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International Joint Conference on Neural Networks (IJCNN)

作者： Xingpeng Zhang Meilin Gao Guohai Gao Xin Wang Qiuli Wang School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Engineering Research Center for Intelligent Oil & Gas Exploration and Development of Sichuan Province Chengdu China Department of Radiology The First Affiliated Hospital of Army Medical University Chongging China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Camouflaged object detection (COD) aims to accurately recognize targets in intricate environments that blend into the background. Although numerous camouflage object identification techniques have demonstrated effectiveness, they often possess a substantial number of parameters. Therefore, we propose a new lightweight edge-guided multilevel feature fusion camouflaged object detection network, codenamed as LEMFNet. Initially, we adopt a lightweight CNN network model for feature extraction to reduce model complexity. Subsequently, we introduced a neighborhood feature association module (NFAM) to integrate feature information from different stages to obtain complementary feature representations and enhance the overall model performance. Furthermore, to obtain a more complete object structure, we introduce a boundary aggregation module (BAM) to delve into the edge semantics associated with the target and integrate the edge features into the proposed edge-guided aggregation module (EGAM). Experiments on three challenging benchmarks demonstrate our approach, with fewer parameters, achieves comparable or superior performance, effectively balancing resource utilization and accuracy.

关键词： Accuracy Target recognition Image edge detection Semantics Neural networks Object detection Benchmark testing

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Hybrid security scheme for a colored image using DNA and chaotic map

Hybrid security scheme for a colored image using DNA and cha...

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International Conference on Mathematics, Actuarial science, computer science and Statistics (MACS)

作者： Tayyaba Ajaz Humaira Ashraf Ghadah Alwakid Dept. of computer science and software Engineering International Islamic University Islamabad Islamabad Pakistan Department of Computer Science College of computer and information sciences Jouf university Al-Jouf Saudia Arabia

ISBN: (纸本)9781665460729

this paper proposes a color image encryption algorithm based on DNA encoding in conjunction with a 1D logistic map and a high-dimensional chaotic map. The XOR function is used to combine low and high-dimensional chaotic maps, resulting in a cipher image. For image encryption, the paper also proposes a system of confusion and diffusion. Confusion and Diffusion apply to high-dimensional chaotic maps. In a low-dimensional logistic map, the keyspace is smaller, and chaotic sequences are used directly from the chaotic map, which has an impact on the encryption process. Recent image encryption focuses primarily on a gray image scale. We present color image encryption that uses non-RGB color spaces like YCbCr, YIQ, HSV, and L*a*b*. The high connection between RGB color spaces red, green, and blue is commonly used in data storage and handling. RGB color spaces merge Luminance and Chroma channels so color analysis becomes more complex so we use non -RGB spaces. Using the logistic sine map or tent map association, different chaotic systems can be found by random number attacks and dictionary attacks. Gives the security analysis of encryption using DNA encoding and chaotic map and explains that it's vulnerable to plaintext-choice attacks we propose a new and more efficient approach and reduce time complexity then- existing approach and become encryption more secure.

关键词： Image color analysis Chaotic communication DNA Color Mobile communication Encoding Encryption

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Life Expectancy Prediction Based on GDP and Population Size of Bangladesh using Multiple Linear Regression and ANN Model 12

Life Expectancy Prediction Based on GDP and Population Size ...

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12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021

作者： Rubi, Maksuda Akter Bijoy, Md Hasan Imam Bitto, Abu Kowshir Department of General Educational Development Daffodil International University Dhaka1207 Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1207 Bangladesh Department of Software Engineering Daffodil International University Dhaka1207 Bangladesh

ISBN: (纸本)9781728185958

Life Expectancy is one of the most basic significant aspects of natives that can have a significant relationship between the health sector and the financial movement of a country. This paper investigates the correlation between the Gross Domestic Product (GDP) and Population with Life Expectancy (LE) of Bangladesh. To achieve our goal, we used a long period of data from World Data Open Data (WBOD) and Trends Economics from 1960 to 2020. Using a different number of prediction models was applied as a Multiple Linear Regression Model and several Artificial Neural Network (ANN) to evaluate the life expectancy of Bangladeshis people. There are two independent variables (GDP & Population) used to assess the dependent variable as life expectancy. Our applied model indicates GDP can impact life expectancy, and it refers to longer life expectancy for Bangladeshi's people. There is a strong correlation between population size with life expectancy. Among the applied model, the Multiple Linear Regression (MLR) model stands at the top position with 98% accuracy, and another model, the multi-feed forwarded artificial neural network (MFFNN), regarding the combination of (8,1) with two hidden layer reaches 94% accuracy. Our examination alludes to the life expectancy from 2009 to 2026 of Bangladeshis people. This study shows that GDP improves life expectancy and that population has an impact on life expectancy, and it recommended that the research be expanded with more data and machine learning algorithms. © 2021 IEEE.

关键词： Open Data

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Natural Language Processing for Analyzing Electronic Health Records and Clinical Notes in Cancer Research: A Review

arXiv

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arXiv 2024年

作者： Bilal, Muhammad Hamza, Ameer Malik, Nadia Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32611 United States Department of Computer Science Faculty of Computing and IT University of Sargodha Punjab Sargodha40100 Pakistan Department of Software Engineering Faculty of Computing and IT University of Sargodha Punjab Sargodha40100 Pakistan

Objective: This review aims to analyze the application of natural language processing (NLP) techniques in cancer research using electronic health records (EHRs) and clinical notes. This review addresses gaps in the existing literature by providing a broader perspective than previous studies focused on specific cancer types or applications. Methods: A comprehensive literature search was conducted using the Scopus database, identifying 94 relevant studies published between 2019 and 2024. Data extraction included study characteristics, cancer types, NLP methodologies, dataset information, performance metrics, challenges, and future directions. Studies were categorized based on cancer types and NLP applications. Results: The results showed a growing trend in NLP applications for cancer research, with breast, lung, and colorectal cancers being the most studied. Information extraction and text classification emerged as predominant NLP tasks. A shift from rule-based to advanced machine learning techniques, particularly transformer-based models, was observed. The Dataset sizes used in existing studies varied widely. Key challenges included the limited generalizability of proposed solutions and the need for improved integration into clinical workflows. Conclusion: NLP techniques show significant potential in analyzing EHRs and clinical notes for cancer research. However, future work should focus on improving model generalizability, enhancing robustness in handling complex clinical language, and expanding applications to understudied cancer types. Integration of NLP tools into clinical practice and addressing ethical considerations remain crucial for utilizing the full potential of NLP in enhancing cancer diagnosis, treatment, and patient outcomes. © 2024, CC BY.

关键词： Electronic health record

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A Multimodal Interaction System for Speech-Based Autism Intervention in Sinhala-Speaking Sri Lankan Children using the NAO Robot

A Multimodal Interaction System for Speech-Based Autism Inte...

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Advancements in Computing (ICAC), International Conference on

作者： Aparna Jayawardena Chan Sri Manukalpa Heshani Bopage Poorna Panduwawala Koliya Pulasinghe Samantha Rajapakshe Department of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This paper presents a multimodal interaction system designed for speech-based autism intervention in Sinhala-speaking Sri Lankan children, utilizing the NAO robot. The system integrates four components, language content detection, dialog management, voice synthesis, and gesture synthesis, to create an engaging and adaptive therapeutic environment. By leveraging these technologies, the system provides real-time, personalized responses that align with the cultural and linguistic needs of Sinhala-speaking children with autism spectrum disorder (ASD). The NAO robot facilitates natural and supportive interaction through synchronized speech and gestures, aiming to improve communication and social skills in children with ASD. Preliminary evaluations demonstrate the potential of the system to enhance engagement and responsiveness in therapy sessions, marking a significant step forward in developing specialized interventions for autism in non-Western, non-English-speaking contexts.

关键词： Autism Visualization Human-robot interaction Medical treatment Speech recognition Real-time systems Natural language processing Synchronization Speech synthesis Robots

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Network Modeling Approach for the Nonlinear Dynamics of the Osseous Bio-Physics and Bio-Mathematics

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Biophysical Reviews and Letters 2024年第4期19卷 391-408页

作者： Khaled A. Al-Utaibi M. Idrees Sadiq M. Sait S. Iqbal Computer Science and Software Engineering Department University of Ha’il Ha’il Saudi Arabia Department of Mathematics and Statistics The University of Lahore Lahore Pakistan Center for Communications and IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Shahdrah Medical Hospital Lahore 54000 Pakistan

Bone is termed the smart material, and its modeling is of great interest in biomechanics, biochemistry, endocrinology, and oncology. The bone health of cancer patients is directly affected by hormonal imbalances, receptor-mediated tumor-targeted processes, and disturbed bone mineral density. Researchers have used different therapeutic approaches to monitor bone health during and after cancer treatment. This paper describes a reverse process of bone rebuilding after the resorption of bone via cancer treatment. A detailed model is used for hormonal therapy, which leads to the physical changes in trabecular structure. These changes are demonstrated with the aid of artificial neural networks and Petri nets. This paper connects the structural modeling of the bone trabecula with chemical kinetics. The main goal of this study is to provide a PN model of bone metastasis and an analysis of its structural properties. These properties are very helpful in demonstrating the complex dynamics of bone metastasis. Although both ANNs and PNs are well organized in the areas of machine learning and network modeling, neither technique is without limitations. ANNs, for example, are very efficient machine-learning applications, but their utter lack of explanation capabilities classifies them as a “black-box” technique. On the other hand, PNs are an effective modeling technique, but their theory does not include machine learning. This paper provides a hybrid approach to address the two approaches in a novel manner.

关键词： Discrete mathematics crossover biomechanics t -invariants hormonal treatment multi-physics simulations

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Performance Analysis of Cell-Free Massive MIMO Systems with Asynchronous Reception

Performance Analysis of Cell-Free Massive MIMO Systems with ...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Zheng, Jiakang Zhao, Zhuoyi Zhang, Jiayi Cheng, Julian Leung, Victor C. M. Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China The University of British Columbia School of Engineering KelownaBCV1V 1V7 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

ISBN: (纸本)9781665459754

Cell-free (CF) massive multiple-input multiple-output (MIMO) is considered as a promising technology for achieving the ultimate performance limit. However, due to its distributed architecture and low-cost access points (APs), the signals received at user equipments (UEs) are most likely asynchronous. In this paper, we investigate the performance of CF massive MIMO systems with asynchronous reception, including both effects of delay and oscillator phases. Taking into account the imperfect channel state information caused by phase asynchronization and pilot contamination, we obtain novel and closed-form downlink spectral efficiency (SE) expressions with coherent and non-coherent data transmission schemes, respectively. Simulation results show that asynchronous reception destroys the orthogonality of pilots and coherent transmission of data, and thus results in poor system performance. In addition, getting a highly accurate delay phase is substantial for CF massive MIMO systems to achieve coherent transmission gain. Moreover, the oscillator phase of UEs has a larger effect on SE than that of the APs, because the latter can be significantly reduced by increasing the number of antennas. © 2022 IEEE.

关键词： Spectrum efficiency

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An IT/OT Converged Cyber Range Framework Supporting Ensemble of Large Multimodal Models-Human Teaming and RAG Capability for Bigdata Information Retrieval – 6G Perspective

An IT/OT Converged Cyber Range Framework Supporting Ensemble...

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IEEE Conference on Global Communications (GLOBECOM)

作者： M. Abdur Rahman M. Saiful Islam M. Repon Islam M. Shamim Hossain Selwa Al-Hazzaa Cyber Security and Forensic Computing Department University of Prince Mugrin Medina Saudi Arabia UoL Worldwide University of London London United Kingdom Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Department of Software Engineering King Saud University Riyadh Saudi Arabia Digital Innovation Sector King Abdulaziz City for Science and Technology (KACST) Riyadh Saudi Arabia

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In IT/OT converged enterprise networks, the management of multimodal big data has surpassed human capabilities, presenting critical challenges in business decision-making. This paper introduces an innovative IT/OT Converged Cyber Range Framework, leveraging the power of 6G technology alongside an ensemble of large multimodal models (LMMs) equipped with Retrieval Augmented Generation (RAG) capabilities. Our proposed framework excels at parsing and retrieving information from diverse and massive multimodal datasets in near real-time, driven by the integration of LMMs and human interactions in natural language. The framework's advanced configuration with RAG achieved top performance with an average score of 80.5 across key metrics, including an 86 in answer relevancy and an 88 in faithfulness, while effectively reducing hallucinations to a score of 26. By combining 6G's connectivity with advanced LMM capabilities, we significantly improve the efficiency of automated big data processing and information retrieval, thereby transforming decision-making processes in IT/OT environments. The implementation of our framework, validated through real-life big data sources, and the encouraging test results showcase its effectiveness and transformative potential in managing and utilizing big data far beyond traditional human capabilities.

关键词： 6G mobile communication Measurement Retrieval augmented generation Decision making Natural languages Big Data Information retrieval Real-time systems Multimedia communication Global communication

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A universal multiple instance learning framework for whole slide image analysis

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computers in Biology and Medicine 2024年 178卷 108714-108714页

作者： Zhang, Xueqin Liu, Chang Zhu, Huitong Wang, Tianqi Du, Zunguo Ding, Weihong College of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Department of Pathology Huashan Hospital Affiliated to Fudan University Shanghai200040 China Department of Urology Huashan Hospital Affiliated to Fudan University Shanghai200040 China

Background: The emergence of digital whole slide image (WSI) has driven the development of computational pathology. However, obtaining patch-level annotations is challenging and time-consuming due to the high resolution of WSI, which limits the applicability of fully supervised methods. We aim to address the challenges related to patch-level annotations. Methods: We propose a universal framework for weakly supervised WSI analysis based on Multiple Instance Learning (MIL). To achieve effective aggregation of instance features, we design a feature aggregation module from multiple dimensions by considering feature distribution, instances correlation and instance-level evaluation. First, we implement instance-level standardization layer and deep projection unit to improve the separation of instances in the feature space. Then, a self-attention mechanism is employed to explore dependencies between instances. Additionally, an instance-level pseudo-label evaluation method is introduced to enhance the available information during the weak supervision process. Finally, a bag-level classifier is used to obtain preliminary WSI classification results. To achieve even more accurate WSI label predictions, we have designed a key instance selection module that strengthens the learning of local features for instances. Combining the results from both modules leads to an improvement in WSI prediction accuracy. Results: Experiments conducted on Camelyon16, TCGA-NSCLC, SICAPv2, PANDA and classical MIL benchmark datasets demonstrate that our proposed method achieves a competitive performance compared to some recent methods, with maximum improvement of 14.6 % in terms of classification accuracy. Conclusion: Our method can improve the classification accuracy of whole slide images in a weakly supervised way, and more accurately detect lesion areas. © 2024 Elsevier Ltd

关键词： Image classification

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Explaining the Applicability of the Stimuli-Organism-Response (SOR) Framework in UX Processes for Static and Dynamic Contexts

Explaining the Applicability of the Stimuli-Organism-Respons...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Emmanuel O.C. Mkpojiogu Obianuju E. Okeke-Uzodike Monday Jubrin Abdullahi Universiti Utara Malaysia Sintok Malaysia Department of Software Engineering Veritas University Abuja Nigeria Department of Applied Management Durban University of Technology Pietermaritzburg South Africa Department of Computer Science Airforce Institute of Technology Kaduna Kaduna State Nigeria West Africa

The implementation of the Stimuli-Organism-Response (SOR) framework introduced by Mehrabian and Russell in 1974 is described in this paper. The framework as an environmental psychology framework specifies that the organism of a person who interacts with a specified external object is influenced by specified external environmental factors. These factors make the individual to respond in some particular way by either accepting or avoiding the objects. With respect to user experience (UX), the external interactive objects are any interactive artifact which a user interacts with in certain specified context. The external environmental factors are design factors or quality attributes that impact or activate (arouse) the user’s organism. The organism is the user’s being that is influenced by external stimulating factors. The influenced organism responds by either approaching the object (interactive application) in delight; satisfaction and pleasure or avoiding the interactive product in disgust, frustration, and pain. Notwithstanding the benefit of this framework, the application is rarely employed in the context of UX. This study seeks to flag off awareness on how applicable the framework is to UX research.

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