populations. Additionally, microbial communities have been reported to evolve during the duration of the pregnancy, and capturing such a signature may require higher, more complex modeling paradigms. In this study, we develop a neural controlled differential equation (CDE)-based framework for identifying early PTBs in racially diverse cohorts from irregularly sampled vaginal microbial abundance data. Methods We obtained relative abundances of microbial species within vaginal microbiota using 16S rRNA sequences obtained from vaginal swabs at various stages of pregnancy. We employed a recently introduced deep learning paradigm known as ‘neural CDEs’ to predict PTBs. This method, previously unexplored, analyzes irregularly sampled microbial abundance profiles in a time-series format. Results Our framework is able to identify signatures in the temporally evolving vaginal microbiome during trimester 2 and can predict incidences of PTB (mean test set ROC–AUC = 0.81, accuracy = 0.75, F1 score = 0.71) significantly better than traditional ML classifiers, thus enabling effective early-stage PTB risk assessment. Conclusion and significance Our method is able to differentiate between term and preterm outcomes with a substantial accuracy, despite being trained using irregularly sampled microbial abundance profiles, thus overcoming the limitations of traditional time-series modeling methods.

Exploring the Effectiveness of Machine and Deep Learning Techniques for Android Malware Detection

The increasing occurrence of Android devices, coupled with their get entry to to touchy and personal information, has made them a high goal for malware developers. The open-supply nature of the Android platform has contributed to the developing vulnerability of malware assaults. presently, Android malware (AM) analysis strategies may be labeled into foremost categories: static evaluation and dynamic evaluation. These techniques are employed to analyze and understand the behavior of AM to mitigate its impact. This research explores the performance of DL model architectures, such as CNN-GRU, as well as traditional ML algorithms including SVM, Random Forest (RF), and decision tree (DT). The DT model achieves the highest accuracy (ACC) of 0.93, followed by RF (0.89), CNN-GRU (0.91), and SVM (0.90). These findings contribute valuable insights for the development of effective malware detection systems, emphasizing the suitability and effectiveness of the examined models in identifying AM.

The five-factor model analysed by machine learning classification techniques

ABSTRACT This study compares the classification performance of traditional factor models and ML to explore the assumption of linearity in risk premiums. We apply ML techniques to the variables used in the Fama – French five-factor (FF5) model to classify the top 30% and bottom 30% of stocks based on whether their prices are expected to increase (winners) or decrease (losers), respectively, over a specified time period. The analysis showed that the ML model’s classification Accuracy outperforms the FF5 classification, which suggests that the higher returns obtained using the FF5 model can be attributed to its effectiveness in classifying stocks with exceptionally high or low returns, rather than the comprehensive use of its variables for overall classification purposes.

Cognitive and clinical predictors of a long-term course in obsessive compulsive disorder: A machine learning approach in a prospective cohort study

BackgroundObsessive compulsive disorder (OCD) is a disabling illness with a chronic course, yet data on long-term outcomes are scarce. This study aimed to examine the long-term course of OCD in patients treated with different approaches (drugs, psychotherapy, and psychosurgery) and to identify predictors of clinical outcome by machine learning. MethodWe included outpatients with OCD treated at our referral unit. Demographic and neuropsychological data were collected at baseline using standardized instruments. Clinical data were collected at baseline, 12 weeks after starting pharmacological treatment prescribed at study inclusion, and after follow-up. ResultsOf the 60 outpatients included, with follow-up data available for 5–17 years (mean = 10.6 years), 40 (67.7 %) were considered non-responders to adequate treatment at the end of the study. The best machine learning model achieved a correlation of 0.63 for predicting the long-term Yale-Brown Obsessive Compulsive Scale (Y-BOCS) score by adding clinical response (to the first pharmacological treatment) to the baseline clinical and neuropsychological characteristics.Limitations.Our main limitations were the sample size, modest in the context of traditional ML studies, and the sample composition, more representative of rather severe OCD cases than of patients from the general community. ConclusionsMany patients with OCD showed persistent and disabling symptoms at the end of follow-up despite comprehensive treatment that could include medication, psychotherapy, and psychosurgery. Machine learning algorithms can predict the long-term course of OCD using clinical and cognitive information to optimize treatment options.

Subsampled Randomized Hadamard Transformation-based Ensemble Extreme Learning Machine for Human Activity Recognition

Extreme Learning Machine (ELM) is becoming a popular learning algorithm due to its diverse applications, including Human Activity Recognition (HAR). In ELM, the hidden node parameters are generated at random, and the output weights are computed analytically. However, even with a large number of hidden nodes, feature learning using ELM may not be efficient for natural signals due to its shallow architecture. Due to noisy signals of the smartphone sensors and high dimensional data, substantial feature engineering is required to obtain discriminant features and address the “curse-of-dimensionality”. In traditional ML approaches, dimensionality reduction and classification are two separate and independent tasks, increasing the system’s computational complexity. This research proposes a new ELM-based ensemble learning framework for human activity recognition to overcome this problem. The proposed architecture consists of two key parts: (1) Self-taught dimensionality reduction followed by classification. (2) they are bridged by “Subsampled Randomized Hadamard Transformation” (SRHT). Two different HAR datasets are used to establish the feasibility of the proposed framework. The experimental results clearly demonstrate the superiority of our method over the current state-of-the-art methods.

Sentiment Analysis Using Deep Learning Approaches on Multi-Domain Dataset in Telugu Language

Recent advancements in Natural Language Processing (NLP) have made sentiment analysis an essential component of a variety of NLP jobs, including recommendation systems, question answering, and business intelligence products. While sentiment analysis research has, to put it mildly, been widely pursued in English, Telugu has barely ever attempted the task. The majority of research works concentrate on analysing the sentiments of Tweets, news, or reviews containing Hindi and English words. There is a growing interest among academics in studying how people express their thoughts and views in Indian languages like Bengali, Telugu, Malayalam, Tamil and so on. Due to a paucity of labelled datasets, microscopic investigation on Indian languages has been published to our knowledge. This work suggested a sentence-level sentiment analysis on multi-domain datasets that has been collected in Telugu. Deep learning models have been used in this work because it demonstrates the significant expertise in sentiment analysis and is widely regarded as the cutting-edge model in Telugu Sentiment Analysis. Our proposed work investigates a productive Bidirectional Long Short-Term Memory (BiLSTM) Network and Bidirectional GRU Network (BiGRU) for improving Telugu Sentiment Analysis by encapsulating contextual information from Telugu feature sequences using Forward-Backward encapsulation. Further, the model has been deployed by merging the domains so as to predict the accuracy and other performance metrics. The experimental test findings show that the deep learning models outperform when compared with the baseline traditional ML methods in four benchmark sentiment analysis datasets. There is evidence that the proposed sentiment analysis method has improved precision, recall, F1-score and accuracy in certain cases. The proposed model has achieved the F1-score of 86% for song datasets when compared with the other existing models.

Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.

Graph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The basis of our exploration involves the application of graph neural network architectures and machine learning to resting-state functional Magnetic Resonance Imaging (rs-fMRI) data for the purpose of detecting schizophrenia. Our study uses single-site data to avoid the shortcomings in generalizability of neuroimaging data obtained from multiple sites. The performance of our graph neural network models is on par with that of our machine learning models, each of which is trained using 69 graph-theoretical measures computed from functional correlations between various regions of interest (ROI) in a brain graph. Our deep graph convolutional neural network (DGCNN) demonstrates a promising average accuracy score of 0.82 and a sensitivity score of 0.84. This study provides insights into the role of advanced graph theoretical methods and machine learning on fMRI data to detect schizophrenia by harnessing changes in brain functional connectivity. The results of this study demonstrate the capabilities of using both traditional ML techniques as well as graph neural network-based methods to detect schizophrenia using features extracted from fMRI data. The study also proposes two methods to obtain potential biomarkers for the disease, many of which are corroborated by research in this area and can further help in the understanding of schizophrenia as a mental disorder.

Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors

ML-based Phishing URL (MLPU) detectors serve as the first level of defence to protect users and organisations from being victims of phishing attacks. Lately, few studies have launched successful adversarial attacks against specific MLPU detectors raising questions on their practical reliability and usage. Nevertheless, the robustness of these systems has not been extensively investigated. Therefore, the security vulnerabilities of these systems, in general, remain primarily unknown that calls for testing the robustness of these systems. In this article, we have proposed a methodology to investigate the reliability and robustness of 50 representative state-of-the-art MLPU models. First, we have proposed a cost-effective Adversarial URL generator URLBUG that created an Adversarial URL dataset ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Adv_\text{data}$</tex-math></inline-formula> ) . Subsequently, we reproduced 50 MLPU (traditional ML and Deep learning) systems and recorded their baseline performance. Lastly, we tested the considered MLPU systems on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Adv_\text{data}$</tex-math></inline-formula> and analyzed their robustness and reliability using box plots and heat maps. Our results showed that the generated adversarial URLs have valid syntax and can be registered at a median annual price of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula> 11.99, and out of 13% of the already registered adversarial URLs, 63.94% were used for malicious purposes. Moreover, the considered MLPU models Matthew Correlation Coefficient (MCC) dropped from median 0.92 to 0.02 when tested against <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Adv_\text{data}$</tex-math></inline-formula> , indicating that the baseline MLPU models are unreliable in their current form. Further, our findings identified several security vulnerabilities of these systems and provided future directions for researchers to design dependable and secure MLPU systems.

Analysis of Colorectal and Gastric Cancer Classification: A Mathematical Insight Utilizing Traditional Machine Learning Classifiers

Cancer remains a formidable global health challenge, claiming millions of lives annually. Timely and accurate cancer diagnosis is imperative. While numerous reviews have explored cancer classification using machine learning and deep learning techniques, scant literature focuses on traditional ML methods. In this manuscript, we undertake a comprehensive review of colorectal and gastric cancer detection specifically employing traditional ML classifiers. This review emphasizes the mathematical underpinnings of cancer detection, encompassing preprocessing techniques, feature extraction, machine learning classifiers, and performance assessment metrics. We provide mathematical formulations for these key components. Our analysis is limited to peer-reviewed articles published between 2017 and 2023, exclusively considering medical imaging datasets. Benchmark and publicly available imaging datasets for colorectal and gastric cancers are presented. This review synthesizes findings from 20 articles on colorectal cancer and 16 on gastric cancer, culminating in a total of 36 research articles. A significant focus is placed on mathematical formulations for commonly used preprocessing techniques, features, ML classifiers, and assessment metrics. Crucially, we introduce our optimized methodology for the detection of both colorectal and gastric cancers. Our performance metrics analysis reveals remarkable results: 100% accuracy in both cancer types, but with the lowest sensitivity recorded at 43.1% for gastric cancer.