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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Local Clustering and Global Spreading of Receptors for Optimal Spatial Gradient Sensing

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Physical Review Letters 2025年第15期134卷 158401-158401页

作者： Albert Alonso Robert G. Endres Julius B. Kirkegaard Niels Bohr Institute University of Copenhagen Copenhagen Denmark Department of Life Sciences and Centre for Integrative Systems Biology and Bioinformatics Imperial College London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark

Spatial information from cell-surface receptors is crucial for processes that require signal processing and sensing of the environment. Here, we investigate the optimal placement of such receptors through a theoretical model that minimizes uncertainty in gradient estimation. Without requiring a priori knowledge of the physical limits of sensing or biochemical processes, we reproduce the emergence of clusters that closely resemble those observed in real cells. On perfect spherical surfaces, optimally placed receptors spread uniformly. When perturbations break their symmetry, receptors cluster in regions of high curvature, massively reducing estimation uncertainty. This agrees in many scenarios with mechanistic models that minimize elastic preference discrepancies between receptors and cell membranes. We further extend our model to motile receptors responding to cell-shape changes and external fluid flow, demonstrating the biological relevance of our model. Our findings provide a simple and utilitarian explanation for receptor clustering at high-curvature regions when high sensing accuracy is paramount.

关键词： Cell membranes

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Exploring the Feasibility of Transformer Based Models on Question Relatedness 24

Exploring the Feasibility of Transformer Based Models on Que...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shu, Honglin Gao, Pei Yang, Ziwei Li, Chen Wu, Man Hong Kong Polytechnic University Department of Computing Hong Kong Nara Institute of Science and Technology Department of Information Science Nara Japan Kyushu Institute of Technology Department of Bioscience and Bioinformatics Iizuka Japan Keio University Deparment of Information and Computer Science Tokyo Japan

ISBN: (纸本)9798350319934

Professional question answering communities, such as Stack Overflow, are becoming a significant aspect of many intellectual endeavors. As a result, developing a strategy for swiftly locating relevant questions and answers can effectively assist experts in issue solving. Various detection models have been presented to address the problem of question-relatedness prediction, but all are sub-optimal since they cannot effectively capture the long-distance dependency of a long sequence. With the invention of the self-attention mechanism and transformer, a better approach to dealing with question-relatedness prediction has just emerged. The primary objective of this study is to investigate the feasibility of the transformer-based model for question-relatedness problems. We turn the question relatedness problem into a text classification problem and introduce a representative transformer-based model, namely Bidirectional Encoder Representation from Transformers (BERT), to deal with text classification. In our experiment, we show that BERT outperforms both SoftSVM and BiDotLSTM, implying that the transformer-based model has considerable potential to address the challenge in the question-relatedness problem. Furthermore, since we can re-formulate text classification problem into a link prediction problem, we discuss the possibility of incorporating the transformer-based approach and graph representation learning. © 2022 IEEE.

关键词： Forecasting

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Parkinson's Disease Diagnosis from Patients Speech Analysis

Parkinson's Disease Diagnosis from Patients Speech Analysis

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International Conference for Convergence of Technology (I2CT)

作者： Harshitha Reddy T Aryagopal Dumpa Venkata Sai Jagadeesh Anand M Peeta Basa Pati Bhanu Prakash KN Department of Computer Science and Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India Bioinformatics Institute A*STAR Singapore

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Parkinson's disease is a neurological disorder mostly observed in the population over the age of 60 years. Over 8.5 million people world-wide are affected by it. Early diagnosis of the disease helps take preventive and curative measures to enable the affected individuals to lead a normal life. Quality of speech is one of the key indicators for identification of the *** this study, we investigate the effectiveness of multiple machine learning algorithms on various speech related attributes to determine the existence of this disease. Additionally, we identify the crucial features which impact the model's functioning. The study employs Explainable AI (XAI) to investigate the influence of voice features on the research outcomes. The experimental findings showcase a diagnostic accuracy of 95% using Random Forest & XGBoost classifiers. We, using SHAP, identify that 6 features out of a set of 24 provide the best efficiency of diagnosis.

关键词： Personal protective equipment Neurological diseases Analytical models Speech analysis Machine learning algorithms Parkinson's disease Sociology

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Parkinson's Disease Severity Assessment: A Comparative Study & Interpretability Analysis

Parkinson's Disease Severity Assessment: A Comparative Study...

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Emerging Technology (INCET), International Conference for

作者： Utkarsh Tiwari P Reddy Jahanve Siwani Karna Anand M Peeta Basa Pati Bhanu Prakash KN Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Bioinformatics Institute A*STAR Singapore

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Parkinson's disease (PD) is a progressive neurological disorder that mainly affects people over the age of sixty. Currently, there is no such cure for PD. However, Treatments are available to help relieve the symptoms related to PD. A rating tool called MDS-UPDRS III is used to gauge the severity and progression and stages of disease severity are measured by the Hoehn and Yahr (NHY) staging system. This study performs a comparative analysis of the effectiveness of various machine learning models such as AdaBoost, XGBoost, Gradient Boosting Classifier (GBC), Decision Trees (DT), K-Nearest Neighbor (KNN), Logistic regression (LR), and Gaussian Naïve Bayes (GNB) on a dataset containing a variety of PD-related features obtained from 2861 persons. Local Interpretable Model-agnostic Explanations (LIME) is employed to perform the analysis of the strength of each feature toward the outcome prediction for various classification tasks. It is observed that the AdaBoost classifier delivers superior performance of 93.2% accuracy and 84.2% F1-Score for the dataset.

关键词： Analytical models Logistic regression Accuracy Parkinson's disease Predictive models Nearest neighbor methods Speech enhancement

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Assessing the Impact of Vulnerabilities on Confidentiality, Integrity, and Availability in Smart Systems 2

Assessing the Impact of Vulnerabilities on Confidentiality, ...

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2nd International Conference on Informatics, ICI 2023

作者： Minhass, Shubham Chauhan, Ritu Kaur, Harleen Amity University Amity Institute of Information Technology UP India Amity University IoT and Artificial Intelligence Lab Center for Computational Biology and Bioinformatics UP India Jamia Hamdard Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350343830

As Internet of Things (IoT) technologies proliferate and smart devices become more and more integrated into our daily lives and workplaces, it is becoming more and more important to address the complex issues of data security and privacy within smart homes and buildings. Even though the Internet of Things (IoT) has many benefits, such as remote control, real-time monitoring, and sophisticated security features, it also raises serious concerns about how sensitive data is protected. By examining the particular problems associated with protecting sensitive data in the IoT era and guaranteeing the continuous provision of dependable services within smart environments, this research paper seeks to close a significant knowledge gap. In order to do this, the research focuses on the complex data-centric features of smart ecosystems, including data processing, generation, and storage. This paper thoroughly examines the range of current IoT technologies, data management practices, and privacy challenges by using a qualitative research approach. It also offers a thorough examination of the privacy protections and security measures that are now in place in smart buildings and homes. This research focuses primarily on the interface between technology, security, and privacy of data. This work effectively fills in the significant research vacuum by clarifying the specific issues it seeks to solve. It provides useful advice and insights that can help individuals and organizations navigate the complex world of smart technologies, securely store their data, and realize the full potential of these game-changing advancements. © 2023 IEEE.

关键词： Sensitive data

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Artificial Intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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A Role of Wearable Health Technology in Smart Cities 3rd

A Role of Wearable Health Technology in Smart Cities

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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SAMPLING IS AS EASY AS LEARNING THE SCORE: THEORY FOR DIFFUSION MODELS WITH MINIMAL DATA ASSUMPTIONS 11

SAMPLING IS AS EASY AS LEARNING THE SCORE: THEORY FOR DIFFUS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Sitan Chewi, Sinho Li, Jerry Li, Yuanzhi Salim, Adil Zhang, Anru R. Department of EECS University of California Berkeley United States Department of Mathematics Massachusetts Institute of Technology United States Microsoft Research United States Microsoft Research Machine Learning Department Carnegie Mellon University United States Departments of Biostatistics & Bioinformatics Computer Science Mathematics and Statistical Science Duke University United States

We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of large-scale real-world generative models such as DALL·E 2. Our main result is that, assuming accurate score estimates, such SGMs can efficiently sample from essentially any realistic data distribution. In contrast to prior works, our results (1) hold for an L2-accurate score estimate (rather than L∞-accurate);(2) do not require restrictive functional inequality conditions that preclude substantial non-log-concavity;(3) scale polynomially in all relevant problem parameters;and (4) match state-of-the-art complexity guarantees for discretization of the Langevin diffusion, provided that the score error is sufficiently small. We view this as strong theoretical justification for the empirical success of SGMs. We also examine SGMs based on the critically damped Langevin diffusion (CLD). Contrary to conventional wisdom, we provide evidence that the use of the CLD does not reduce the complexity of SGMs. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Diffusion

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Clinical Knowledge and Reasoning Abilities of Large Language Models in Pharmacy: A Comparative Study on the NAPLEX Exam

Clinical Knowledge and Reasoning Abilities of Large Language...

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International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Mirana Angel Anuj Patel Amal Alachkar Pierre Baldi Institute for Genomics and Bioinformatics University of California Irvine Irvine USA Department of Computer Science University of California Irvine Irvine USA School of Pharmacy & Pharmaceutical Sciences University of California Irvine Irvine USA

This study aims to evaluate the capabilities and limitations of three large language models (LLMs)-GPT-3, GPT-4, and Bard, in the field of pharmacy by assessing their reasoning abilities on a sample of the North American Pharmacist Licensure Examination (NAPLEX). Additionally, we explore the potential impacts of LLMs on pharmacy education and practice. To evaluate the LLMs, we utilized the sample of the NAPLEX exam comprising 137 multiple-choice questions. These questions were presented to GPT-3, GPT-4, and Bard through their respective user interfaces, and the answers generated by the LLMs were subsequently compared with the answer key. The results reveal a notable disparity in the performance of the LLMs. GPT-4 emerged as the top performer, accurately answering 78.8% of the questions. This marked a substantial 11% and 27.7% improvement over Bard and GPT-3, respectively. However, when considering questions that required multiple selections, the performance of each LLM decreased significantly. GPT-4, GPT-3, and Bard could only correctly respond to 53.6%, 13.9%, and 21.4% of such questions, respectively. Among the three LLMs evaluated, GPT-4 was the only model capable of passing the NAPLEX exam. Nevertheless, given the continuous evolution of LLMs, it is reasonable to anticipate that future models will effortlessly excel in this context. This highlights the significant potential of LLMs to influence the field of pharmacy. Hence, we must evaluate both the positive and negative implications associated with the integration of LLMs in pharmacy education and practice.

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