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Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features

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computer Modeling in Engineering & sciences 2024年第12期141卷 2375-2397页

作者： Amjad Rehman Muhammad Mujahid Robertas Damasevicius Faten S.Alamri Tanzila Saba Artificial Intelligence&Data Analytics Lab College of Computer&Information Sciences(CCIS)Prince Sultan UniversityRiyadh11586Saudi Arabia Centre of Real Time Computer Systems Kaunas University of TechnologyKaunasLT-51386Lithuania Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

This study aims to develop a computational pathology approach that can properly detect and distinguish histology *** is crucial for histopathological image analysis,as it involves segmenting cell ***,challenges exist,such as determining the boundary region of normal and deformed nuclei and identifying small,irregular nuclei *** learning approaches are currently dominant in digital pathology for nucleus recognition and classification,but their complex features limit their practical use in clinical *** existing studies have limited accuracy,significant processing costs,and a lack of resilience and generalizability across diverse *** proposed the densely convolutional Breast U-shaped Network(BU-NET)framework to overcome the mentioned *** study employs BU-NET’s spatial and channel attention methods to enhance segmentation *** inclusion of residual blocks and skip connections in the BU-NEt architecture enhances the process of extracting features and reconstructing the *** enhances the robustness of training and convergence processes by reducing the occurrence of vanishing *** primary objective of BU-NEt is to enhance the model’s capacity to acquire and analyze more intricate features,all the while preserving an efficient working *** BU-NET experiments demonstrate that the framework achieved 88.7%average accuracy,88.8%F1 score for Multi-Organ Nuclei Segmentation Challenge(MoNuSeg),and 91.2%average accuracy,91.8%average F1 for the triple-negative breast cancer(TNBC)*** framework also achieved 93.92 Area under the ROC Curve(AUC)for *** results demonstrated that the technology surpasses existing techniques in terms of accuracy and effectiveness in ***,it showcases the ability to withstand and recover from different tissue types and diseases,indicating possible uses in medical *** research evaluated the efficacy of the proposed method

关键词： Breast cancer histopathology BU-NET deep learning

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QoS Based Hybrid Swarm Intelligent Intrusion Detection System for Network Security 14

QoS Based Hybrid Swarm Intelligent Intrusion Detection Syste...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Karthikeyan, N. Bhargav, Jajjara Kavitha, M.S. Karthik, S. Sns College of Technology Department of Computer-Science and Engineering Coimbatore India

ISBN: (纸本)9798350335095

The proliferation of Internet usage and the wide range of military applications have prompted scholars to consider the development of intelligent systems that can aid users and applications in obtaining services by providing the necessary quality of service in networks. Certain intelligent techniques are suitable for ensuring security in communication within distributed environments, including but not limited to mobile technology, e-commerce, communication, and managing networks. Multicast routing techniques can assist modern applications utilizing highly efficient data networks in fulfilling QoS requirements. The objective of this methodology is to reduce the network burden during a multicast session. To achieve this objective, it is crucial to minimize the overall expense of the multicast tree or the expenses associated with the links that constitute the multicast tree. We describe a unique approach that leverages Artificial Bee Colony (ABC) operators in conjunction with the Genetic Algorithm (GA) in order to alleviate the restrictions that are caused by the techniques that are currently in use. In addition to this, identification is used across a variety of communication nodes in order to determine the presence of people within the premises. As a consequence of this, security precautions have been taken. © 2023 IEEE.

关键词： Genetic algorithms

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Quantum-Assisted activation for supervised learning in healthcare-based intrusion detection systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 977-984页

作者： Laxminarayana, Nikhil Mishra, Nimish Tiwari, Prayag Garg, Sahil Behera, Bikash K. Farouk, Ahmed Department of Electronics and Communications Engineering Indian Institute of Information Technology West Bengal 741235 India Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal 741235 India Department of Computer Science Aalto University Espoo02150 Finland Ecole de Technologie Superieure MontrealQCH3C 1K3 Canada Pvt. Ltd. WB Mohanpur741246 India Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada83523 Egypt

Intrusion detection systems (IDSs) are amongst the most important automated defense mechanisms in modern industry. It is guarding against many attack vectors, especially in healthcare, where sensitive information (patients medical history, prescriptions, electronic health records, medical bills/debts, and many other sensitive data points) is open to compromise from adversaries. In the big data era, classical machine learning has been applied to train IDS. However, classical IDS tend to be complex: either using several hidden layers susceptible to overfitting on training data or using overly complex architectures such as convolutional neural networks, long-short term memory systems, and recurrent neural networks. This article explored the combination of principles of quantum mechanics and neural networks to train IDS. A hybrid classical-quantum neural architecture is proposed with a quantum-Assisted activation function that successfully captures patterns in the dataset while having less architectural memory footprint than classical solutions. The experimental results are demonstrated on the popular KDD99 dataset while comparing our solution to other classical models. Impact Statement Intrusion detection systems are dynamic defenses against network breach attacks. Lately, machine learning has been leveraged to perform automated intrusion detection. However, classical machine learning needs a large amount of data and overly complex architectures to "learn" patterns from data. In this work, we leverage machine learning concepts to derive a novel quantum activation function that greatly simplifies neural architectural complexity while achieving same level of accuracy and performance. Our architecture is much simpler than state-ofthe-Art based classical systems, simpler to train, and easy to handle. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Network architecture

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Efficient static minkowski clustering for web service aggregation

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International Journal of information technology (Singapore) 2024年 1-13页

作者： Ayfan, Suad kamil Al-Shammary, Dhiah Mahdi, Ahmed M. Ibaida, Ayman Ahmed, Khandakar College of Computer Science and Information Technology University of Al-Qadisiyah Al Diwaniyah Iraq Intelligent Technology Innovation Lab Victoria University Melbourne VIC Australia

The paper presents new design and implementation for Web messages static clustering based on TF-IDF and Minkowski Distance metric. The target of the proposed Minkowski clustering is to empower Web messages aggregators in order to reduce network traffic by aggregating highly similar messages. Web services (W.S.) technology offers an extensive platform for representing, discovering, and calling services in many environments, including Service Oriented Architectures (SOA). The basis of W.S. technology is built upon several XML-based protocols, such as the Simple Object Access Protocol (SOAP), which effectively guarantees W.S. flexibility, transparency, and harmony. There is an increasing demand to enhance the efficiency of online services. It is mainly limited by the over utilization of XML. SOAP communications lead to high network congestion. Furthermore, they cause higher latency and processing delays when compared to alternative technologies. Previous studies have proposed XML clustering techniques to support compression- aggregation models. Technically, aggregation can decrease the overall size the SOAP messages, hence decreasing the needed bandwidth across clients and servers. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Minkowski distance SOAP Static clustering Web services

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Prediction of Lung Disease from Respiratory Sounds Using Convolutional Neural Networks 5th

Prediction of Lung Disease from Respiratory Sounds Using Con...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Rajadevi, R. Devi, E. M. Roopa Dharani, M.K. Logeswaran, K. Dineshkumar, S. Chandru, G. Mohan Department of Information Technology Kongu Engineering College Perundurai India Department of Computer Science and Engineering Kongu Engineering College Perundurai India

ISBN: (纸本)9789819906000

Recently, the pollution in the world is leading into many diseases which are incurable at current situation, and also the treatments are not in the leading position. These ailments have an impact on the tubes (airways) that carry gases like oxygen into and out of the lungs. They frequently obstruct or tighten airways. Airway ailments include chronic, asthma, obstructive pulmonary disease (COPD), and bronchiectasis. People with airway illnesses frequently describe their sensation as "attempting to exhale through a straw". The main aim is to detect the lung diseases that are affected in humans and that are very vulnerable to the human bodies. Currently, a collective system for identifying and recommending lung problems is not easily available to a large number of people, and it is technically implemented by machine learning algorithms. This research makes use of deep learning techniques to learn from the respiratory audio signal of patients. The unwanted noise is removed by means of preprocessing, and it is converted to overlapping frames. The FFT is used to convert the signals into the spectrogram, and it is used for predicting the lung diseases. The convolution neural network is applied to spectrogram with eight hidden layers to predict the COPD, upper respiratory tract infection (URTI), lower respiratory tract information (LRTI), etc. This model provides 95% of accuracy, and also comparison is done with the help of different optimizers like ADAM, SGD, and RMSPROP. This system might be improved by further changing both the preprocessing techniques and fine tuning the parameters. This system detects the lungs diseases in the earlier stage itself, so that the disease may not be affect the whole body. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Systematic Survey on Big Data Analytics and Artificial Intelligence for COVID-19 Containment

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computer Systems science & Engineering 2023年第11期47卷 1793-1817页

作者： Saeed M.Alshahrani Jameel Almalki Waleed Alshehri Rashid Mehmood Marwan Albahar Najlaa Jannah Nayyar Ahmed Khan Department of Computer Science College Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Computer in Al-LithUmm Al-Qura UniversityMakkahSaudi Arabia High-Performance Computing Center King Abdulaziz UniversityJeddahSaudi Arabia

Artificial Intelligence(AI)has gained popularity for the containment of COVID-19 pandemic *** AI techniques provide efficient mechanisms for handling pandemic *** methods,protocols,data sets,and various validation mechanisms empower the users towards proper decision-making and procedures to handle the *** so many tools,there still exist conditions in which AI must go a long *** increase the adaptability and potential of these techniques,a combination of AI and Bigdata is currently gaining *** paper surveys and analyzes the methods within the various computational paradigms used by different researchers and national governments,such as China and South Korea,to fight against this *** process of vaccine development requires multiple medical *** process requires analyzing datasets from different parts of the *** learning and the Internet of Things(IoT)revolutionized the field of disease diagnosis and disease *** accurate observations from different datasets across the world empowered the process of drug development and drug *** overcome the issues generated by the pandemic,using such sophisticated computing paradigms such as AI,Machine Learning(ML),deep learning,Robotics and Bigdata is essential.

关键词： COVID-19 IoT artificial intelligence big data coronavirus deep learning robotics machine learning

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Grocery Purchase through Blockchain System 7

Grocery Purchase through Blockchain System

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Boddu Manoj Rajeswari, G. Thanush, B.V. Krishna, Vamsi Sree, E. Divija Balakrishnan, Arunkumar Sathyabama Institute of Science and Technology School of Computer Science Engineering with Blockchain Technology Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology School of Computer Science Engineering Tamil Nadu Chennai600119 India VIT-AP University School of Computer Science Engineering India Crm Institute of Technology School of Computer Science Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Rangampeta School of Computer Science Engineering with Artificial Intelligence Tirupati517102 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798331527822

The integration of blockchain technology into the grocery purchasing process offers a transformative approach to enhancing transparency, security, and efficiency. This paper presents a comprehensive framework for a blockchain-based grocery system, detailing each step from user registration and identity verification to product listing, smart contract creation, and real-time inventory management. Consumers can place orders with confidence, knowing that payments are securely processed and supply chain logistics are fully transparent. The use of IoT devices ensures quality control during transportation, while smart contracts automate delivery and receipt confirmation. Additionally, the system supports consumer feedback and dispute resolution through immutable records and predefined arbitration mechanisms. This innovative application of blockchain technology aims to revolutionize the grocery sector, fostering trust and accountability among all stakeholders involved. Future research will explore the potential for AI integration and address scalability and interoperability challenges. © 2024 IEEE.

关键词： Smart contract

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Evaluating Neighboring Site Group Queries with Constraint of Budget Range 23

Evaluating Neighboring Site Group Queries with Constraint of...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Huang, Yuan-Ko Lin, Lien-Fa Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

ISBN: (纸本)9798400707735

In recent years, many of the location-based queries have been proposed that consider the proximity of spatial sites to provide users with information. However, most studies focus on a single type of sites and do not consider the cost of visiting the sites. As a result, in this paper we present a novel type of location-based queries, namely the Budget-Range-based neighboring site group query (or BR-based NSGQ for short), to find the groups of sites that are within a distance d of each other and have a total cost not exceeding a budget range [bgtm, bgtM]. The site groups are termed the neighboring site groups within budget range (abbreviated as NSGs-BR). In order to efficiently answer the BR-based NSGQs, we modify the R-tree structure to present the RcC-tree, and then design four checking rules to prune the non-qualifying site groups. We propose a query processing algorithm, namely the BR-based NSGQ algorithm, to find all the NSGs-BR by combing the traversal of the RcC-tree and the four checking rules. Finally, numerous experiments are evaluated to demonstrate the efficiency of the BR-based NSGQ algorithm in terms of the CPU time and the number of node accesses of the RcC-tree. © 2023 ACM.

关键词： Budget control

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Automatic Clustering of User Behaviour Profiles for Web Recommendation System

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Intelligent Automation & Soft Computing 2023年第3期35卷 3365-3384页

作者： S.Sadesh Osamah Ibrahim Khalaf Mohammad Shorfuzzaman Abdulmajeed Alsufyani K.Sangeetha Mueen Uddin Department of Computer Science and Engineering Velalar College of Engineering and TechnologyErode638012Tamil NaduIndia Al-Nahrain Nanorenewable Energy Research Center Al-Nahrain UniversityBaghdad64074Iraq Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia School of Digital Science University Brunei DarussalamJln Tungku LinkGadong BE1410Brunei Darussalam

Web usage mining,content mining,and structure mining comprise the web mining ***-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedﬁltering *** cluster user proﬁle-based clustering process is delayed when it has a low precision *** Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Proﬁle(UBP)model group’s similar user behavior on a dynamic update of *** Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate ***’s Filtering Framework(HFF)is designed toﬁlter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accurateﬁltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining ***-GD decreases the delay time in the end-to-end workﬂow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model deﬁnes higher Classiﬁcation Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

关键词： Data mining web mining process search engine web-page recommendation accuracy

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