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2023 International Conference on Advances in Computation, communication and information technology, ICAICCIT 2023

作者： Vinutha, C.B. Vivek, V. Pandeya, Megha Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Machine learning relies heavily on time series forecasting, which has a wide range of uses, including although not limited to forecasting air quality, traffic patterns, and power use. Recent developments in deep training and matrix factorization models, however, have shown competitive performance and are emerging as alternatives. However, a fundamental disadvantage of these contemporary models over more established ones is their complexity. The Gradient Boosted Regression Tree, a well-known machine learning baseline, and popular deep learning models are compared in this work. We convert the time series forecast challenge into a window-based prediction issue, which is akin to (DNN) models. In addition, we design the GBRT model's input and output structure. In order to construct a single input instance to simulate a multi-output GBRT model, we concatenate the desired values with additional characteristics for each training window before flattening them. Using nine datasets, we undertake a thorough investigation and evaluate eight cutting-edge deep learning models recently presented at prestigious conferences. The findings show that a simple GBRT model performs much better than the state-of-the-art DNN models evaluated in this research thanks to the window-based input transformation. © 2023 IEEE.

关键词： Deep Learning DNN and Temporal Regularized Matrix Factorization Feature Engineering Gradient Boosted Regression Trees (GBRT) Time Series Forecasting

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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An Explainable Educational Data Mining System for Predicting Student Academic Performance

An Explainable Educational Data Mining System for Predicting...

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2024 IEEE International Conference on Signal Processing, information, communication and Systems, SPICSCON 2024

作者： Islam, Md Mahmudul Sojib, Farhad Hossain Mihad, Md Fazle Hasan Hasan, Mahmudul Rahman, Mahfujur Department of Electronics and Communication Engineering Bangladesh Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh School of Information Technology Deakin University GeelongVIC3220 Australia

ISBN: (纸本)9798331510213

Students’ educational accomplishments are vital as they are a key indicator of their overall academic journey. This study proposes a Machine Learning (ML) enabled approach to categorize students’ academic performance with the explainability of the proposed EDM system. We utilized standard ML models like Decision Tree, Random Forest, and Boosting models such as Gradient Boosting and Extreme Gradient Boosting (XGB) for multi-class classification, where XGB outperforms other models with 83% accuracy, precision, and recall. The proposed EDM approach demonstrates superior performance compared to existing works, offering a more effective approach for anticipating and enhancing student academic outcomes. The explainability results demonstrate that changes in curriculum, courses, and the status of paying tuition fees can bring a positive result in the student’s academic performance. Future research focuses on designing a recommendation system to help the students justify their current status and get personalized support to enhance academic performance in real time. © 2024 IEEE.

关键词： Students

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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The Deep Learning and Machine Learning Methods for Botnet Identification in the Internet of Things 6

The Deep Learning and Machine Learning Methods for Botnet Id...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Gangone, Anjali Bala, Bhoomeshwar Gangone, Swapna Bharat Kumar, G.J. CMR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Debre Tabor University Department of Information Technology Debre Tabor Ethiopia Mettu University Department of Computer Science Mettu Ethiopia

ISBN: (纸本)9798350304480

In recent years, IoT (Internet of Things) devices and their attendant solutions have become more prevalent, particularly in manufacturing, supply chain management, healthcare, transportation, and numerous other 'smart'settings. Thanks to IP (Internet Protocol) addresses designated to IoT devices, cyber-physical systems are now able to communicate with one another automatically. Inadequate protection on these ultimate platforms has resulted in attacks such as denial-of-service, Botnets, identity theft, and information theft. Mirai, Torii, Emotet, Dridex, Trickbot, Gluteba, and QBots are examples of Internet of Things-related threats. With the aid of AI, we can at last create cyber-physical systems that are both trustworthy and secure. Machine learning and deep learning approaches combat cyberattacks by identifying and blocking Botnets. This paper investigates Botnet attacks, which are prevalent in IoT devices due to a lack of security requirements during manufacturing or a lack of user security awareness. Anomaly detection is a potential weapon in the hands of machine learning or Deep Learning for identifying and preventing cyberattacks on Internet of Things devices. In this study, we present a Botnet detection system capable of detecting an attack on live traffic. Next, we employ the Aposemat IoT-23 dataset to evaluate and compare a variety of machine learning as well as deep learning techniques for detecting Botnets based on their conventional characteristics. Gated Recurrent Unit (GRU) is a deep learning model with a detection accuracy of 99.87% for identifying Botnets. Thirdly, we use the Wireshark program to investigate the Aposemat IoT- 23 dataset's raw packet captured (pcap) files for attacks. Then, we employ GRU, a deep learning model, to identify malware infections with a 99.89% success rate and reduced time complexities. © 2023 IEEE.

关键词： Botnet Cyberattack Deep Leaning Devices IoT Machine Learning

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AI-Powered Developments in Oncological Diagnostic Imaging: Revolutionary Approaches to Cancer Treatment

AI-Powered Developments in Oncological Diagnostic Imaging: R...

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2023 International Conference on Advances in Computation, communication and information technology, ICAICCIT 2023

作者： Singh, Shweta Ashwini, B. Vivek, V. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

This project aims to demonstrate new uses of deep learning techniques in the detection of cancer. A huge body of examples that show the desired behaviour may be used to train a system using deep learning, which is a family of algorithms. Introduction of deep learning methods to tumour imaging may help pathologists identify and categorise cancer early on so that patients can get the right therapies to improve their chances of surviving or recovering. Utilising statistics and other analytical techniques and scienceDirect have data, it appears that since the late 1990s, a sharp increase in research on deep learning technologies in medical imaging has been motivated by the exceptionally high mortality rates of some cancer types in order to address the concomitant issues of a more precise recognition and the identification of cancer types to use effective therapies for cancer. This study also identifies the institutions, nations, and intellectual subject areas with the greatest levels of intellectual output in multiple disciplines as the origins of the trajectory of deep learning technology in cancer detection. According to Amara's law, this novel technique may result in a change in the way that cancer diagnoses and treats its many forms. When they can submit digital photos to laboratories in other developed nations to have malignancy kinds diagnosed, this fresh innovation may also aid developing regions, narrowing the present healthcare divide as much as feasible. © 2023 IEEE.

关键词： Cancer Detection Cancer Types Deep Learning Early Diagnosis Machine Learning Medical Imaging Metastatic Cancer Pathologists and Clinical Oncology Therapies Tumour Imaging

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An Exhaustive Meta-Analysis on Consumer Retention Forecasting Using Advanced Machine Learning Techniques 3

An Exhaustive Meta-Analysis on Consumer Retention Forecastin...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Shreenidhi, H.S. Yadav, Dhyan Chandra Swetha, G. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka562112 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

In this meta-analysis, we present a robust approach for consumer retention forecasting by integrating advanced machine learning algorithms. The method comprises three core algorithms: Consumer Behaviour Neural Network (CBNN), Retention Decision Trees (RDT), and Long Short-Term Memory for Retention (LSTM-R). The objective is to model intricate relationships within consumer behaviours and accurately predict their retention probabilities. CBNN a key component, adeptly captures intricate patterns within consumer behaviour data. It involves multiple hidden layers with activation functions, enabling accurate prediction of retention probabilities based on input features. The flowchart for CBNN showcases the training process involving forward and backward passes, underlining its deep learning approach. RDT this method utilizes decision tree algorithms to predict consumer retention decisions. By recursively splitting the dataset based on information gain, RDT efficiently models consumer behaviour, as depicted in the flowchart. information gain is maximized to create a robust predictive model. LSTM-R tailored for temporal data in consumer retention prediction, leverages specialized LSTM architecture. It effectively processes sequential consumer interactions, crucial for accurate prediction of consumer retention probabilities. The proposed method integrates these algorithms, leveraging historical consumer data, and conducts a comprehensive meta-analysis to evaluate their predictive performance. © 2023 IEEE.

关键词： Decision trees

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The Performance Analysis of Distributed Denial of Service Attacks in Wireless Networks 2

The Performance Analysis of Distributed Denial of Service At...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Agrawal, Trapty Raghavendra, R. Singh, K. Ranjith Rajiv Gandhi, N. Dhabliya, Dharmesh Katariya, Jitendra Kumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350308846

Complete studies paper that examines the outcomes that malicious 0.33-celebration actors will have on Wi-Fi networks via dispensed denial of carrier (DDoS) attacks. This paper explores the available numerous assault models and the potential countermeasures that may be applied if you want to mitigate the damage due to such assaults. In addition, this paper focuses on the capability blessings related to deploying preventative measures to lessen the impact of such assaults. Moreover, the paper investigates existing attack trends. It affords an outline of the safety techniques to be had for limiting the harm caused by DDoS attacks in wireless networks. By comparing and analyzing the performance of diverse protection mechanisms, this paper offers an evidenceprimarily based evaluation of the effectiveness of DDoS attack safety answers. ultimately, this paper concludes with the aid of supplying recommended measures for strengthening and ensuring the safety of Wi-Fi networks against malicious actors. © 2023 IEEE.

关键词： Evaluation Measures Performance Recommended Strengthening

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Effect of Stopwords and Stemming Techniques in Urdu IR

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SN computer science 2023年第5期4卷 547页

作者： Sahu, Siba Sankar Dutta, Debrup Pal, Sukomal Rasheed, Imran Department of Computer Science and Engineering Indian Institute of Technology (BHU) Uttar Pradesh Varanasi 221005 India Department of Computer Science and Engineering International Institute of Information Technology Odisha Bhubaneswar 751003 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur 522502 India

This paper explores and evaluates the effect of different stopword removal and stemming techniques in Urdu IR. The issues are examined from four viewpoints. Is there any performance difference between non-corpus-based and corpus-based stopword removal in Urdu IR? Can corpus-based stopword lists improve performance in Urdu IR? Among the different corpus-based stopword lists, which stopword list gives the best performance in the IR domain? Does language-independent stemmer improve the performance of Urdu IR? Which is the best stemmer for Urdu IR? Whether to use clustering-based (YASS) or fast corpus-based (FCB) or co-occurrence-based (SNS) or graph-based (GRAS) or Trunc-n-based indexing? It was observed that the shorter length of a corpus-based stopword list outperforms a larger length of a non-corpus-based stopword list in the IR domain. Among the different corpus-based stopword lists, Zipf’s law-based stopword list (nidf approach) provides the best performance and improves a mean average precision (MAP) score of 4.9% compared to baseline approaches. During stemming evaluation, we observed that the language-independent stemming techniques improve retrieval performance in Urdu IR. Among the different stemming techniques, the FCB V-1-based stemmer performs best and improves a MAP score of 1.41% compared to the no-stemming approach. The trunc-n-based indexing strategy provides comparable performance to the language-independent stemming approach. In both the stopword removal and stemming strategies, the BB2 retrieval model outperforms other models in the IR domain. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Evaluation information retrieval Stemming Stopword Urdu

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Developing a Convolutional Neural Model for Segmentation of Medical Images 1

Developing a Convolutional Neural Model for Segmentation of ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Arunachalam, M. Kumar, P. Mohan Selvam, L. Kumar, S. Senthil Arunkumar, B. Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India Hindusthan Institute of Technology & Sciences Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Sethu Institute of Technology Department of Computer Science and Engineering Kariapatti India

ISBN: (纸本)9798350359756

Medical image segmentation is a new biomedical image processing method that has made a significant contribution to sustainable health care. It has now become a major study area in the realm of computer vision research. Medical image processing based on deep convolutional neural networks has become a research hotspot due to the fast development of deep learning techniques. An additional cascade component is included to the architecture in this article to better replicate spatial closure tags' direct dependences. It is also utilised for post-segmentation processing, resolving the conflict between classification performance and network intensity and number of mixing times in a typical convolutional net. Numerous state-of-the-art deep recognition models, and a multi-atlas segmentation methodology, are outperformed by our methodology. © 2023 IEEE.

关键词： Image segmentation

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