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Topological Evaluation of Certain computer Networks by Contraharmonic-Quadratic Indices

computers, Materials & Continua 2023年第2期74卷 3795-3810页

作者： Ahmed M.Alghamdi Khalid Hamid Muhammad Waseem Iqbal M.Usman Ashraf Abdullah Alshahrani Adel Alshamrani Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and Engineering at University of JeddahJeddah21493Saudi Arabia

In various fields,different networks are used,most of the time not of a single kind;but rather a mix of at least two *** kinds of networks are called bridge networks which are utilized in interconnection networks of PC,portable networks,spine of internet,networks engaged with advanced mechanics,power generation interconnection,bio-informatics and substance intensify *** number that can be entirely calculated by a graph is called graph *** mathematical graph invariants have been portrayed and utilized for connection investigation during the latest twenty ***,no trustworthy evaluation has been embraced to pick,how much these invariants are associated with a network graph or subatomic *** this paper,it will discuss three unmistakable varieties of bridge networks with an incredible capacity of assumption in the field of computer science,chemistry,physics,drug industry,informatics and arithmetic in setting with physical and manufactured developments and networks,since Contraharmonic-quadratic invariants(CQIs)are recently presented and have different figure qualities for different varieties of bridge graphs or *** study settled the geography of bridge graphs/networks of three novel sorts with two kinds of CQI and Quadratic-Contraharmonic Indices(QCIs).The deduced results can be used for the modeling of the above-mentioned networks.

关键词： Bridge networks invariants Quadratic-Contraharmonic Indices maple network graph molecular graph

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Detection of Acute Lymphoblastic Leukemia Using a Novel Bone Marrow Image Segmentation

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Tsinghua science and Technology 2025年第2期30卷 610-623页

作者： M.Anline Rejula Ben M Jebin Ravi Selvakumar S.Amutha Eberlein George Department of Computer Applications-PG Scott Christian College(Autonomous)Nagercoil 629003India Department of Computer Science Malankara Catholic CollegeMariagiri 629153India School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Pediatrics Government Medical College and HospitalGovernment of Tamil NaduNamakkal 637001India

In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm *** is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous *** address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering *** methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise ***,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency *** allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of *** classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted *** enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic *** innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved *** extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification *** segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),***(97%),***(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.

关键词： Adaptive Neuro-Fuzzy Inference System(ANFIS) blood cells segmentation leukemia classification

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Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions

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ACM Computing Surveys 2025年第8期57卷 1-43页

作者： Ahmad Jan, Mian Adil, Muhammad Brik, Bouziane Harous, Saad Abbas, Sohail Department of Computer Science University of Sharjah Sharjah27272 United Arab Emirates Department of Computer Science and Engineering University at Buffalo BuffaloNY United States

Intelligent Transportation Systems (ITS) generate massive amounts of Big Data through both sensory and non-sensory platforms. The data support batch processing as well as stream processing, which are essential for reliable operations on the roads and connected vehicles in ITS. Despite the immense potential of Big Data intelligence in ITS, autonomous vehicles are largely confined to testing and trial phases. The research community is working tirelessly to improve the reliability of ITS by designing new protocols, standards, and connectivity paradigms. In the recent past, several surveys have been conducted that focus on Big Data Intelligence for ITS, yet none of them have comprehensively addressed the fundamental challenges hindering the widespread adoption of autonomous vehicles on the roads. Our survey aims to help readers better understand the technological advancements by delving deep into Big Data architecture, focusing on data acquisition, data storage, and data visualization. We reviewed sensory and non-sensory platforms for data acquisition, data storage repositories for archival and retrieval of large datasets, and data visualization for presenting the processed data in an interactive and comprehensible format. To this end, we discussed the current research progress by comprehensively covering the literature and highlighting challenges that urgently require the attention of the research community. Based on the concluding remarks, we argued that these challenges hinder the widespread presence of autonomous vehicles on the roads. Understanding these challenges is important for a more informed discussion on the future of self-driven technology. Moreover, we acknowledge that these challenges not only affect individual layers but also impact the functionality of subsequent layers. Finally, we outline our future work that explores how resolving these challenges could enable the realization of innovations such as smart charging systems on the roads and data centers

关键词： Intelligent transportation systems big data sensors storage visualization data centers on the wheels smart computers on the roads

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Deep Learning Model-Based Region Measurement Analysis for Breast Cancer Classification 1

Deep Learning Model-Based Region Measurement Analysis for Br...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meegada, Radhika Bhuyan, Hemanta Kumar Department of Computer Science Engineering Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Andhra Pradesh Guntur India

ISBN: (纸本)9798331518943

The mortality of Breast cancer patients is issued based on lack of identification and treatment, and mammography is a useful tool for early screening. Deep learning-based computer-aided diagnosis (CAD) of mammography can help radiologists make more accurate and objective decisions. However, a lot of the current approaches rely on datasets that have been manually annotated for segmentation. Furthermore, several techniques are less interested in using ROI for image fusion models because of the high image sizes and modest lesion proportions. These flaws raise the application of the model's labour, cost, and computational overhead. Consequently, a network with regional scoring based on the deep location network (DLN) model is suggested. Utilizing pooling modules, DLN is considered a single feature extractor end-to-end mammography image classification approach that locates lesion sites without the need for training. Different deep learning models have been considered to test our proposed model. The experiments are demonstrated on publicly available IN breast and CBIS-DDSM datasets, and the proposed model fared well when compared to earlier cutting-edge techniques for classifying mammogram images. © 2024 IEEE.

关键词： Mammography

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Content Based Image Retrieval: A Study of Approaches and Techniques 4

Content Based Image Retrieval: A Study of Approaches and Tec...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Bano, Mehnaz Matta, Priya Chandel, Shivali Tula's Institute Department of Computer Science and Engineering Dehradun India Tula's Institute Department of Computer Applications Dehradun India

ISBN: (纸本)9798350387490

The Content Based Image Retrieval (CBIR) is basically used to extract images from the collection of images using several image matching and extraction methods. As it is now with the advancement and ongoing consistent technology and negligible cost of the electronic, digital and virtual sensors, a huge collection of image databases is processed by different types of applications. This type of database of images extends the requirement that we should provide effective search methods for the retrieval of images that can fulfill the demands of end-user. The texture, color, or shape of a picture is the most basic feature used to symbolize and index the images. These distinct elements of photographs are retrieved and used as a measure to find image similarities. A search procedure is initiated for relevant images that fulfill the user's information need using a sketch drawing or a query image given by the user that has similar features. The relevance feedback can be used as an optional step to retrieve and enhance the CBIR process. In this paper, after extracting semantic characteristics from photos, an analysis of the content of the images is performed with respect to features connected to the low level. In this paper, an overview of traditional CBIR methods (e.g.: Color histogram-based, texture based and shape based) are discussed. This paper's primary goal is to study the comprehensive details on the various computational techniques used for content-based image retrieval. To further enhance the efficiency of the image retrieval process, their limitations are also addressed. © 2024 IEEE.

关键词： Content based retrieval

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Enhancing English accent identification in automatic speech recognition using spectral features and hybrid CNN-BiLSTM model

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Multimedia Tools and applications 2025年 1-28页

作者： Ahmed, Ghayas Lawaye, Aadil Ahmad Jain, Vishal Chatterjee, Jyotir Moy Mahajan, Shubham Department of Computer Sciences Baba Ghulam Shah Badshah University J&K Rajouri India Department of Computer Science & Engineering Sharda University Noida India Dehradun India Department of Computer Science & Engineering Amity University Haryana India

Automatic Speech Recognition (ASR) has been the regnant research area in the domain of Natural Language Processing for the last few decades. Past years’ advancement provides progress in this area of research. The accent of spoken is the prominent factor affecting the performance of ASR. Accent is the pronunciation of a word in a distinct way which may vary from specimen to specimen depending upon their social class, formality, age, vocal property, gender, geography, and influence of native or other language. Recognizing an accent requires various complex characteristics and features of voice such as voice quality, prosody, and phoneme pronunciation which are difficult to extract and analyze. To solve these difficulties, the researchers focus their attention on spectral representation such as Mel Spectrogram and Mel-Frequency Cepstral Coefficient (MFCC). In this study, we will analyze which features achieve maximum accuracy for the English accent classification task. Here in this work, we perform our experiments on Log Mel filter bank and MFCC features with four different window function. We extract features from raw audio with various windowing function and train a hybrid model of Convolutional Neural Network and Bidirectional Long Short-term Memory network (CNN-BiLSTM) on a custom dataset having nine different accents of English language namely American, Australian, British, Indian (Oriya, Bangla, Telegu, Malayalam), and Welsh. The accuracy of each feature is evaluated and compared. The log Mel filter bank outputs the highest accuracy of 99.75% and 99.91% of training and validation with Han window function while MFCC with Bartlett window function achieves 99.98% of training and 99.33% of validation accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Spectrographs

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Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehensive Study using XGBoost Classifier and Feature Selection Techniques 2

Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehe...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nair, Rekha R. Babu, Tina Kishore, S. Vilashini, V. Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Karnataka Bengaluru India

Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue. © 2024 Elsevier B.V.. All rights reserved.

关键词： Principal component analysis

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MOBES: A MODIFIED BALD EAGLE SEARCH BASED TECHNIQUE FOR OPTIMAL CLUSTERING IN WIRELESS SENSOR NETWORK

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第1期86卷 207-224页

作者： Kusla, Vipan Brar, Gurbinder Singh Department of Computer Science and Application CT University Punjab Ludhiana India Department of Computer Science and Engineering CT University Punjab Ludhiana India

Energy efficiency has emerged as a critical concern in Wireless Sensor Networks (WSN). Sensor nodes deplete their energy faster and die earlier making whole network unstable due to poor clustering in the network. Therefore, Optimal clustering is considered as a crucial technique for enhancing the performance of WSNs, with the objective of optimizing efficacy of active nodes. Metaheuristic optimization is commonly used due to its efficiency, therefore, the Bald Eagle Search (BES) algorithm is used in this research. This paper improves the efficacy of a BES algorithm by avoiding problems with local optima stagnation. By improving the exploration phase, a variant of the BES algorithm called Modified Bald Eagle Search (MoBES) is developed and implemented in WSN. MoBES based technique gives an optimal set of nodes selected as Cluster Heads (CH) to which nearest normal nodes join to form a balanced cluster for clustering process. The objective function used in this technique considers intra-cluster distance, inter-cluster distance and equipoise cluster formation using node degree. The performance of this proposed technique is compared to other common metaheuristic algorithms by varying different node densities and making use of evaluation parameters namely average energy consumption, network residual energy, active nodes, dead nodes, and network lifetime. Based on the findings, the proposed technique, which employs a modified Bald Eagle Search algorithm demonstrates reduction in energy consumption thus improves network lifetime when compared with Artificial Bee Colony (ABC), Biogeography-Based Optimization (BBO), Harmony Search Optimization (HS), Salp Swarm Algorithm (SSA) and Bald Eagle Search Algorithm (BES). © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Sensor nodes

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Crop Disease and Pest Detection using Convolutional Neural Networks (CNN) 5

Crop Disease and Pest Detection using Convolutional Neural N...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Kalaimanivel, S. France, K. Hindustan institute of technology and science Department of Computer Applications Chennai22295014 India Hindustan institute of technology and science Department of Computer Applications Chennai India

ISBN: (纸本)9798350367171

Agriculture is often known as the art and science of nurturing soil. It involves preparing plants and animals for use in products. Agriculture is the process of growing crops and rearing animals for human consumption, fiber production, and other reasons. It is one of the oldest and most important human activities, laying the groundwork for food production and billions of people's lives throughout the world. As technology advances, additional capabilities for crop protection and disease prevention become accessible. Artificial Intelligence (AI) and Machine Learning (ML) algorithms capture features such as crop and soil monitoring, crop maturity detection, autonomous weeding, intelligent crop spraying, pest and disease detection, and more. This study suggests a novel technique for automated crop disease identification by utilizing Convolutional Neural Networks (CNNs) from the field of computer vision. By performing a thorough testing and validation on separate test sets, the proposed methodology outperforms other existing methods in terms of accuracy. The Plant Village collection, maintained by the Centers for Disease Control and Prevention (CDC), includes damaged plant leaf photos and labels. The proposed method has achieved an accuracy of about 99.6%. © 2024 IEEE.

关键词： Convolutional neural networks

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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

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Journal of Communications and Information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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