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Integration of IoT and cloud computing to manage the patient e- prescription 2

Integration of IoT and cloud computing to manage the patient...

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2nd ieee international conference on Advances in computing, Communication and Applied informatics, ACCAI 2023

作者： Chandel, Garima Niwazi Qurashi, Shahazad Bindu Madhavi, N. Madhu, G.C. Singh, Charanjeet Chandigarh University Department of Electronics and Communication Engineering Mohali India Jazan University College of Public Health and Tropical Medicine Department of Health Informatics Jazan Saudi Arabia Andhra Pradesh Vaddeswaram India Mohan Babu University Department of ECE Tirupati India DeenbandhuChhotu Ram University of Science and Technology Electronics and Communication Department Murthal India

ISBN: (纸本)9798350315905

Healthcare data management affects software performance. Current healthcare systems do not use the newest technology to connect with stakeholders. Pharmacists' existing system flexibility could be more efficient. This research proposes an Android and web application system that allows doctors to prescribe to patients using a stylus pen. It will enable patients, receptionists, pharmacists, and admins to interact with the system via their web access. Arduino and E-Health sensors integrate the Internet of Things and cloud computing in the suggested solution. Registered IoT devices help healthcare stakeholders, making the system efficient, user-friendly, and error-free. We present a superior healthcare data management solution and an intuitive system interface. © 2023 ieee.

关键词： Cloud computing

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Probabilistic computing with p-bits: Optimization, Machine Learning and Quantum Simulation

Probabilistic Computing with p-bits: Optimization, Machine L...

引用

ieee international Magnetics conference (INTERMAG)

作者： Camsari, Kerem Y. Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

ISBN: (纸本)9798350362213;9798350362220

The increasing computational demands of the modern AI has been in steady conflict with the slowing down of Moore's Law driven era of electronics. To meet the demands of AI in the new era, augmenting existing complementary metal oxide semiconductor (CMOS) technology with emerging technologies has become a promising new direction. One research thread along this line is the notion of physical computing, where a set of computational tasks are mapped onto a programmable "computer" whose natural physics leads to desired outcomes for computational problems. What is different from traditional computers in this approach is a shift from the focus of general-purpose to domain-specific computing. One example is probabilistic computing with fluctuating probabilistic bits (p-bit) that are conceptually in between bits and qubits. Among many possible implementations, p-bits that use the inherent stochasticity of magnetic tunnel junctions is emerging as one of the most scalable, energy-efficient and manufacture-ready option and their scaled implementation could be of particular use in a variety of applications in combinatorial optimization, machine learning and quantum simulation. Below, I discuss the rapid pace of progress of this field from device, architecture and algorithmic perspectives.

关键词： probabilistic bits probabilistic computation stochastic Magnetic Tunnel Junctions

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Enhancing Protein Language Model With Feature Integration for Anticancer Peptide Prediction

Enhancing Protein Language Model With Feature Integration fo...

引用

2024 ieee-EMBS international conference on Biomedical and Health informatics, BHI 2024

作者： Binte Sayuti, Tiara Natasha Cheng, Shen Ajith, Santhisenan Bin Abdul Samad, Abdul Hadi Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Health Informatics Lab Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350351552

In the fight against cancer, anticancer peptides (ACP) hold promising therapeutic potential due to their selective cytotoxicity and lower side effects compared to traditional treatments. However, identifying novel ACP is challenged by high costs and labor-intensive processes. Protein language models (PLMs), such as ESM-2 and ProtBERT, have revolutionized peptide prediction by leveraging vast datasets to capture complex biological patterns through pre-training. However, they often struggle to accurately model specific biochemical interactions. To address this limitation, we integrated four sequence-based features: amino acid composition (AAC), dipeptide composition (DPC), composition of k-spaced amino acid group pairs (CKS), and k-mer sparse matrix (k-mer) through a cross-attention mechanism. These features infuse biochemical insights that PLM alone may overlook, enabling a more detailed prediction of anti-cancer properties. This integration enhances biochemical insights, improving prediction accuracy by 15.8% for ProtBERT and 2.9% for ESM-2, with ESM-2 achieving the highest accuracy at 77.8%. SHapley Additive exPlanations (SHAP) analysis confirms the importance of these features, demonstrating that incorporating amino acid features into PLMs enhances ACP prediction. © 2024 ieee.

关键词： Prediction models

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Investigating Developer Experiences with UI Components in Flutter: Challenges and Implications

Investigating Developer Experiences with UI Components in Fl...

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2024 international Visualization, informatics and Technology conference, IVIT 2024

作者： Donglan, Zou Bin Darus, Mohamad Yusof Ramli, Azlin Binti College of Computing Informatics and Mathematics Malaysia

ISBN: (纸本)9798350354447

The rapid proliferation of smartphones, particularly those using the Android operating system, has positioned Android as the leading platform in the mobile market. With the advent of various mobile app development frameworks such as Ionic, Flutter, Corona SDK, and Xamarin, developers face the challenge of selecting the optimal framework for their projects. This study focuses on the Flutter framework, a cross-platform development tool by Google, exploring developers' experiences with User interface (UI) construction, specifically in handling common components such as network images and animations. A comprehensive survey was conducted, targeting Flutter developers from diverse backgrounds, including engineers, university students, and instructors. The survey aimed to identify the challenges associated with using Flutter's NetworkImage feature and Animation component. Results indicate significant issues with performance, network connectivity, image quality, dependence on third-party libraries, cache management, and traffic consumption for network images. Similarly, animations in Flutter pose challenges related to memory insufficiency, varying effects across different devices, and performance impacts on user experience. The findings highlight the necessity for enhanced support and innovative solutions within the Flutter framework to address these limitations. This study serves as a resource for developers to better understand and navigate the complexities of Flutter, and it underscores the importance of further research to improve mobile application development technologies. Future investigations should broaden the scope to include more diverse application cases and larger sample sizes to build on these initial insights. © 2024 ieee.

关键词： Cache memory

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Improving computing Education through a Holistic Learning Framework: A Focus Group Study

Improving Computing Education through a Holistic Learning Fr...

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ieee international conference on Teaching, Assessment and Learning for Engineering (ieee TALE)

作者： Modi, Bansri Amish Cain, Andrew Wood-Bradley, Guy Tubino, Laura Renzella, Jake Deakin Univ Melbourne Vic Australia Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9781665453318

Developing computing education curricula and methods that align with university-wide learning objectives can be challenging. The goal is to ensure that students are engaged and have agency over their learning and that the curriculum is designed to enhance their formative growth to prepare them for the industry. To address this challenge, it is important to understand the underlying motives and learning patterns of both the course structure and the individual students. The Task-Oriented Portfolio Assessment (TOPA) model, along with software tools, enhances student success in computing education. TOPA employs frequent formative feedback, delayed summative grading, and outcomes-based assessment, fostering ongoing dialogue between students and staff. Recent changes introduce focus into the model, enhancing formative feedback and learning outcomes. Focus directs student attention and guides tutor feedback, addressing the challenge of understanding course structures and individual student motivations. In this paper, we describe progress through staff focus groups in a university computing department. Findings from sessions with course directors and team members identify key learning objectives, course themes, and skills. We explore course infrastructure, unit sequencing, and their alignment with the graduate course and unit learning outcomes. We also conceptualize tracking student progress and achievements across units through assessments and feedback. Results suggest that incorporating a focus framework with traceability techniques encourages students to see course units as interconnected, improving course completion and graduate employability. Challenges include rubric support, feedback consistency, and potential resistance from tutors or students. Further research is needed to validate effectiveness across institutions.

关键词： Formative Feedback Assessment Learning Outcomes Constructive Alignment

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Intrusion Detection Systems to avoid Cyberattack using Machine Learning 7

Intrusion Detection Systems to avoid Cyberattack using Machi...

引用

7th international conference on Contemporary computing and informatics, IC3I 2024

作者： Paithankar, Om Nandkumar Haider, Armaan Mohammed Suman, Preetam Padhy, Sasmita Siddiqui, Masood H Dash, Sachikanta Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh Bhopal India University of Lucknow Department of Statistics Uttar Pradesh Lucknow India Giet University Department of Cse Odisha Gunupur India

ISBN: (纸本)9798350350067

Machine learning brings another perspective into intrusion detection systems. Conventional techniques stumble at the pace of fast-evolving cyber threats, so we delve into machine learning-based intrusion detection systems. In this paper, we survey different datasets using different models: supervised, unsupervised, and hybrid. The work presents feature selection, model optimization, and performance metrics. This paper handles well-known challenges connected with imbalanced data and risks of overfitting. Ensembling and deep learning realize stunning improvements over the traditional approaches. These findings offer in-depth views of practical ways in which machine learning can be integrated into network security with a clear way forward toward real adaptive resilient intrusion detection, underpinning the transformative role of ML in hardening defenses within the cyber domain against sophisticated attacks. © 2024 ieee.

关键词： Adversarial machine learning

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Event-Triggered Switched Observers for Networked Distributed Multi-Agent Systems via Cloud computing 6

Event-Triggered Switched Observers for Networked Distributed...

引用

6th international conference on Electronic Engineering and informatics, EEI 2024

作者： Zhang, Chen Ren, Hongru Ma, Hui Zhou, Qi School of Automation Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798350353594

This paper designs event-triggered switched observers for the networked distributed multi-agent systems based on the cloud computing systems. Firstly, a novel cloud computing system architecture is designed, in which each group of agents can interact with each others through distributed cloud nodes, and each cloud node also serves as an event-triggered switched observer to observe the topologies of the systems. Secondly, event-triggered switched observers are designed, which can assist distributed agents in accurately obtaining information from the leader, even with continuous switching topologies. Then, through stability analysis, we demonstrate the effectiveness of the observers. Finally, we prove the feasibility of the observers through simulation. © 2024 ieee.

关键词： Distributed cloud

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Deep Reinforcement Learning in Stock Trading: Evaluating DDPG and DQN Strategies 6

Deep Reinforcement Learning in Stock Trading: Evaluating DDP...

引用

6th ieee international conference on Emerging Smart computing and informatics, ESCI 2024

作者： Kodurupaka, Nithin Basavadeepthi, H.M. Pecheti, Shiva Teja Amudha, J. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350306613

This study presents a comparative analysis of the Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithms in the context of stock trading, focusing on historical stock price data of S&P 500 stock index. The primary objective is to evaluate the effectiveness of these RL algorithms in making trading decisions and achieving financial performance relative to a passive buy-And-hold strategy. The assessment revolves around key metrics, with a special emphasis on the buy, hold, and sell indications as essential signals for trading actions. Results indicate that DQN outperforms DDPG, showcasing a smaller total loss during the evaluation period. This suggests that DQN may offer superior capabilities in limiting financial losses and making effective trading decisions compared to DDPG, providing valuable insights into algorithm selection for dynamic trading environments. It is crucial to recognize the significance of buy, hold, and sell indications as key elements in assessing the performance of RL algorithms in stock trading scenarios. © 2024 ieee.

关键词： Algorithmic Trading Buy-And-Hold Strategy DDPG Trading Agent Deep Neural Networks Deep Reinforcement Learning DQN Portfolio Performance Total Return

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Enhancing Literary Exploration: A Machine Learning Approach to Book Recommendations 7

Enhancing Literary Exploration: A Machine Learning Approach ...

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7th international conference on Contemporary computing and informatics, IC3I 2024

作者： Singh, Kritika Gupta, Sumita Dhir, Saru Gambhir, Sapna Amity University Amity School of Engineering & Technology Department of Computer Science & Engineering Uttar Pradesh Noida India Amity University Amity School of Engineering & Technology Department of Information Technology Uttar Pradesh Noida India George Mason University College of Engineering and Computing Department of Computer Science United States

ISBN: (纸本)9798350350067

In today's era of rapid internet expansion, the need for efficient information retrieval tools is paramount. Recommendation systems serve as indispensable instruments, aiding users in navigating vast datasets swiftly and accessing relevant information. This paper presents a novel approach to book recommendation systems, offering readers a quick and intuitive method to discover their next read. Implementations encompass three algorithms: Popularity-based, Contentbased, and Collaborative-based recommendation systems. The primary focus lies in proposing a swift book idea system, facilitating users in promptly discovering fitting reading materials. By harnessing the power of these algorithms, readers can explore a diverse range of book options tailored to their preferences, enhancing their overall reading experience and satisfaction. Through this study, we aim to contribute to the advancement of recommendation systems, providing valuable insights and practical solutions for optimizing the book discovery process in the digital age. © 2024 ieee.

关键词： Collaborative filtering

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A Comparative Study on using Genetic Algorithm with DEAP framework on Different Optimization Problems 7

A Comparative Study on using Genetic Algorithm with DEAP fra...

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7th international conference on Contemporary computing and informatics, IC3I 2024

作者： Aravind Krishna, S.V. Prashanna, R. Midhru Jayan, J. Jeyakumar, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

Evolutionary Algorithms (EAs) are the solution approaches for solving optimization problems using the classical generate-and-test method. These algorithms are widely used by the practitioners for solving the optimization problems and are studied by the researchers for their advancements. Few highlighted advancements in the domain of EAs are distributed EAs, ensemble EAs, modular EAs, and tuning free EAs. Nevertheless, for the practitioners to get the clarity on the working principle of EAs, the literature is lacking with a comprehensive article reporting their performance on different problems. The research study presented in this paper is an attempt to alleviate this shortcoming. This paper aimed at presenting in detail the working principle of Genetic Algorithm (GA) on different problems which are generally used as reference problems for design and analysis of algorithms. The problems considered in the study are one-max problem, 0/1 Knapsack problem and N Queen problem. The experimental setups are carefully designed, for these three problems, and are solved by implementing GAs on the DEAP (Distributed Evolutionary Algorithms in Python) framework. The performance of the GAs is studied based on the quality and speed performance metrics and the inferences are presented in this paper. © 2024 ieee.

关键词： Genetic algorithms

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