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Integrating multi-modal insights with transfer learning for detecting metastatic breast cancer (MBC-stage IV) prognostics

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International Journal of Information technology (Singapore) 2025年第1期17卷 637-643页

作者： Kaur, Sugandha Kaur, Manpreet Khanna, Ashish Department of Computer Science Mata Sundri College for Women University of Delhi New Delhi India Department of Computer Science & Technology Manav Rachna University Faridabad India Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Center for Global Health Research Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India

Accurate breast cancer prognosis prediction is crucial for efficient treatment planning and improving patient outcomes. While significant progress has been made in treating primary breast cancer, the development of robust predictive models remains a critical challenge. This study introduces multi-modal prognostic approach for enhanced Metastatic Breast Cancer prediction. Recognizing the limitations of relying solely on uni-modal data, our approach leverages the power of multi-modal datasets. The images are taken from breast ultrasound scans dataset and mammograms are resized into 224 × 224, and finally, data pre-processing steps are used. This study presents a two-stage model: first, a convolutional neural network is used to extract salient features from the multi-modal data. These extracted features are further concatenated for feature-fusion and served as an input for a dual transfer learning hybrid model in the second stage by combining EfficientNetB4 and InceptionV3, thus enabling robust and accurate prognosis prediction. The outputs are flattened and concatenated followed by implementation of Deep_dense layers to learn the weights of the combined models. The proposed hybrid approach effectively categorizes medical images for the prognostics of breast-cancer. Evaluations demonstrate the superior performance of our proposed model, achieving an accuracy of 99.12%, precision of 95%, recall of 93%, and F1 score of 98.12%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Convolutional neural network (CNN) EfficientNetB Feature fusion InceptionV3 Metastatic breast-cancer Multi-modal image data

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An Efficient Fog Computing Platform Through Genetic Algorithm-Based Scheduling 1st

An Efficient Fog Computing Platform Through Genetic Algorith...

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Chauhan, Shivam Swain, Chinmaya Kumar Behera, Lalatendu Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India Department of Computer Science and Engineering SRM University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819981281

The Internet of Things (IoT) has led to the adoption of fog computing for data-intensive applications that require low-latency processing. Fog computing uses distributed nodes near the network edge to reduce delays and save bandwidth. To optimize resource usage and achieve goals in fog computing, task scheduling plays a crucial role. To tackle this challenge, we propose an approach based on genetic algorithms, inspired by genetics and natural selection. We evaluate GA algorithm based on their ability to complete tasks and prioritize them effectively. The results show that the genetic algorithm performs exceptionally well, completing more tasks and handling priorities efficiently. Its adaptability to changing situations enables optimal resource usage and task completion. Our findings underscore the significance of genetic algorithms in fog computing systems. The insights gained from this study contribute to the advancement of fog computing systems, benefiting IoT applications and services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog

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Securing academic certificate verification with blockchain-based algorithmic rules 4

Securing academic certificate verification with blockchain-b...

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4th IEEE International Multidisciplinary Conference on engineering technology, IMCET 2023

作者： Manoj, R. Joshi, Sandeep Manipal Institute of Technology Department of Computer Science and Engineering Manipal India Manipal India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350313826

In this current digital world, academic certificates are digitalized by universities and distributed to students. It is difficult to maintain these certificates and verification of these certificates is difficult and time-consuming. The proposed system will solve this problem of storage of certificates and verification/validation of academic certificates. The certificates are first converted to digital certificates and algorithmic rules are designed to convert certificates to hash. This hash is in blockchain storage. Then verification process can be done by comparing the hashes. Using blockchain technology we propose a scalable and secure model for verifying academic certificates. It has been found that 95% of utilization of CPU and response time in average is 30%. The analysis of the vulnerability of this blockchain decentralized application (Dapp) to security threats and its possible countermeasures is identified. © 2023 IEEE.

关键词： Blockchain

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N-Gram Based Sarcasm Detection for News and Social Media Text Using Hybrid Deep Learning Models

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SN computer science 2024年第1期5卷 163页

作者： Thaokar, Chetana Rout, Jitendra Kumar Rout, Minakhi Ray, Niranjan Kumar School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneshwar India Ramdeobaba College of Engineering and Management Maharashtra Nagpur India Department of Computer Science and Engineering National Institute of Technology Chattisgarh Raipur India

Sarcasm is a type of communication that involves using words with meanings opposite to their literal definition to create humor or mock someone. This form of communication can be confusing as it often uses positive words to express negative feelings, making it difficult for people to understand the speaker’s intention. Detecting sarcasm in text can be challenging as it changes the polarity of the sentence and the difference between the words used and the way it is spoken. As a result, sarcasm detection in news, comments, or tweets on social media poses a challenge for researchers. In this research, various word-level features have been studied to detect sarcasm from three benchmark datasets, which include the creation of an N-gram probability dictionary, negation words, and PoS tags. Different machine learning and hybrid deep learning models have been examined and compared with handcrafted features and also with word embedding features. The results demonstrate a maximum accuracy of 87% using manual features with deep learning models and up to 92% accuracy using RF classifiers. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN LSTM N-gram probability Negation feature Word embedding

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

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computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

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computer Systems science & engineering 2023年第9期46卷 2651-2666页

作者： A.M.Hafiz M.Hassaballah Abdullah Alqahtani Shtwai Alsubai Mohamed Abdel Hameed Department of Electronics&Communication Engineering Institute of TechnologyUniversity of KashmirSrinagarJ&K190006India Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkhrjKSA Department of Computer Science Faculty of Computers and InformationLuxor UniversityLuxorEgypt

With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world *** the scope of this area,various techniques are found in the *** such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each *** learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully *** more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance *** a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL *** uses multiple binary action DQNs having shared state and *** benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based *** approach can potentially be extended to any type of DQN by forming its *** extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent *** proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.

关键词： Deep Q-networks ensemble learning reinforcement learning OpenAI Gym environments

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Solving Secluded Path Problem by Heuristics and Local Search

Solving Secluded Path Problem by Heuristics and Local Search

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Acikalin, Utku Umur Caskurlu, Bugra Abul, Osman Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400716928

Wireless Sensor Networks (WSNs) play a crucial role in various domains, from commercial to military, enabling precise monitoring of environmental and other conditions. However, the transmission of sensitive data in WSNs poses security challenges, particularly concerning eavesdropping attacks. While cryptographic methods offer protection, they often incur high energy costs, making them impractical for battery-powered WSNs. Our problem formulation, based on graph theory, reduces to the Secluded Paths problem for which no polynomial solution yet to known. Motivated by the challenge of reducing the eavesdropping risk through intelligent routing, we propose a scalable optimization approach using heuristics and local search techniques. We present a solution framework, followed by an experimental evaluation to demonstrate its efficacy. This paper contributes to the advancement of secure and efficient data transmission in WSNs. © 2024 ACM.

关键词： Sensitive data

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Design & analysis of Optical Ring Resonator with Circular Cavity as Add-drop Multiplexer 7

Design & analysis of Optical Ring Resonator with Circular Ca...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Patil, Prasenjeet Damodar Bahadure, Nilesh Bhaskarrao Selokar, Pradip Ram Govind Sarate, Gajanan Mit Art Design & Technology University Department of Computer Science and Engineering Maharashtra Pune India Gsfc University Department of Computer Science and Engineering Gujarat Vadodara India Ramdeobaba University Department of Electronics and Communication Engineering Maharashtra Nagpur India Government Polytechnic Department of Electronics Engineering Maharashtra Amaravati India

ISBN: (纸本)9798331527822

From the last decade, there has been immense interest in development of optical components. Optical components can be broadly classified into two types viz. active and passive components. These components are used for purpose of splitting, multiplexing, filtering etc. In order to investigate performance of these components, computationally complex Maxwell's equations are solved either in time or frequency domain. Numerous mathematical methods are developed to solve these equations for analysis of wave propagation inside optical components. The effectiveness of these method depends on the dimensions of the component, wavelength and step size used in the problem. Optical ring resonators are passive components, which are used as channel dropping filters in dense wavelength division multiplexing(DWDM). Filtering performance of ring resonators depends upon various parameters such as size and shape of cavity, coupling length, separation gap from adjacent waveguides etc. In this paper, finite difference beam propagation method(FDBPM) is used to analyze the filtering performance of optical ring resonator with circular cavity. The filtering performance is determined by selecting circular cavities with different diameters and its separation distance for adjacent parallel waveguide. Simulation results shows that maximum optical power can be dropped for circular cavity with smaller diameters with lesser separation gaps, which are consistent with theoretical results. This paper also investigates effect of negative separation gap on filtering performance of ring resonator. © 2024 IEEE.

关键词： Maxwell equations

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A Secure and Efficient Scheme for Mutual Authentication for Integrity of Exchanged Data in IoMT 28

A Secure and Efficient Scheme for Mutual Authentication for ...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Faraji, Mahmoud Reza Shahriari, Hamid Nikooghadam, Mahdi Amirkabir University of Technology Tehran Polytechnic Faculty of Computer Engineering Computer Science Department Tehran Iran

ISBN: (纸本)9798350338195

The emergence of pandemic diseases like Covid-19 in recent years has made it more important for Internet of Medical Things (IoMT) environments to build contact between patients and doctors in order to control their health state. Patients will be able to send their healthcare data to the cloud server of the medical service provider in remote medical environments through sensors connected to their smart devices, such as watches or smartphones. However, patients' worries surrounding their data privacy protection are still present. In order to ensure the security and privacy of patients' healthcare data in remote medical environments, a number of different schemes have been proposed by researchers. However, these schemes have not been able to take all security requirements into account. Consequently, in this study, we have proposed a secure and effective protocol to safeguard the privacy of patients' medical data when it is sent to the server. This protocol entails two components: mutual authentication of the patient and the server of the medical service provider, as well as the integrity of the exchanged data. Also, our scheme satisfies security requirements and is resistant to well-known attacks. Following this, we used the Scyther tool to formally analyze our proposed scheme. The results showed that the scheme is secure, and in the section on performance analysis, we demonstrated that the proposed scheme performs better than comparable schemes. © 2023 IEEE.

关键词： Authentication

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer Systems science & engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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