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2nd International Conference on Sustainable Computing and Data Communication systems, ICSCDS 2023

作者： Jayanthi, R. Sundararajan, S. Alam, Tanweer Garg, Neha Singh, Rupesh Roshan Udhayamoorthi, M. University of Madras Vidhya Sagar Women's College Department of Commerce Tamilnadu Chennai India Sns College of Technology Tamilnadu Coimbatore35 India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Lovely Professional University Mittal School of Business Block-14 Jalandhar-Delhi G.T. Road Punjab Phagwara144411 India

ISBN: (纸本)9781665491990

For all parties involved in a logistics system, precise data and smart business operations are essential. However, a hurdle to implementation could be posed by a lack of shared trust. Supply chains experience difficulties as a result of a lack of confidence in data sharing, according to several studies. The degree to which each stakeholder has faith in the data they acquire can have a significant impact on management choices. The processing of bitcoin transactions has made extensive use of blockchain technology. It has recently been shown to be successful in building confidence within the Network of *** building trust between entities who would otherwise have been distrustful of each other's data, blockchain technology permits more seamless and secure data sharing. However, businesses may encounter severe delays and the requirement for significant processing resources if the network is not IoT-optimized. Additionally, Traditional consensus algorithms still have severe limitations on the nodes' ability to agree. Trust is one of the major concerns in blockchain technology and stands as a severe challenge presently. Confidentiality, privacy, data integrity, and scalability analysis can be identified as the main challenges presently. © 2023 IEEE.

关键词： Internet of things

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An Ensemble of Optimal Deep Learning Features for Brain Tumor Classification

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computers, Materials & Continua 2021年第11期69卷 2653-2670页

作者： Ahsan Aziz Muhammad Attique Usman Tariq Yunyoung Nam Muhammad Nazir Chang-Won Jeong Reham R.Mostafa Rasha H.Sakr Department of Computer Science HITEC UniversityTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsanKorea Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Science Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt

Owing to technological developments,Medical image analysis has received considerable attention in the rapid detection and classification of *** brain is an essential organ in *** tumors cause loss of memory,vision,and *** 2020,approximately 18,020 deaths occurred due to brain *** cases can be minimized if a brain tumor is diagnosed at a very early *** vision researchers have introduced several techniques for brain tumor detection and ***,owing to many factors,this is still a challenging *** challenges relate to the tumor size,the shape of a tumor,location of the tumor,selection of important features,among *** this study,we proposed a framework for multimodal brain tumor classification using an ensemble of optimal deep learning *** the proposed framework,initially,a database is normalized in the form of high-grade glioma(HGG)and low-grade glioma(LGG)patients and then two pre-trained deep learning models(ResNet50 and Densenet201)are *** deep learning models were modified and trained using transfer ***,the enhanced ant colony optimization algorithm is proposed for best feature selection from both deep *** selected features are fused using a serial-based approach and classified using a cubic support vector *** experimental process was conducted on the BraTs2019 dataset and achieved accuracies of 87.8%and 84.6%for HGG and LGG,*** comparison is performed using several classification methods,and it shows the significance of our proposed technique.

关键词： Brain tumor data normalization transfer learning features optimization features fusion

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The Impact of Social Media in Marketing on Brand Awareness and Customer Trust in Increasing Purchase Intention

The Impact of Social Media in Marketing on Brand Awareness a...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Erick Fernando Yulius Denny Prabowo Winanti Department of Information Systems Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia Department of Information Systems Universitas Insan Pembangunan Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

In the current digital era, marketing has developed using technology through media to reach consumers. One prominent feature is influencer marketing, where individuals with significant social media influence promote products or services to their audiences. This strategy has become dominant because of its potential to expand brand reach and boost sales. However, there are challenges in ensuring the effectiveness of influencer marketing, particularly in delivering content with sufficient information and entertainment value. Overcoming these challenges is critical to maximizing customer purchase intent. This research explores the influence of information value, entertainment value, brand awareness, and customer trust on customer purchase intentions in influencer marketing. This research used an online survey (google form) with 398 valid respondents used for analysis PLS-SEM with SMART PLS 3.0. The indicator test results are valid and reliable with loading value for each item with a value >0.5, AVE value $\geqslant 0.50$, Cronbach’s alpha value $\geqslant 0.70$ and Composite Reliability (CR) value $\geqslant 0.7$. The findings show that these factors have a significant effect on purchase intention. In emphasizing the importance of influencers delivering educational and entertaining content to increase Brand Awareness in building customer trust, with the consistency of influencers. Overall, providing valuable insights for companies looking to optimize their influencer marketing strategy to increase brand awareness and customer trust, ultimately driving customer purchase intent.

关键词： Surveys Technological innovation Social networking (online) Buildings Entertainment industry Psychology Companies Data models Reliability Testing

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Defensive Auto-Updatable and Adaptable Bot Recommender System (DAABRS): A New Architecture Approach in Cloud Computing systems 6

Defensive Auto-Updatable and Adaptable Bot Recommender Syste...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Khwaldeh, Sufian Mohit, Y. Khushwant, S. Department of Network and Information Security College of lnformation Technology University of Fujairah 1207 United Arab Emirates Department of Information Technology Faculty of Information Technology and Systems The University of Jordan Aqaba77110 Jordan Department of Mathematics University Institute of Sciences Chandigarh University Punjab Mohali140413 India Department of Computer Science & Engineering University Institute of Engineering and Technology M. D. University Rohtak124001 India

ISBN: (纸本)9798350394634

Cloud server data security makes extensive use of data centers and instantly accessible edge systems for possible transactions involving sizable and confidential data. These Edge systems are chosen for rental based on their usefulness in situations where new privacy and security difficulties must be overcome. The use of Edge systems is subject to dynamic mobility, which increases the danger of attacking threats. With this suggested approach, the Defensive Auto-updatable and Adaptable Bot Recommender approach (DAABRS) is used by the Cloud Center to identify the safeguarded Edge System. Cloud Data Centers with Edge systems contain a variety of users and environments, making it simple for attackers to enter anonymously and compromise Edge System privacy. The most effective security measure against increasing risks and assaults for Edge systems that rent more cloud storage is to use Stealth Mode Bots inside of data segments. In addition to anticipating and averting data breaches in server partitioning, tenant systems, edge systems, and containers by integrating stealth mode bots into each. Suggested use cases: Tenants, Edge systems, or Containers for effective security-enhanced Stealth Mode Bots that are activated when a particular data segment is unplugged. Updatable and adaptive bots are used in the suggested strategy to counter Edge strategy assaults. Additionally, it uses Stealth Mode Bots to stop heavily attacked Data Containers, and machine learning will be used to update specifications to identify and prevent further assaults. © 2024 IEEE.

关键词： Containers

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HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques

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IEEE Access 2025年 13卷 94416-94436页

作者： Arshad, Wishal Masood, Tehreem Shahzad, H.M. Ahmed, Hassan A. Ahmed, Syed Hamza Khushi, Hafiz Muhammad Tayyab University of Central Punjab Faculty of Software Engineering and Information Technology Lahore54000 Pakistan Superior University Faculty of Computer Science and Information Technology Lahore54000 Pakistan Superior University Department of Software Engineering Lahore54000 Pakistan Cleveland State University Monte Ahuja College of Business Department of Information Systems ClevelandOH44114 United States

Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches (50x50 pixels) from Paul Mooney's IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on the test set. Validation on BreakHis (97% accuracy) and BACH (90% accuracy) datasets confirm generalizability, though detecting minority IDC(+) cases remains challenging. Low training and test losses underscore reliability. HistoDX empowers pathologists by enhancing diagnostic efficiency and minimizing subjectivity through effective class imbalance mitigation. Its robust performance across diverse datasets like BreakHis and BACH suggests readiness for clinical integration. Future research into advanced augmentation techniques, ensemble models, and whole-slide image analysis could further optimize accuracy, sensitivity, and scalability, paving the way for broader adoption in precision oncology. © 2013 IEEE.

关键词： Diseases

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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IEEE International Conference on Big Data

作者： Krzysztof Hanzel Damian Grzechca Adam Ziebinski Lukas Chruszczyk Artur Janus Department of Telecommunications and Teleinformatics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Distributed Systems and Informatics Devices Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Student of Electrical Engineering Faculty of Electrical Engineering Silesian University of Technology Gliwice Poland

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.

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Chinese Temple shaped slotted monopole UWB patch antenna for Microwave Imaging Applications

Chinese Temple shaped slotted monopole UWB patch antenna for...

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2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021

作者： Talukder, Md Siam Samsuzzaman, Md Hasan, Md Mahedi Islam, Md Tarikul Hossain, Ismail Singh, Mandeep Singh Jit Patuakhali Science and Technology Faculty of Computer Science and Engineering Patuakhali Bangladesh Universiti Kebangsaan Malaysia Dept of Electrical Electronic and Systems Engineering Selangor Malaysia Universiti Kebangsaan Malaysia Selangor Malaysia

ISBN: (纸本)9781665414609

A new Chinese Temple shaped (CTS) slotted monopole UWB patch antenna is introduced in this research for the purposes of microwave imaging applications such as breast imaging. Geometrically the antenna was being comprised of a slotted pentagonal radiator with a diminished slotted ground-plane carved rectangularly. On the patch and ground plane, numerous types of slots were explored. Slotted pentagonal-shape patch and slotted rectangular ground offer significant Gain over 2.4 to 7.8GHz frequency ranges, wide bandwidth, along with comparatively balanced radiation properties. Whole dimension of the prototype is 40×40×1.6mm3 with 0.320λ×0.320λ×0.0128λ electrical dimension at starting frequency 2.4GHz. The antenna has a fractional bandwidth of 105.88% and an operating bandwidth of 5.4 GHz (2.4-7.8GHz). Several parameters are taken into account and optimized to maximize Gain, and the maximum gain value is 6 dBi. Efficiency reaches a maximum of 96.23% with an average of 94.32% within 2.4-7.8 GHz frequency ranges. Being obtainable and affordable, the prototype mounted on 1.6 mm thick FR-4 substrate with such a 4.6 and 0.002 Permeability and loss tangent respectively. Simulation performed through 3D simulator software CST Microwave Studio 2019 and HFSS. May the antenna really be a respectable contender to use in MI applications for its lightweight structure and high Gain. © 2021 IEEE.

关键词： Microstrip antennas

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Collaborative Smart Environmental Monitoring Using Flying Edge Intelligence

Collaborative Smart Environmental Monitoring Using Flying Ed...

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IEEE Conference on Global Communications (GLOBECOM)

作者： T. Tolga Sari Sabtain Ahmad Atakan Aral Gökhan Seçinti Department of Computer Engineering Istanbul Technical University Istanbul Turkey Institute of Information Systems Engineering Vienna University of Technology Vienna Austria Department of Computing Science Umeå University Umeå Sweden Faculty of Computer Science University of Vienna Vienna Austria

Smart environmental monitoring is crucial for public health and ecological balance as it enables us to monitor and react to environmental hazards. However, effective environmental monitoring can be hindered by the lack of infrastructure and high monetary costs. These challenges are even more pronounced in remote areas, where networking and energy sources are often limited or nonexistent. To address these challenges, we utilize UAVs to form a FANET which can provide effective communication infrastructure suitable for environment monitoring. Moreover, we utilize Edge Intelligence at these UAVs to increase the processing speed and reduce the data size that needs to be transmitted. Our results show that, compared to statically placed gateways, our solution is able to attain similar average age of information for monitoring results while also significantly increasing system capacity.

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Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology

Between Generating Noise and Generating Images: Noise in the...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Nati Daniel Eliel Aknin Ariel Larey Yoni Peretz Guy Sela Yael Fisher Yonatan Savir Department of Physiology Biophysics and Systems Biology Faculty of Medicine Haifa Israel Faculty of Industrial Engineering Haifa Israel Faculty of Computer Science Haifa Israel Faculty of Electrical Engineering Haifa Israel Division of Pathology Rambam Health Care Campus Haifa Israel

Artificial intelligence and machine learning techniques have the promise to revolutionize the field of digital pathology. However, these models demand considerable amounts of data, while the availability of unbiased training data is limited. Synthetic images can augment existing datasets, to improve and validate AI algorithms. Yet, controlling the exact distribution of cellular features within them is still challenging. One of the solutions is harnessing conditional generative adversarial networks that take a semantic mask as an input rather than a random noise. Unlike other domains, outlining the exact cellular structure of tissues is hard, and most of the input masks depict regions of cell types. This is also the case for non-small cell lung cancer, the most common type of lung cancer. Deciding whether a patient would receive immunotherapy depends on quantifying regions of stained cells. However, using polygon-based masks introduce inherent artifacts within the synthetic images – due to the mismatch between the polygon size and the single-cell size. In this work, we show that introducing random single-pixel noise with the appropriate spatial frequency into a polygon semantic mask can dramatically improve the quality of the synthetic images. We used our platform to generate synthetic images of immunohistochemistry-treated lung biopsies. We test the quality of the images using a three-fold validation procedure. First, we show that adding the appropriate noise frequency yields 87% of the similarity metrics improvement that is obtained by adding the actual single-cell features. Second, we show that the synthetic images pass the Turing test. Finally, we show that adding these synthetic images to the train set improves AI performance in terms of PD-L1 semantic segmentation performances. Our work suggests a simple and powerful approach for generating synthetic data on demand to unbias limited datasets to improve the algorithms’ accuracy and validate their robustness.

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Revolutionizing Human Assessment: AI-Driven Analysis of SQ, EQ, and IQ

Revolutionizing Human Assessment: AI-Driven Analysis of SQ, ...

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Cyber Resilience (ICCR), International Conference on

作者： Amer Ibrahim Wael Ali Nidal A. Al-Dmour Taher M. Ghazal American University in the Emirates Dubai United Arab Emirates Department of Computer Engineering College of Engineering Mutah University Jordan Computer Science Department Centre for Cyber Physical Systems Faculty of Information Science and Technology Khalifa University Center for Cyber Security Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This study focuses on the estimate and analysis of social quotient (SQ), emotional quotient (EQ), and intellectual quotient (IQ) to investigate the synergy between artificial intelligence (AI) and human competence evaluation. Traditional evaluation procedures confront intrinsic limitations, necessitating a deeper look at AI's ability to revolutionize existing methods. AI's ability to absorb massive quantities of data, comprehend complicated behavioral patterns, and refine the identification of social, emotional, and intellectual qualities opens the door to more nuanced evaluations. This article digs at artificial intelligence AI's crucial position in data collecting, algorithmic processing, and the ethical concerns inherent in this expanding sector. This study intends to explain the progress and consequences of altering our knowledge of human capacities across multiple dimensions by scrutinizing the transformational potential and challenges of incorporating AI into SQ, EQ, and IQ evaluations.

关键词： Ethics Behavioral sciences Appraisal Artificial intelligence Resilience

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