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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanjina Ahmed Tuly Tanjid Ahammed Shafin Md. Zahid Hasan Md. Hasan Imam Bijoy Mohammad Shamsul Arefin Mohammad Shorif Uddin Department of Computer Science and Engineering Health Informatics Research Lab Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Computer Science and Engineering Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Diabetes mellitus is a chronic condition characterized by insufficient insulin production and the inability to effectively utilize the insulin hormone, leading to elevated blood glucose levels. As diabetes is incurable and persists throughout a patient's lifetime, effective management requires early identification of risk factors to ensure continuous and feasible treatment. This study proposes a unique decision support system based on the Mamdani Fuzzy Inference System (MFIS) to predict diabetes risk. This study utilizes a publicly available diabetes dataset containing 442 data that includes nine key health metrics such as BMI, blood glucose, cholesterol levels, and blood pressure to predict the risk of diabetes. The dataset aims to support early diagnosis and better diabetes management by providing insights into individual risk factors. This resource contributes to the development of more accurate decision-support systems in healthcare. We identify key variables and establish a membership function to assess individual risk. The Fuzzy Inference System (FIS) is well-suited for handling complex and uncertain conditions, making it an effective tool for this application. The advantages of the MFIS include its ability to incorporate expert knowledge through linguistic variables and fuzzy rules. This system demonstrates the practical benefits of MFIS in developing intelligent decision-making tools, particularly in the domain of diabetes risk prediction.

关键词： Fuzzy logic Decision support systems Accuracy Uncertainty Predictive models Blood pressure Diabetes Glucose Insulin Blood

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Co-synthesis of Histopathology Nuclei Image-Label Pairs using a Context-Conditioned Joint Diffusion Model

arXiv

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arXiv 2024年

作者： Min, Seonghui Oh, Hyun-Jic Jeong, Won-Ki College of Informatics Department of Computer Science and Engineering Korea University Seoul Korea Republic of

In multi-class histopathology nuclei analysis tasks, the lack of training data becomes a main bottleneck for the performance of learning-based methods. To tackle this challenge, previous methods have utilized generative models to increase data by generating synthetic samples. However, existing methods often overlook the importance of considering the context of biological tissues (e.g., shape, spatial layout, and tissue type) in the synthetic data. Moreover, while generative models have shown superior performance in synthesizing realistic histopathology images, none of the existing methods are capable of producing image-label pairs at the same time. In this paper, we introduce a novel framework for co-synthesizing histopathology nuclei images and paired semantic labels using a context-conditioned joint diffusion model. We propose conditioning of a diffusion model using nucleus centroid layouts with structure-related text prompts to incorporate spatial and structural context information into the generation targets. Moreover, we enhance the granularity of our synthesized semantic labels by generating instance-wise nuclei labels using distance maps synthesized concurrently in conjunction with the images and semantic labels. We demonstrate the effectiveness of our framework in generating high-quality samples on multi-institutional, multi-organ, and multi-modality datasets. Our synthetic data consistently outperforms existing augmentation methods in the downstream tasks of nuclei segmentation and classification. © 2024, CC BY-NC-SA.

关键词： Tissue

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation 14

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Varshini Devi, I. Oviya, I.R. Raja, Kalpana Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350344837

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Fused Transformers: Fused Information of Arabic long Article for Summarization 6

Fused Transformers: Fused Information of Arabic long Article...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Ezzat, Zeyad Khalfallah, Ayman Khoriba, Ghadad Center for Informatics Science Information Technology and Computer Science School Nile University Giza Egypt Computer and Systems Department Faculty of Engineering Alexandria University Alexandria Egypt Applied Innovation Centre Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt

This paper introduces a novel approach for extending the applicability of pre-trained models to accommodate longer texts. Addressing the inherent limitation of quadratic performance in attention models within transformers, we propose the concept of fused transformers. By integrating new adaptor techniques, we enhance the encoding of lengthy text segments by breaking them into shorter spans. Subsequently, these segments are fused to increase the model's effectiveness in processing extended texts. This fusion mechanism serves to fortify the model's capacity for fine-tuning. Furthermore, we implement a length-based curriculum for expedited training Our experiments yielded 16 Rouge-2 points, representing a doubling of the score achieved by the vanilla fine-tuning method on the newly introduced "Mukhtasar" dataset for summarization. This highlights the effectiveness of managing complex relations among text segments and confirms that our method can outperform conventional training approaches. © 2024 Elsevier B.V.. All rights reserved.

关键词： Distribution transformers

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IoT Enabled Technologies in Smart Farming and Challenges for Adoption

Studies in Big Data

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Studies in Big Data 2021年 99卷 141-164页

作者： Kumar, Rajesh Sinwar, Deepak Pandey, Amit Tadele, Tesfaye Singh, Vijander Raghuwanshi, Ghanshyam Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Department of Computer Science College of Informatics Bule Hora University Bule Hora Ethiopia Department of Information Technology College of Informatics Bule Hora University Bule Hora Ethiopia Department of Computer Science and Engineering Manipal University Jaipur Jaipur India

Technologies are transforming the universe with the help of digitalization, where IoT is leading in digitalization in almost every field such as health care, agriculture, livestock management, smart houses, cities, renewable energy management, cyberspace environment, etc. 5G wireless technology is also playing a vital role in the management of these IoT devices due to its unique network virtualization functionality. Traditional agricultural methods are continuously changing due to the involvement of the emerging technologies. Food security and food safety are the big concerns in the recent years, and it is expected that up to 2050 world population will grow approximately 33% of the total world population. It is foremost required to adopt smart technology in farming that can help in maximizing food production with minimum cost. Smart farming is a key interest of the scientific community to increase the yield production with minimum cost because due to urbanization agriculture land is continuously depleted. Smart farming uses AI/ML techniques, IoT infrastructure devices, computer vision technology, and unmanned aerial or ground vehicles to maximize the yield productions with minimum production cost. The work embodied in this chapter is focused on how smart farming is digitally transforming using emerging technologies such as machine learning, IoT, computer vision, unmanned aerial vehicles. Several key challenges for the adopting smart farming are also discussed for providing sufficient information before actual implementation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

关键词： Internet of things

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Application of Artificial Intelligence for Maternal and Child Disorders in Indonesia: A Review 22nd

Application of Artificial Intelligence for Maternal and Chil...

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22nd Asia Simulation Conference, AsiaSim 2023

作者： Kurnianingtyas, Diva Indriati Muflikhah, Lailil Department of Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang65145 Indonesia

ISBN: (纸本)9789819972395

The development of Artificial Intelligence (AI) technology is used to minimize the risk of maternal disorders during pregnancy. Maternal health needs to be monitored so as not to cause problems during the baby's birth. The purpose of this study is to provide a literature review from 2017 to 2023. The method used is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study is a basis for researchers to help solve maternal health problems using AI. This technology is proven to detect and predict problems during pregnancy, thereby reducing maternal and infant mortality and preventing abnormalities in the development process. In addition, AI can help improve medical personnel's performance by minimizing human error. This study also presents trends in AI problems and methods used. However, the rapid development of AI methods has not provided novelty in solving maternal pregnancy issues because researchers rarely implement them on a wider scope of problems. Furthermore, it needs to be developed with other methods, such as simulation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstetrics

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RMSD Calculations for Comparing Protein Three-Dimensional Structures 10th

RMSD Calculations for Comparing Protein Three-Dimensional S...

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10th International Conference on Numerical Methods and Applications, NMA 2022

作者： Sapundzhi, Fatima Popstoilov, Metodi Lazarova, Meglena Department of Communication and Computer Engineering Faculty of Engineering South-West University "Neofit Rilski" Blagoevgrad2700 Bulgaria Department of Mathematical Modelling and Numerical Methods Faculty of Applied Mathematics and Informatics Technical University of Sofia Sofia1000 Bulgaria

ISBN: (纸本)9783031324116

The root Mean Square Deviation (RMSD) is a popular measure of structural similarity between protein structures in the field of bioinformatics. The RMSD calculations involve alignment and optimal superposition between matched pairs of atoms, searching for the lowest RMSD result for both structures. Among the popular methods for calculating the optimal rotation matrix that minimizes the RMSD are the Kabsch algorithm and the Quaternion algorithm. The aim of this research is to present a simple tool for calculation of the RMSD between pairs of aligned three dimensional structures by applying the methods mentioned above. As an implementation language the object-oriented C# programming language has been chosen. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Proteins

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Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

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computers, Materials & Continua 2022年第8期72卷 2963-2980页

作者： Mariem Ayedi Walaa H.ElAshmawi Esraa Eldesouky First Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia MEDIATRON Lab. SUP’COMCarthage UniversityTunis2083Tunisia Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Faculty of Computer Science Misr International UniversityCairoEgypt

Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network *** intriguing design goal for such networks is to achieve balanced energy and spectral resource *** paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource *** proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of *** experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency ***,the network’s lifetime is ***,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,***,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

关键词： Underground wireless sensor networks resource efficiency chaotic theory crossover algorithm salp swarm algorithm

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Multiclass Imbalance Resampling Techniques for Network Intrusion Detection

Multiclass Imbalance Resampling Techniques for Network Intru...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Zawiyah Saharuna Tohari Ahmad Department of Informatics Institut Teknologi Sepuluh November Surabaya Indonesia Department of Informatics & Computer Engineering Politeknik Negeri Ujung Pandang Makassar Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Security datasets often exhibit significant imbalances that can introduce bias during model training, diminish sensitivity to actual attacks, and lead to a substantial number of false negatives, potentially overlooking real threats. This is particularly evident in the highly skewed distribution of the UNSW-NB18 Bot-IoT dataset. To mitigate these issues, this study proposes implementing either Random Oversampling (ROS) or Synthetic Minority Oversampling (SMOTE) in conjunction with five ensemble algorithms to develop models for predicting intrusions in the Internet of Things networks. The results show that incorporating these methods with ensemble learners significantly improves model accuracy by 1 % to 4 % across the four algorithms compared to their absence. In addition, there were dramatic increases in precision, recall, and F1-score, achieving values between 95% and 100%.

关键词： Training Accuracy Sensitivity Network intrusion detection Predictive models Prediction algorithms Ensemble learning Security Internet of Things Random forests

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Extreme Learning Machine for Parking Slot Recognition in Smart Parking System 8

Extreme Learning Machine for Parking Slot Recognition in Sma...

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8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Rahmat, Romi Fadillah Faza, Sharfina Syahputra, Ilham Nainggolan, Pauzi Ibrahim Sulaiman, Riza Rahman, Mohd Amiruddin Abd Universitas Sumatera Utara Department of Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Sumatera Utara Department of Computer Science Medan Indonesia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Universiti Putra Malaysia Faculty of Science Selangor Malaysia

ISBN: (纸本)9798331531249

In the modern era, technology has become an integral part of human life, particularly in image processing. This advanced technology is now applied to parking areas to identify vacant parking spaces. The application is designed to detect empty parking slots in real time using image data captured by a camera as input. The system then automatically scans for available parking spaces, displaying the results on the interface with green indicators for empty slots and red for occupied ones. The process involves several key stages, such as image pre-processing, image segmentation, and object detection. The system employs the Extreme Learning Machine (ELM) method to enhance its object detection capabilities. This system achieves a high accuracy rate of 94%under well-lit conditions. This novel technology optimizes parking management, providing an effective and dependable method to monitor parking availability in real time. © 2024 IEEE.

关键词： Adversarial machine learning

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