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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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Calligraphy Alphabet Perception Using Artificial Intelligence 9

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

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computer Systems science & engineering 2022年第2期40卷 735-747页

作者： K.Sangeetha D.Prabha Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning *** this,most research uses sentiment analysis to track students’*** sentence-level sentiment analysis focuses on the whole sentence *** studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a *** is a need to extract the targets in a given sentence which helps to find the sentiment towards those *** extraction is the subtask of targeted sentiment *** this paper,we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields(CRF).A Parallel fusion neural network model is designed to perform this *** evaluate the model using the Michigan dataset and we build a dataset for target extraction from student *** experimental results show that our proposed fusion model achieves better results compared to baseline models.

关键词： Feedback sentimental analysis deeplearning integrated CRF

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Analyzing energy efficiency of flying objects in Wireless Sensor and Mobile Ad Hoc Networks 5

Analyzing energy efficiency of flying objects in Wireless Se...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Siddiqui, Shams Tabrez Ahmad, Adeel Ahmad, Jorair Haseebuddin, Mohammad Siddiqui, Alighazi Salim, Abu Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia Jazan University College of Engineering and Computer Science Department of Electrical and Electronics Engineering Jazan Saudi Arabia

ISBN: (纸本)9798350351378

The convergence of flying objects, Mobile Ad Hoc Networks (MANETs), and Wireless Sensor Networks (WSNs) has been made feasible by the widespread proliferation of wireless communication technology. This study delves into the historical development and significant achievements of WSNs and MANETs, with a focus on the incorporation of drones and UAVs to enhance network capabilities. Our main goals involve conducting thorough assessments of communication protocols, routing algorithms, and network architectures in the presence of flying objects. Our particular emphasis is on maximizing energy efficiency. Examining the dynamics of flying objects in WSNs and MANETs, this analysis delves into the various challenges and opportunities that arise. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The study explores existing protocols such as LEACH, AODV and OLSR, highlighting their benefits and challenges in flying objects. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability. © 2024 IEEE.

关键词： Wireless sensor networks

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Application of 3D Magnetic Resonance Imaging in the Characterization of Brain Tumors 2

Application of 3D Magnetic Resonance Imaging in the Characte...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Samender Kishore, Lalit Badhoutiya, Arti Sagar, Pramod Kumar Saraswat, Laxmi Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India SRMIST Delhi Ncr Department of Computer Application Modinagar UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India Raj Kumar Goel Institute of Technology Srmist Department of Computer Science & Engineering UP Ghaziabad India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

MRI has revolutionized the analysis and remedy of disorder by using imparting precise and accurate images of soft tissue. This technology has been similarly superior by means of the improvement of 3-D MRI, which permits improved detection and characterization of tumors and improves accuracy of diagnosis and remedy. 3D MRI has been found to be in particular beneficial within the characterization of brain tumors as it offers a greater detailed assessment of tumor size, shape and composition, in comparison to traditional imaging techniques including X-ray or CT. This information can help in identifying the nature of the tumor that may impact its remedy and analysis. Furthermore, 3D MRI is useful for figuring out the chance of malignant invasion, which can provide important information for affected person control. Additionally, the advanced decision of 3-D MRI permits better delineation of areas of healthy and diseased tissue that can assist clinicians in making plans complex surgeries. (3-D) MRI has emerged as an increasingly more critical tool inside the characterization of brain tumors. The three-D imaging affords distinct anatomical visualization of the tumor and it's courting to surrounding systems, allowing for extra effective remedy making plans. Furthermore, the potential to visualize tumor tissue parameters together with edema, necrosis, and perfusion may be used to decide whether a tumor is malignant or no longer. 3D MRI has additionally enabled the evaluation of tumor vascularization, which serves as a vital prognostic indicator. The statistics received from 3-D imaging can then be used to be expecting the recurrence of a tumor following treatment, supporting to manual decisions on comply with-up remedies. The reduction in test time, decrease value, and better resolution of 3-D MRI in contrast to standard x-ray imaging also makes the technology more and more attractive for brain tumor management. © 2024 IEEE.

关键词： Attractive Characterization Superior Traditional

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Recommender Systems in E-Commerce 5

Recommender Systems in E-Commerce

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5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Mohanty, Lopamudra Saraswat, Laxmi Garg, Puneet Lamba, Sonia ABES Engineering College Department of Computer Science Ghaziabad India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

Recommendations as a social process play an important role when people rely on external knowledge to make decisions about the artifact of interest. A recommendation system is an intelligent program that generates a high-quality list of items that a user may be interested in. Today, there are a huge variety of different methods and algorithms for filtering data and providing recommendations. Recommendations can be divided into three main categories: Collaborative Sorting, Content-Based Recommendations and Integrated Recommendations. This paper compares and describes these methods and discusses their limitations by describing the problems plagued by recommendations.E-commerce platform makes easy to sell and buy products on a large customer base. Customers now search on Google instead of directly going certain e-commerce site. E-commerce website helps customers to narrow down their comprehensive ideas and be able to complete the product. © 2022 IEEE.

关键词： Recommender systems

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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

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computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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Routing with Cooperative Nodes Using Improved Learning Approaches

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Intelligent Automation & Soft Computing 2023年第3期35卷 2857-2874页

作者： R.Raja N.Satheesh J.Britto Dennis C.Raghavendra Department of CSIT CVR College of EngineeringTelanganaIndia Department of Computer Science and Engineering St.Martin’s Engineering CollegeSecunderabadTelanganaIndia Department of Computer Science and Engineering Dhanalakshmi Srinivasan UniversityTrichyTamilnaduIndia CSIT Department CVR College of EngineeringHyderabadIndia

In IoT,routing among the cooperative nodes plays an incredible role in fulﬁlling the network requirements and enhancing system *** eva-luation of optimal routing and related routing parameters over the deployed net-work environment is *** research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory(s-LSTM)and Bi-directional Long Short Term Memory(b-LSTM).It is used to hold the routing information and random routing to attain superior *** pro-posed model is trained based on the searching and detection mechanisms to com-pute the packet delivery ratio(PDR),end-to-end(E2E)delay,throughput,*** anticipated s-LSTM and b-LSTM model intends to ensure Quality of Service(QoS)even in changing network *** performance of the proposed b-LSTM and s-LSTM is measured by comparing the signiﬁcance of the model with various prevailing ***,the performance is measured with Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)for mea-suring the error rate of the *** prediction of error rate is made with Learn-ing-based Stochastic Gradient Descent(L-SGD).This gradual gradient descent intends to predict the maximal or minimal error through successive *** simulation is performed in a MATLAB 2020a environment,and the model performance is evaluated with diverse *** anticipated model intends to give superior performance in contrast to prevailing approaches.

关键词： Internet of Things(IoT) stacked long short term memory bi-directional long short term memory error rate stochastic gradient descent

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Collaborative Search With Knowledge Sharing And Summarization 4

Collaborative Search With Knowledge Sharing And Summarizatio...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohan, Venkateshwaran Sivaganesan, D. Balasubramaniam, S. Vikhas, S.G. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Dr Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Pollachi India Psg College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798331540364

A collaborative search engine is used for a group of users who want to search information together for various group activities. This will be time saving as the users can split the work between them. Summarization is the process of condensing a document into a shorter version which will contain the important details about the document. This will be useful to quickly get the main ideas without having to go through the entire document. Our main aim is to combine collaborative search with document summarization so that it will be helpful when users in a group want to quickly read the summary of the documents whenever required. In the collaborative search, the results will be re-ranked based on the user's interaction with the system, so that feedback driven ranking of results is attained and the users in a session will be able to view pages that were even ranked by their co-mates. This re-raking process is done by obtaining the user's interest on a particular page using decision logic and updating it in the priority queue. User's meta-data like page size, click-through rate, time spent and scrolling speed of the document will be calculated and these will be used as parameters for the decision logic to find the interest level of the user. Extractive summarization is implemented with the concept of Maximum Independent Set. The raw text document is first converted into a graph having its sentences as nodes and the edge weight is the similar instances between sentences. Then graph MIS is identified and removed after which the top-n sentences are fetched upon its eigenvector centrality value and the summary is generated. © 2024 IEEE.

关键词： Computation theory

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