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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kediya, Shailesh Somnathe, Tushar Surya, Amrin Garg, Neha Ali, Athar Javed Gudadhe, Amit A. School of Logistics and Supply Chain Management Symbiosis Skills and Professional University Pune India Placements and Corporate Connect Imt Nagpur India Renaissance Institute of Management Studies Chandrapur India Central Institute of Business Management Research and Development Nagpur India Faculty of Engineering and Technology Wardha India

ISBN: (纸本)9798350369960

The practice of giving and accepting promotions is one of the most important in human resources. Management can keep morale and productivity high with the use of a merit-based, open promotion system inside the company. The prospect of a promotion is one of several extraneous reasons that drives many employees. maintaining the worker engaged and committed to the organization is crucial to maintaining his current productivity levels intact. It's a vital tool for motivating workers and monitoring their output. The promotion process takes into account a wide range of factors, including an employee's seniority, performance, competencies, age, awards, training score, and organizational devotion. Workers' promotion needs will be evaluated using machine learning methods such Support Vector machine, Artificial Neural Network, and Random Forest, and a prediction strategy will be explored based on the findings. Accuracy, precision, and recall all improved by 1%, and the f1-score reached a perfect 98% when the ROS technique was combined with the Random Forest algorithm. Human resource professionals and business leaders might use the findings of this study to make more accurate predictions about who and when promotions will occur. © 2023 IEEE.

关键词： machine learning algorithms Principal Component Analysis Support Vector machine

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Determining the most accurate machine learning algorithms for medical diagnosis using the monk' problems database and statistical measurements

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2025年第2期37卷 357-376页

作者： Avuclu, Emre Aksaray Univ Dept Software Engn Fac Engn Aksaray Turkiye

Computer-aided diagnosis process in the field of health, especially cancer diagnosis, is of vital importance. Computer-aided diagnosis helps specialist physicians to make the most accurate diagnosis. According to research studies, it has been stated that the number of wrong or late diagnosis increases with each passing year and ultimately causes the death of people living in many parts of the world. For this reason, some calculations must be made to determine the most accurate one in the algorithm to be used to make the correct diagnosis. In this study, three different database Monk' problems were used to determine the most accurate algorithm for medical diagnosis. Monk' problems are used as one of the several classification problems used to create an important comparative study. Train and test operations were performed using five different machine learning algorithms (MLAs) (k Nearest Neighbor (k-NN), Decision Tree Algorithm (DT), Random Forest Algorithm (RF), Naive Bayes algorithm (NB), Support Vector Cases (SVM)). These machine learning algorithms are compared statistically in terms of performance. Two different databases in the medical field were used to test the results (Breast Cancer Coimbra Data Set, Diabetic Retinopathy Debrecen Data Set). In the test processes in the experimental studies, the highest accuracy rate was obtained from the k-NN, DT, RF, NB, SVM algorithms, respectively;0.9758, 1, 1, 0.9180, 0.9344. The best performance was obtained from RF MLA for 1. dataset, DT MLA for 2. dataset, highest accuracy rates from k-NN and RF MLAs in 3. dataset.

关键词： The most accurate diagnosis Monk' problems Cancer diagnosis statistical measurements machine learning algorithms

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In-depth exploration of machine learning algorithms for predicting sidewall displacement in underground caverns

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GEOMECHANICS AND ENGINEERING 2024年第4期37卷 307-321页

作者： Samadi, Hanan Alanazi, Abed Muhodir, Sabih Hashim Alsubai, Shtwai Alqahtani, Abdullah Marzougui, Mehrez Univ Halabja Civil Engn Dept IRO Halabja 46018 Iraq Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj Dept Comp Sci POB 151 Al Kharj 11942 Saudi Arabia Cihan Univ Erbil Dept Architectural Engn Erbil Kurdistan Iraq King Khalid Univ Coll Comp Sci Abha 61413 Saudi Arabia

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

关键词： importance ranking machine learning algorithms sidewall displacement underground caverns

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Neuromorphic devices assisted by machine learning algorithms

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INTERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第4期7卷 042007-042007页

作者： Huo, Ziwei Sun, Qijun Yu, Jinran Wei, Yichen Wang, Yifei Cho, Jeong Ho Wang, Zhong Lin Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Engn Beijing 100049 Peoples R China Guangxi Univ Ctr Nanoenergy Res Sch Phys Sci & Technol Nanning 530004 Peoples R China Shandong Zhongke Naneng Energy Technol Co Ltd Dongying Peoples R China Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea Georgia Inst Technol Atlanta GA 30332 USA

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided. The review discusses the basic structure of simple neuron models inspired by biological neurons and how they process information in simple neural networks, laying the foundation for neuromorphic device *** progress in the fabrication of neuromorphic devices is highlighted, focusing on advancements in materials, structures, and the development of st

关键词： neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors

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Utilizing machine learning algorithms to Predict Accuracy of the Index of Relative Tectonic Activity (IRTA), Dhansiri (North) River Basin in India and Bhutan

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IEEE ACCESS 2024年 12卷 60482-60495页

作者： Roy, Shayani Mandal, Pritam Chowdhury, Amitava Abdullah-Al-Wadud, M. Seikh, Ariyan H. Seikh, Ayan H. Ghosh, Manojit Mukhopadhyay, Ananya Indian Inst Engn Sci & Technol IIEST Dept Earth Sci Howrah 711103 India Indian Inst Engn Sci & Technol IIEST Dept Met & Mat Engn Howrah 711103 India Pandit Deendayal Energy Univ Dept Comp Sci & Engn Gandhinagar 382007 India King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia Motilal Nehru Natl Inst Technol Allahabad Prayagra Dept Comp Sci & Engn Prayagraj 211004 India

A river tries to maintain a dynamic equilibrium state by adjusting different controlling factors. A significant change in one of the controlling factors will dictate modifications in the others to re-establish the equilibrium in a river system. A river basin may indicate active tectonic movements more precisely than the best space-based geodetic techniques. Morphometric analyses, with the help of DEM and GIS often generates insights into the tectonic activities of an area. The Dhansiri (North) River basin lies on the north bank of the Brahmaputra and on the northern part of the Dhansiri-Kopili fault, which is tectonically active at different times. This paper analyses the impact of relative tectonics on drainage pattern development in the basin based on various morphometric parameters of linear (stream length ratio, bifurcation ratio), areal (form factor, basin elongation ratio), and relief (relief ratio, ruggedness number) aspects. Eleven well-known ML algorithms,namely, Logistic Regression (LR), K Nearest Neighbors (KNN), Random Forest (RF), Support Vector machine (SVM), Decision Tree (DT), Gaussian Naive Bayes (GNB) classifier, Neural Network (NN), Extra Tree Classifier (ET), Ada Boost Classifier (AB), Gradient Boosting Classifier (GB), XG Boost Classifier (XGB) is used to model the spatial distribution of relative tectonic *** algorithms were executed in Python to assess prediction accuracy using standard metrics like accuracy, precision, recall, and F1 score. The assessment utilized widely used libraries such as sci-kit-learn and TensorFlow to implement and test the algorithms, benefiting from their comprehensive model evaluation and performance assessment tools. The SVM, ET, DT, and GNB techniques had the best performance, achieving an accuracy of 82.60 percent as per the modeling *** Dhansiri (North) is a sixth-seven-ordered basin characterized by a dendritic drainage pattern. Notably,the spatial prediction of morphometric parameters with M

关键词： Morphometric parameters index of relative tectonic activity tectonics Dhansiri (North) river basin machine learning algorithms

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Two-way detection of tuberculosis bacilli using fano resonance sensor and machine learning algorithms

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MATERIALS RESEARCH EXPRESS 2025年第3期12卷 035003-035003页

作者： Singh, Pooja Singh, Lokendra Yadav, Sameer Shekhar, Sudhanshu Bhalerao, Siddharth Rai, Preeti Pareek, Prakash Suri, Ajay SSBSR Sharda Univ Ctr Solar Cells & Renewable Energy CSRE Dept Phys Greater Noida 201310 India Graph Era Dept ECE Dehra Dun 248002 Uttarakhand India Gyan Ganga Inst Technol & Sci Dept CSE Jabalpur 482003 MP India Vishnu Inst Technol Dept ECE Bhimavaram 534202 Andhra Pradesh India ABES Engn Coll Dept ECE Ghaziabad 201009 Uttar Pradesh India

A serious worldwide health concern, tuberculosis (TB) necessitates creative methods for quick and precise diagnosis. This paper presents the detection of Mycobacterium tuberculosis (mTB) using a two-way approach utilizing refractive index (RI) sensors and machine learning (ML) techniques. The approach is validated by adopting a Fano resonance (FR) sensor which consists of freshly prepared cerium oxide nanostructures (CNS) covered sensing slots. The CNS is prepared by using combustion technique and characterized through transmission electron microscopy (TEM) and x-ray diffraction (XRD). The RI values sensed through the sensor yield a high sensitivity and autocorrelation coefficient of 163 nm RIU-1 and R2=92.37% , respectively. The microscopic sputum smear examination is used to detect mTB through five different ML classifiers. Among all, the random forest classifier provides the highest accuracy of 96%. According to preliminary findings, the suggested system provides a quick, affordable, and non-invasive diagnostic option with excellent accuracy, sensitivity, and specificity. The approach addresses important TB diagnostic problems and is more suitable for low-resource environments. This hybrid strategy can transform TB detection, enhance patient outcomes, and support TB control initiatives.

关键词： fano resonance mycobacterium tuberculosis two-way detection machine learning algorithms

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Multi-Channel Nonlinearity Mitigation Using machine learning algorithms

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2535-2550页

作者： Zhao, Haotian Diaz, Julian Camilo Gomez Hoyos, Sebastian Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

This paper investigates multi-channel machine learning (ML) techniques in the presence of receiver nonlinearities and noise, and compares the results with the single-channel receiver architecture. It is known that the multi-channel architecture relaxes the sampling speed requirement of analog to digital conversion and provides significant robustness to clock jitter and front-end noise due to the bandwidth-splitting property inherent in these receivers. However, when a high-voltage swing signal is used in a wireline communication link, the received signal suffers from third-order harmonic distortions and inter-modulation products caused by the nonlinearity profile of the analog front-end (AFE). To this end, this paper proposes the channel decision passing (CDP) algorithm in combination with nonlinear feedback cancellation as a low-complexity candidate for nonlinearity mitigation and compares the performance of this solution with other well-known ML algorithms. Simulation results show significant improvement in a multi-channel receiver architecture equipped with nonlinear feedback cancellation and CDP in comparison with its single-channel counterpart under practical nonlinearity profiles and noise conditions.

关键词： Receivers Clustering algorithms Classification algorithms machine learning algorithms Bandwidth Supervised learning Unsupervised learning Multi-channel receiver nonlinearities machine learning supervised learning unsupervised learning reinforcement learning

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Comparative Analysis of machine learning algorithms for CKD Risk Prediction

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IEEE ACCESS 2024年 12卷 171205-171220页

作者： Yang, Weilin Ahmed, Nasim Barczak, Andre L. C. Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Bond Univ Ctr Data Analyt Gold Coast Qld 4226 Australia

Chronic Kidney Disease (CKD) remains a significant global health challenge, with increasing prevalence and a substantial impact on patient quality of life. Early and accurate prediction of CKD risk is crucial for timely intervention and management. This study presents a comprehensive comparative analysis of both machine learning and deep learning algorithms applied to predict CKD risk. The research involved the application of eight traditional machine learning algorithms: Naive Bayes, K-nearest Neighbors, Decision Tree, Random Forest, Support Vector machine, Logistic Regression, AdaBoost, and XGBoost, each implemented on a CKD dataset retrieved from the UCI data repository. Furthermore, three neural network-based algorithms, Artificial Neural Network (ANN), Simple Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) were used to compare to the traditional algorithms. This comparative study not only assessed each algorithm's performance in terms of accuracy, precision, recall, and F1 score but also examined their computational efficiency and applicability in real-world clinical settings. All eleven algorithms were trained with three versions of the dataset. The first version kept the original unbalance between classes and used KNN imputation to fill up missing values (unbalanced). The second dataset used SMOTENC to create new samples to balance the dataset (balanced). The third dataset used feature selection to choose 14 features from the original 24. The results showed that there is almost no performance difference among the classifiers produced with the balanced, unbalanced and feature selection datasets. This means that the best algorithms for this task are the ones with short training and testing runtime, namely RF, SVM, AdaBoost and XGBoost. The experiments also showed that the neural network-based algorithms had no performance advantage and were slower to train due to the small size of samples available in the original dataset.

关键词： Chronic kidney disease Predictive models Accuracy Random forests machine learning algorithms machine learning Prediction algorithms Nearest neighbor methods Support vector machines Classification algorithms machine learning deep learning risk prediction healthcare analytics

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Survival Analysis of Thyroid Cancer Patients Using machine learning algorithms

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IEEE ACCESS 2024年 12卷 61978-61990页

作者： Alhashmi, Saadat M. Polash, M. D. Shohidul Islam Haque, Aminul Rabbe, Fazley Hossen, Shazzad Faruqui, Nuruzzaman Hashem, Ibrahim Abaker Targio Fathima Abubacker, Nirase Univ Sharjah Dept Informat Syst Sharjah U Arab Emirates Daffodil Int Univ Dept Comp Sci & Engn Dhaka 1216 Bangladesh Frankfurt Univ Appl Sci Dept Informat Technol D-60318 Frankfurt am Main Germany Daffodil Int Univ Dept Software Engn Dhaka 1216 Bangladesh Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Asia Pacific Univ Sch Comp Kuala Lumpur 57000 Malaysia

The medical community strives continually to improve the quality of care patients receive. Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent years have witnessed the development of numerous new cancer survival prediction models. Most attempts to predict the prognosis of people with malignant growth rely on classification techniques. We could experiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology, and End Results) data. These models were created using machine learning techniques by selecting univariate features and calculating correlations. We illustrated the variation in results and discrepancy of impurity that can result from varying data quantities and critical factors. Seventeen crucial factors were identified, and a group of classification algorithms were trained to evaluate the effectiveness of an estimation technique. In the display mode, the accuracy of these computations ranges from 97% to 99% Along with accuracy, the models are further evaluated regarding the F1 score, precision, recall, and the AUC score. Compared to earlier studies, a more accurate model has been developed, and, to the best of our knowledge, our prediction model is superior to the models studied in the previous works.

关键词： Thyroid cancer Predictive models Encoding Random forests machine learning algorithms Medical treatment Patient monitoring Logistic regression Life estimation machine learning random forest thyroid survivability

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Analysis of Quantum machine learning algorithms in Noisy Channels for Classification Tasks in the IoT Extreme Environment

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 3840-3852页

作者： Satpathy, Sritam Kumar Vibhu, Vallabh Behera, Bikash K. Al-Kuwari, Saif Mumtaz, Shahid Farouk, Ahmed Indian Inst Sci Educ & Res Dept Phys Sci Berhampur 760010 India Bikashs Quantum Private Ltd Quantum Comp Mohanpur 741246 India Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Doha Qatar Silesian Tech Univ Dept Appl Informat PL-44100 Gliwice Poland Nottingham Trent Univ Engn Dept Nottingham NG1 4FQ England South Valley Univ Fac Comp & Artificial Intelligence Dept Comp Sci Hurghada 83523 Egypt

By 2050, there will be a 50% rise in energy demand,and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively har-vesting renewable energy. However, it gets more challenging for conventional computers to perform optimization as the number of factors affecting power generation and transmission *** environmental cases will consequently lead to the imper-fect functioning of Internet of Things (IoT) systems. By utilizing quantum-mechanical properties, such as superposition and entan-glement, quantum computers can computationally out perform classical computers while consuming much less energy. In thisarticle, we investigate various quantum machine learning algo-rithms on two data sets (TWTDUS and SDWTT18) related toIoT extreme environment and study the effect of a noisy quan-tum environment. We observe that for the TWTDUS data set, thevariational UU dagger with analytical clustering methods achieves thehighest accuracy of 98.10%. Similarly, for the SDWTT18 data set,theUU dagger method withk-means clustering achieves an accuracyof 94.43%. The results show that the accuracy of the proposedquantum algorithms outperforms the existing classical methodsand can be utilized to forecast output power generation daily bymeasuring the metrics required in energy sector decision-makingsituations. This will be useful to save energy and costs in an IoT-extreme environment, where energy organizations must decideinstantly whether to start or stop generating units.

关键词： Quantum computing Internet of Things Computers Optimization Costs Power generation machine learning algorithms Internet of Things (IoT) extreme environment quantum energy utilization quantum neural network (QNN) UU dagger method variational UU dagger method

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