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A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques

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INTERNATIONAL JOURNAL OF LEGAL MEDICINE 2023年第1期137卷 237-249页

作者： Lu, Xiao-jun Li, Jian Wei, Xue Li, Na Dang, Li-hong An, Guo-shuai Du, Qiu-xiang Jin, Qian-qian Cao, Jie Wang, Ying-yuan Sun, Jun-hong Shanxi Med Univ Sch Forens Med 98 Univ St Jinzhong 030604 Shanxi Peoples R China Baotou Publ Secur Bur Criminal Invest Detachment 191 Jianshe Rd Baotou City 014030 Inner Mongolia Peoples R China

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

关键词： Forensic pathology Metabolomics machine learning algorithms Postmortem interval Stacking algorithm Classification

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A review on machine learning based routing protocols for delay tolerant networks

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2023年第2期17卷 287-299页

作者： Bhavani, Ashapu Ramana, Venkata Chakravarthy, A. S. N. JNTU Kakinada Dept CSE Kakinada India GMR Inst Technol Dept CSE Rajam India

Nowadays, Delay Tolerant Network plays an important role in improving the communication between the network nodes. Applications of Delay Tolerant Network are disaster recovery, vehicular communication, sensor networks, interplanetary networks, and communication in remote and rural areas. Routing is one of the important tasks for enhancing the energy effectiveness of data transmission among the mobile nodes under network congestion and dynamic topology. machine learning-based routing algorithms are used for improving network communication in Delay Tolerant Networks. Its objective is to reduce the delay, minimize the overhead, reduce energy consumption, improve throughput, minimize packet loss, and efficient data transmission. This paper presents a comprehensive review of routing algorithms using machine learning for Delay Tolerant Networks.

关键词： Delay tolerant network routing protocols machine learning algorithms performance analysis

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Authentic learning of machine learning in Cybersecurity with Portable Hands-on Labware: Neural Network algorithms for Network Denial of Service (DOS) Detection

Authentic Learning of Machine Learning in Cybersecurity with...

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IEEE International Conference on Big Data

作者： Md Jobair Hossain Faruk Hossain Shahriar Kai Qian Dan Lo Michael Whitman Alfredo Cuzzocrea Fan Wu Victor Clincy Department of Software Engineering and Game Development Kennesaw State University USA Department of Information Technology Kennesaw State University USA Institute for Cyber Workforce Development Kennesaw State University USA Department Computer Science Kennesaw State University USA iDEA Lab University of Calabria Rende Italy LORIA Nancy France Department of Computer Science Tuskegee University USA

ISBN: (纸本)9781665480468

The primary goal of the authentic learning approach is to engage and motivate students in a learning environment that encourages all students in learning. This approach provides students with hands-on experiences in solving real-world security problems. We designed and developed ten learning modules based on 10 cybersecurity cases with different ML solutions. Each learning module consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. All portable labs are made available on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will engage students in learning concepts and getting more experience for hands-on problem-solving skills. In this paper, we adopt Neural Network algorithms for Network Denial of Service (DOS) Detection where we apply the KDDCup 1999 datasets contain a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. Our primary goal of this lab is to show whether a link is a malicious or safe connection. Our demonstration shows an achieved accuracy of 99.89%.

关键词： Military standards machine learning algorithms Neural networks machine learning Big Data Data models Internet

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Analysis and Synthesis of Adaptive Gradient algorithms in machine learning: The Case of AdaBound and MAdamSSM

Analysis and Synthesis of Adaptive Gradient Algorithms in Ma...

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IEEE Conference on Decision and Control

作者： Kushal Chakrabarti Nikhil Chopra University of Maryland Maryland U.S.A

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Adaptive gradient algorithms have become the prevalent tool in training complex neural networks; recent examples include AdaBound and MAdam. For a better understanding of such existing optimization algorithms and design ideas for new algorithms, well-known tools from classical control appear to be promising. This area of research is built upon modeling optimization algorithms as closed-loop dynamical systems. Consequently, this paper exploits a control-theoretic methodology in analyzing AdaBound, a recent adaptive gradient algorithm, and proposing a novel optimizer for machine learning. Specifically, inspired by the recently developed state-space perspective in the G-AdaGrad and the Adam algorithm, we present a simple convergence analysis of the AdaBound algorithm for non-convex optimization problems. Next, we propose a new variant of the MAdam algorithm upon applying the concept of transfer functions. Our experimental results demonstrate the efficiency of the proposed algorithm in training CNN models for image classification problems. The findings in this paper suggest further exploration of the existing tools from control theory in complex machine learning problems.

关键词： Training Adaptation models machine learning algorithms Heuristic algorithms Neural networks Transfer functions Classification algorithms

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Classification of Fruit Plants Leaf and Comparative Analysis of machine learning and Deep learning algorithms

Classification of Fruit Plants Leaf and Comparative Analysis...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Aaryan Kumar Hrithik Vipin Kumar Department of Computer Science and Information Technology Mahatma Gandhi Central University Motihari Bihar India

ISBN: (纸本)9781665462013

Leaf is the essential part of plant which have significant feature for the identification of Plant. But without an expert, it is an exceedingly difficult task to find the type of plant. Fruits Plant have significant role in the fulfillment of proper nutrition. Therefore, automatic fruit plant detection is highly required, and it can be accomplished using image processing algorithms. In this paper, the authors themselves have collected 30 categories of fruit plant leaf(i. e., 11321 RGB images) leaves using a smartphone device and comparative studies have been performed for the classification of the leaf of the fruit plant using multiple machine learning (ML) algorithms i.e., Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector machine (SVM), Decision tree (DT), Multi-layer Perceptron (MLP), and Naive Bayes (NB). According to the performance analysis, MLP performed better than other classifiers including LR, KNN, SVM, DT, and NB, with an accuracy rate of 93.18%. In this paper, used DL models are ResNet, AlexNet, VggNet, SqueezeNet, GoogLeNet, ShuffleNet, Resnext50, and DenseNet. Based on the analysis of DL models, we can conclude that ShuffleNet has performed better than the others i.e., 99.96% accuracy.

关键词： Support vector machines Performance evaluation machine learning algorithms Image processing Classification algorithms Performance analysis Task analysis

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Enhanced Transfer learning Strategies for Effective Kidney Tumor Classification with CT Imaging

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第8期14卷 421-432页

作者： Majid, Muneer Gulzar, Yonis Ayoub, Shahnawaz Khan, Farhana Reegu, Faheem Ahmad Mir, Mohammad Shuaib Jaziri, Wassim Soomro, Arjumand Bano Glocal Univ Glocal Sch Sci & Technol Delhi Yamunotri MargState Highway 57 Mirzapur Pole 247121 India King Faisal Univ KFU Al Ahsa Dept Management Informat Syst Coll Business Adm Al Hufuf 31982 Saudi Arabia Jazan Univ Coll Comp Sci & Informat Technol Jazan 45142 Saudi Arabia Univ Sindh Fac Engn & Technol Dept Software Engn Sindh Pakistan

Kidney tumours (KTs) rank seventh in global tumour prevalence among both males and females, posing a significant health challenge worldwide. Early detection of KT plays a crucial role in reducing mortality rates, mitigating side effects, and effectively treating the tumor. In this context, computer-assisted diagnosis (CAD) offers promising benefits, such as improved test accuracy, cost reduction, and timesaving compared to manual detection, which is known to be laborious and time-consuming. This research investigates the feasibility of employing machine learning (ML) and Fine-tuned Transfer learning (TL) to improve KT detection. CT images of individuals with and without kidney tumors were utilized to train the models. The study explores three different image dimensions: 32x32, 64x64, and 128x128 pixels, employing the Grey Level Cooccurrence Matrix (GLCM) for feature engineering. The GLCM uses pixel pairs' distance (d) and angle (?) to calculate their occurrence in the image. Various detection approaches, including Random Forest (RF), Support Vector machine (SVM), Gradient Boosting (GB), and Light Gradient Boosting Model (LGBM), were applied to identify KTs in CT images for diagnostic purposes. Additionally, the study experimented with fine-tuned ResNet-101 and DenseNet-121 models for more effective computer-assisted diagnosis of KT. Evaluation of the efficient diagnostics of fine-tuned ResNet-101 and DenseNet-121 was conducted by comparing their performance with four ML models 121 achieved the highest accuracy of 94.09%, precision of 95.10%, recall of 93.5%, and F1-score of 93.95% when using 32x32 input images. These results outperformed other models and even surpassed state-of-the-art methods. This research classifying KT in CT kidney scans using ML approaches. The use of fine-tuned ResNet-101 and DenseNet-121 shows promising diagnosis of kidney tumors.

关键词： Kidney kidney tumor automatic diagnosis machine learning algorithms CT imaging deep learning transfer learning

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machine learning approaches to credit risk: Evaluating Turkish participation and conventional banks

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BORSA ISTANBUL REVIEW 2025年第3期25卷 497-512页

作者： Sugozu, Ibrahim Halil Verberi, Can Yasar, Sema Kyrgyz Turkish Manas Univ Dept Econ Bishkek 720044 Kyrgyzstan Sirnak Univ Dept Econ TR-73100 Sirnak Turkiye

This study investigates the impact of competition on credit risk in the Turkish banking system, focusing on Islamic (participation) and conventional banking under the same regulatory conditions regarding credit risk. The credit risk model is trained using CatBoost, extreme gradient boosting (XGBoost), random forest, and LightGBM algorithms, and the results are analyzed using Tree SHAP (SHapley Additive exPlanation) algorithms with swarmplots. The empirical analysis covers 33 conventional and 6 Islamic banks in T & uuml;rkiye, using annual data between 2009 and 2022. The findings reveal that (1) credit risk is relatively higher at participation banks than conventional banks;(2) competition increases credit risk;(3) loan size is a key determinant of credit risk;(4) profitability increases credit risk;and (5) economic growth reduces credit risk. This study recommends some policy measures, such as increasing the economy of scale at Islamic banks and implementing specific regulations at participation banks to reduce risk.

关键词： Competition Credit risk Dual-banking system machine learning algorithms Tree SHAP

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Reliable machine learning for Wearable Activity Monitoring: Novel algorithms and Theoretical Guarantees

Reliable Machine Learning for Wearable Activity Monitoring: ...

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IEEE International Conference on Computer-Aided Design

作者： Dina Hussein Taha Belkhouja Ganapati Bhat Janardhan Rao Doppa Washington State Univ Pullman WA USA

Wearable devices are becoming popular for health and activity monitoring. The machine learning (ML) models for these applications are trained by collecting data in a laboratory with precise control of experimental settings. However, during real-world deployment/usage, the experimental settings (e.g., sensor position or sampling rate) may deviate from those used during training. This discrepancy can degrade the accuracy and effectiveness of the health monitoring applications. Therefore, there is a great need to develop reliable ML approaches that provide high accuracy for real-world deployment. In this paper, we propose a novel statistical optimization approach referred as StatOpt that automatically accounts for the real-world disturbances in sensing data to improve the reliability of ML models for wearable devices. We theoretically derive the upper bounds on sensor data disturbance for StatOpt to produce a ML model with reliability certificates. We validate StatOpt on two publicly available datasets for human activity recognition. Our results show that compared to standard ML algorithms, the reliable ML classifiers enabled by the StatOpt approach improve the accuracy up to 50% in real-world settings with zero overhead, while baseline approaches incur significant overhead and fail to achieve comparable accuracy.

关键词： Training machine learning algorithms Upper bound Wearable computers machine learning Reliability theory Data models

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A Review on Artificial intelligence algorithms and machine learning to Predict the Quality of Groundwater for Irrigation Purposes

A Review on Artificial intelligence Algorithms and Machine L...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Poornima Jayaraman Kothalam Krishnan Nagarajan Pachaivannan Partheeban Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of Electrical and Electronics Engineering SSN College of Engineering Kanchipuram India Department of Civil Engineering Chennai Institute of Technology Chennai India

ISBN: (纸本)9798350333855

Utilising ordinary techniques to predict the irrigation water quality is generally costly and difficult for Farmers, especially for emerging countries. Moreover, machine learning and ANN algorithms are emerging technologies implemented in many countries nowadays. To overcome the issues for predicting and evaluating water quality indices in irrigation water, we need to analyse the efficient algorithms. There are several implemented models to predict the water quality parameters, and this study has been analysed various machine learning algorithms and ANN models. Finally concluded with an efficient developed approach that has been useful for the farmers in minimal expense and real-time forecasting of groundwater quality. The Water Quality Index (WQI) is the most basic indicator to distinguish underground water quality. Finally, insight calculations are utilised to streamline the boundaries of participation capabilities in the ANN model to promote the expectation of precision

关键词： Irrigation Analytical models Technological innovation machine learning algorithms Water quality machine learning Predictive models

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The impact of personality, behavior, and geography on participation in the private pension system in Türkiye: A machine learning approach

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BORSA ISTANBUL REVIEW 2025年第1期25卷 149-162页

作者： Verberi, Can Kaplan, Muhittin Sirnak Univ Dept Econ TR-73100 Sirnak Turkiye Ibn Haldun Univ Dept Econ TR-34480 Istanbul Turkiye

This study examines regional disparities in the factors that affect participation in the Private Pension System (PPS) in T & uuml;rkiye, focusing on sociodemographic characteristics, personality traits and behavior, and pension and financial literacy. The behavioral factors identified encompass procrastination, locus of control, pessimism, compulsive buying, and time perspective, and the personality traits include openness, agreeableness, extraversion, neuroticism, and conscientiousness. The study employs data on two provinces in T & uuml;rkiye, S,& imath;rnak and Istanbul, and uses XGBoost and Tree SHAP algorithms and a probit model. Our findings indicate that personality traits such as openness, agreeableness, and conscientiousness have a positive influence on individual engagement in pension plans, whereas extraversion has a negative impact. Additionally, basic pension literacy is more influential than advanced pension literacy. The results also show that regional geography significantly influences personality and behavioral factors. Finally, a perception of protection is a critical factor in PPS participation.

关键词： Behavioral factors machine learning algorithms Personality traits Private pension system Tree SHAP

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