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Analysis of interferometric configuration for optical devices

Journal of Optical Communications 2024年第4期45卷 737-740页

作者： Sharma, Himanshu Kumar, Arun Department of Computer Science & Engineering Jecrc University Jaipur India Department of Electrical & Electronics Engineering Jecrc University Jaipur India

With the exponential growth in information related applications and the continuous increase in voice over IP (VoIP) applications, the carriers are expanding their networks to provide improved services to their end users. In such cases, the synchronous optical network/synchronous digital hierarchy model is not suitable. In this work, we are going to investigate the electro optic switch with the help of a VB script to simulate the device for various parameters. Further, we will then scan the central electrode voltages and take account of the overlap integral taking place at the output port with the help of waveguide mode. The best switching for switches at different voltages so as to have negligible coupling loss for different input signals is investigated. We aim to evaluate the MZI switches with the Li-Tn channel profile wave guiding structure and confirm their performance as compared to the existing MZI switches. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Chemical bonds

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An Experimental Analysis of Machine Learning Models for Diabetes Classification 7th

An Experimental Analysis of Machine Learning Models for Di...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Ghosh, Subhayu Acharya, Riyan Jana, Nanda Dulal Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819764648

Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection and effective management of diabetes are crucial to prevent severe complications and improve the quality of life for affected individuals. Machine Learning (ML) techniques have shown great promise in aiding the early detection of various diseases on patient data, including diabetes. ML algorithms can analyze vast datasets, identify patterns, and make accurate predictions, helping medical professionals to diagnose diabetes at its early stages. In our work, we employed several ML models for diabetes classification using different datasets. These models include K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient-Boosted Model (GBM), eXtreme Gradient Boosting (XG-Boost), Adaptive Boosting (Ada-Boost), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). We performed a comparative analysis of their performance on three distinct datasets using evaluation metrics like accuracy, precision, F1-score, sensitivity, specificity and Cohen’s Kappa Value. Our findings revealed that the RF algorithm is optimal for symptoms-based and primary lab report-based diabetes detection, while XG-Boost excels in classifying different types of diabetes from a multi-class dataset. Moreover, we investigated diverse symptoms and their impact on diabetes outcomes, offering insights into preventive measures and early stage monitoring for this disease classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Three-Way Task Scheduling Algorithm for Cloud Computing 28

Three-Way Task Scheduling Algorithm for Cloud Computing

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28th International computer science and engineering Conference, ICSEC 2024

作者： Balobaid, Awatef Salem Jazan University College of Engineering and Computer Sciences Department of Computer Science Jazan45142 Saudi Arabia

ISBN: (纸本)9798350366860

Cloud task scheduling is a crucial aspect of cloud computing, and it directly influences cloud resource usage. This study presents a cloud task scheduling algorithm based on granularity and three-way decision making (CTSA-3WD). This research aims to address the issues of load imbalance, low resource utilization, and job completion time. In the proposed approach, the execution duration of cloud jobs and the actual computing resource situation restrict the task set's light-load and heavy-load functions. The algorithm is based on the three-way decision-making and separates the work set into three pieces. The system focusses on three distinct task sets and determines an optimal scheduling strategy by employing the Max-Min algorithm for the significant proportion of light-load jobs task set, the Min-Min algorithm for the substantial percentage of heavy-load studies, and the Min-Min and Max-Min algorithms combination for the task set with both light and heavy load tasks. The experimental results demonstrate that the CTSA-3WD algorithm, when compared to Min-Min, Max-Min, and selective scheduling algorithms, effectively enhances overall resource utilization. Additionally, it enables improved load balancing across the entire system. © 2024 IEEE.

关键词： Scheduling algorithms

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Exchange Rate Forecasting in Money Market

Exchange Rate Forecasting in Money Market

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Panda, Manaswinee Madhumita Panda, Surya Narayan Pattnaik, Prasant Kumar Chitkara University Institute of Engineering and Technology Chitkara University Computer Science and Engineering Punjab India Kiit University Computer Science and Engineering School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350325904

Changes in currency exchange rates can impact a country's economy, making it important to carefully consider and anticipate foreign currency exchange rates in the money market. Therefore, accurately predicting FOREX fluctuations is a significant concern. Convolutional neural networks (CNN) and random forest regression have been suggested as prediction algorithms for foreign exchange. The efficiency of the approach is assessed by forecasting the worth of the New Zealand dollar against the US dollar (NZU/USD)for 2, 4, 6, 8, 10, 12, and 14 weeks into the future. The best weight for the suggested Model is calculated utilizing the adaptive learning rate method (ADAM) optimization approach. The new approach provides an additional precise way to estimate foreign exchange rates (Forex) based on the estimation criteria. The CNN model with the layer for random forest regression performs better than the other three models. © 2023 IEEE.

关键词： Forecasting

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Small Intestine Cancer Prediction using Deep Learning: A Comparative Study of CNNs and RNNs 3

Small Intestine Cancer Prediction using Deep Learning: A Com...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Sherin Beevi, L. Joe Prathap, P.M. Reddy, Pulimi Hanith Sai Kumar Priya, Gomasani Bhanu Sai R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India R.M.D. Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350381580

Small bowel cancer is one of the rare tumors that often stays untreated until it has spread to a more advanced stage. Cancer is the leading cause of death in the globe. Early detection is critical for successful treatment, and machine learning algorithms have showed promise in detecting cancer at its early stages. In this article, we offer a deep learning-based technique to predicting small intestinal cancer that employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We used a dataset of clinical characteristics and biomarkers from small intestinal cancer patients to train and evaluate our models. Our findings reveal that the proposed models were highly accurate in predicting small intestinal cancer, with the CNN-based model slightly outperforming the RNN-based model. We also compared our approach to other cutting-edge methodologies, and discovered that the proposed models surpassed them in terms of accuracy. Our study indicates the potential of deep learning-based approaches for predicting small intestine cancer and provides information on the relative efficacy of CNNs and RNNs. Our findings can help design more accurate and efficient cancer prediction models for small intestinal cancer. © 2024 IEEE.

关键词： Recurrent neural networks

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On the Complexity Aspects of Various Sub-classes of Linear Languages 9

On the Complexity Aspects of Various Sub-classes of Linear L...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Praveen, G.L. Sunil, K.S. Mar Baselios College of Engineering And Technology Department of Computer Science & Engineering Kerala Thiruvananthapuram India Government Engineering College Idukki Department of Computer Science & Engineering Kerala Painavu India

ISBN: (纸本)9798350394474

The non-terminals in the underlying grammar play an important role in determining different characteristics of the language generated. The languages generated by the grammars are determined by a unique non-terminal known as the start symbol. Even without altering any of the production rules, the grammar produces a distinct language if we swap the start symbol for any other non-terminal. Occasionally, we purposefully include extra non-terminals to attain specific characteristics in both the grammar and parse trees. One such scenario is when we convert a language to Chomsky's Normal Form (CNF), in which case we add additional non-terminals to convert the parse tree into a binary tree. In this paper, we consider different sub classes of CFLs by restricting the number of nonterminals. We study different characteristics like the generative power of the grammar, the stack height, and the number of turns of the PDA which accept the languages produced by the grammars. We show that these characteristics are influenced by the number of nonterminals in the grammar. © 2024 IEEE.

关键词： Binary trees

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Invoice Processing Automation Using UI Path Studio 5

Invoice Processing Automation Using UI Path Studio

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ramyadevi, R. Anbuselvan, A. Julian, Anitha Selvi, S. Murali Dhar, M.S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Invoices are the fundamental document that play a vital role in encapsulating the details of the products and the business transactions between the seller and the buyer. We cannot check our invoices at any time for emergencies. Substituting the case, by storing the basic details of multiple invoices in an excel sheet might be used for future purposes. This paper provides the advancement of data extraction through automation technologies. We begin this research by the technique Optical Character Recognition (OCR) in automation of data extraction from invoices. This reduces the manual errors during extraction as the entire process works with the automation and provides an accuracy of 95%. The paper discusses the design of the automation that has to be processed for the data extraction including the invoice details, line items and relevant metadata and stored in a structured and accessible format. This storage allows the customer to get through all their product details in a document. Furthermore, the study investigates the integration of extracted data from the invoice to the customer. This integration acts as the strategic role in transforming invoice data into a dynamic asset, enabling customers to glean insights. The paper incorporates real-world examples and case studies to illustrate implementations of scrapping techniques using automation. By addressing challenges such as technological limitations, integration complexities, and data security concerns, the paper provides practical understanding for customers in processing the automation. © 2024 IEEE.

关键词： Accuracy Automation Technologies Data Extractions Data Storage Invoice Processing Optical Character Recognition (OCR)

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Leaving None Behind: Data-Free Domain Incremental Learning for Major Depressive Disorder Detection

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IEEE Transactions on Affective Computing 2025年第2期16卷 758-770页

作者： Chen, Tao Guo, Yanrong Hao, Shijie Hong, Richang Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei230009 China

While deep learning techniques have shown promising performance in the Major Depressive Disorder (MDD) detection task, they still face limitations in real-world scenarios. Specifically, given the data scarcity, some efforts have resorted to aggregating data from different domains to expand the data volume. However, their effectiveness is currently limited by the domain gap and data privacy. Additionally, the class imbalance issue is particularly severe in our application, leading to biased classifying performance accordingly. To address these challenges, we propose Data-Free Domain Incremental Learning for the MDD detection (DIL-MDD) task, accommodating multiple feature distributions by only accessing well-trained models from previous domains and the data in the current domain. Specifically, DIL-MDD consists of two key modules: Adaptive Class-tailored Threshold Learning (ACTL) and Data-Free Domain Alignment (DFDA). The first module measures the discrepancy between the outputs of two sequential domains, based on which we learn a class-tailored threshold adaptively. Building on this, we differentiate between samples that either exhibit similarities or dissimilarities with the previous domain, where this similar sample set is identified to investigate the feature distribution of the historical data. The second module imposes an alignment constraint to narrow the gap between these two sample sets, thereby exploring the expertise of the previous domain. To validate the effectiveness of the proposed method, we conduct extensive experiments on the public MDD datasets, i.e., DAIC-WOZ, MODMA, and CMDC. We also apply our method to another mental health condition, Autism Spectrum Disorder (ASD), to further demonstrate its applicability. Finally, the ablation studies validate the superiority of the proposed modules. © 2024 IEEE.

关键词： Adversarial machine learning

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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An optimal deep learning-based framework for the detection and classification of android malware

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Journal of Intelligent and Fuzzy Systems 2023年第6期44卷 9297-9310页

作者： Jebin Bose, S. Kalaiselvi, R. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Tamil Nadu Kumaracoil India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Gummidipoondi Tamil Nadu Puduvoyal India

The use of smartphones is increasing rapidly and the malicious intrusions associated with it have become a challenging task that needs to be resolved. A secure and effective technique is needed to prevent breaches and detect malicious applications. Through deep learning methods and neural networks, the earliest detection and classification of malware can be performed. Detection of Android malware is the process to identify malicious attackers and through the classification method of malware, the type is categorized as adware, ransomware, SMS malware, and scareware. Since there were several techniques employed so far for malware detection and classification, there were some limitations like a reduced rate of accuracy and so on. To overcome these limitations, a deep learning-based automated process is employed to identify the malware. In this paper, initially, the datasets are collected, and through the preprocessing method, the duplicate and noisy data are removed to improve accuracy. Then the separated malware and benign dataset from the preprocessing phase is dealt with in feature selection. The reliable features are extracted in this process by Meta-Heuristic Artificial Jellyfish Search Optimizer (MH-AJSO). Further by the process of classification, the type of malware is categorized. The classification method is performed by the proposed Dense Dilated ResNet101 (DDResNet101) classifier. According to the type of malware the breach is prevented and secured on the android device. Although several methods of malware detection are found in the android platform the accuracy is effectively derived in our proposed system. Various performance analysis is performed to compare the robustness of detection. The results show that better accuracy of 98% is achieved in the proposed model with effectiveness for identifying the malware and thereby breaches and intrusion can be prevented. © 2023-IOS Press. All rights reserved.

关键词： Smartphones

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