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A Survey of Lung Nodules Detection and Classification from CT Scan Images

Computer Systems science & engineering 2024年第6期48卷 1483-1511页

作者： Salman Ahmed Fazli Subhan Mazliham Mohd Su’ud Muhammad Mansoor Alam Adil Waheed Faculty of Computing and Technology IQRA UniversityIslamabad44000Pakistan Faculty of Engineering and Computer Science National University of Modern LanguagesIslamabad44000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad44000Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan

In the contemporary era,the death rate is increasing due to lung ***,technology is continuously enhancing the quality of *** improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung *** paper presented a detailed,systematic review of several identification and categorization techniques for lung *** analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)*** findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer *** paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer ***,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated ***,while accepting the strides formed by CAD systems,the review addressed persistent challenges.

关键词： Lung nodules computed tomography scans lung cancer deep learning

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XOR Encryption based on Index Value Enumeration used in IoT based Healthcare

XOR Encryption based on Index Value Enumeration used in IoT ...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Dendy Jonas Irwan Sembiring Adi Setiawan Danny Manongga Indrastanti Ratna Widiasari Dwi Julianingsih Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Informatics Engineering Satya Wacana Christian University Salatiga Indonesia Faculty of Economics and Business University of Raharja Tangerang Indonesia

The use of healthcare-based IOT becomes something that is unavoidable, especially when a pandemic occurs, where a screening process is needed for a person’s condition. This initial condition can be a determinant of further steps to be taken based on these conditions. Healthcare-based IOT can help make it easier for Health in initial checks where this can be done in remote areas and at a relatively low cost. The use of the Safe Entry Station (SES) can assist in determining whether or not a person is healthy, measurements are made with basic things, namely blood pressure, heart rate, respiratory rate and other things. The results of the test are then sent to the central health institution, where the data will be used for further decision making on the results. For this reason, it is necessary to increase the security of the data sent, to avoid leaks, changes, manipulation and so on. This article discusses the encryption of data sent from Safe Entry Stations to Advanced Health Institutions, the use of XOR-based Encryption, encryption is done with indexvalue formulation of data that is enumerated. The results of this encryption are proven to increase the security of the resulting data from the Safe Entry Station which is sent to the Advanced Health Institute. Testing is done with several tools that are available for free.

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Evaluating the Effectiveness of E-Learning Website Using Electroencephalogram 8th

Evaluating the Effectiveness of E-Learning Website Using Ele...

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8th International Visual informatics Conference, IVIC 2023

作者： Aning, Alberto Baharum, Aslina Mohd Naim, Nur Faraha Mohd Yusop, Nurhafizah Moziyana Darius, Dian Darina Indah Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Computing and Information System School of Engineering and Technology Sunway University 5 Jalan Universiti Selangor Sunway City Malaysia Faculty of Defence Science and Technology National Defence University of Malaysia Kuala Lumpur Malaysia Faculty of Engineering Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur57000 Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9789819973385

Although e-learning technology provides numerous benefits for educators, enticing students to use e-learning services is a challenge, particularly for the e-learning websites of higher education institutions in Malaysia. E-learning websites of Malaysian higher education institutions have few guidelines for user interface design that foster emotional engagement. Due to this problem, there is a significant percentage of student disengagement on e-learning platforms. A visualization pattern as a guideline has been proposed for designing an e-learning GUI website for HEIs in Malaysia. The proposed guideline has been implemented on the prototype website’s graphical user interface (GUI). The effectiveness of the GUI was evaluated using an electroencephalogram (EEG) device and resulted in 65% of the participants being in the ‘Most Effective’ category, with the highest average of most effectiveness standing at 32.6%. The research has demonstrated that the developed guideline increased student engagement with the GUI of the e-learning website prototype. The guideline is intended to assist higher education institutions (HEIs) and website developers and designers in creating e-learning websites that can sustain students’ interest in e-learning over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphical user interfaces

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

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Computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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Investigating the Effects of Color Light on Brain Activity Using NIRS: Implications for Emotion Regulation and Cognitive Function

Investigating the Effects of Color Light on Brain Activity U...

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Biomedical engineering International Conference (BMEiCON)

作者： Watchara Sroykham Keiji Iramina Thanate Angsuwatanakul Yodchanan Wongsawat Department of Health Technology Faculty of Science and Health Technology Navamindradhiraj University Bangkok Thailand Department of Informatics Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan College of Biomedical Engineering Rangsit University Pathum Thani Thailand Department of Biomedical Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand

Light contains different wavelengths of the spectrum that are detected and received by photoreceptor cells (rod and cone cells) in the retina. However, light serves non-visual functions in human perception, such as emotion regulation, cognitive performance, sleep cycle, and hormone production. These functions alter brain activity and oxygen consumption in the brain. Our objective was to investigate the effect of light on brain activity using NIRS. Ten subjects (8 males, 2 females) with normal color vision were exposed to achromatic light (white and gray) or chromatic light (blue, green, yellow, and red) for 20 seconds in random order, followed by 30 seconds of rest (dim light). This stimulation-rest cycle was repeated 4 times (total time = 20.30 minutes). NIRS was used to measure Oxy-Hb, Deoxy-Hb, and Total-Hb levels in the frontal lobe of the brain during stimulation. After light stimulation, participants were assessed using the BRUMS and a color effectiveness test. The results indicated that the z-score of Oxy-Hb significantly altered in response to gray light compared to white light. Red and blue light led to a greater alter in Oxy-Hb than green and yellow light. However, there was no significant alter in Deoxy-Hb during both achromatic and chromatic light exposure. Gray light induced feelings of depression and confusion, while red light affected tension and anger. Red and green light promoted feelings of vigor. Fatigue did not show a significant difference between achromatic and chromatic light. Additionally, long wavelengths had a more positive effect compared to short wavelengths. These findings indicate possible uses in neurotherapeutic interventions for stimulating brain activity in individuals with cognitive impairments using light therapy boxes, as well as in architectural lighting design to impact emotion regulation.

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Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems

arXiv

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arXiv 2024年

作者： Keren, Sarah Essayeh, Chaimaa Albrecht, Stefano V. Morstyn, Thomas Taub Faculty of Computer Science Technion - Israel Institute of Technology Israel Departement of Engineering Nottingham Trent University United Kingdom School of Informatics University of Edinburgh United Kingdom Department of Engineering Science University of Oxford United Kingdom

The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving nature of the network. This survey explores how multi-agent reinforcement learning (MARL) can support the decentralization and decarbonization of energy networks and mitigate the associated challenges. This is achieved by specifying key computational challenges in managing energy networks, reviewing recent research progress on addressing them, and highlighting open challenges that may be addressed using MARL. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm 5

Comparison of Biocode Based Machine Learning and Segmentatio...

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Zariful, Muhammad Wafiq Abd Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Fakhrurroja, Hanif Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Basri, Aida Maryam Universiti Technologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei National Research and Innovation Agency Research Center for Smart Mechatronics Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Universiti Technologi Brunei School of Applied Science and Matematics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350374407

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively. © 2024 IEEE.

关键词： Deep learning

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TWE‐WSD: An effective topical word embedding based word sense disambiguation

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CAAI Transactions on Intelligence technology 2021年第1期6卷 72-79页

作者： Lianyin Jia Jilin Tang Mengjuan Li Jinguo You Jiaman Ding Yinong Chen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunmingChina Library Yunnan Normal UniversityKunmingChina School of Computing Informaticsand Decision SystemsArizona State UniversityTempeArizonaUSA

Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of ***,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are *** order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word *** of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each *** integrating strategies are designed to obtain high quality contextual *** experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.

关键词： embedding Word WSD

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A Novel Inception Deep Learning Model for Mobile AI Stroke Prediction System: AI Research and Industry Perspectives for Connected Health

A Novel Inception Deep Learning Model for Mobile AI Stroke P...

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Computing and informatics (ICCI), International Conference on

作者： Bassant M. Elbagoury Rytis Maskeliuans Marwa Zaghow Nabil Kamel Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Ain Shams University Computer Science and Artificial Intelligence Programs King Salman International University KSIU Department of Multimedia Engineering Faculty of Informatics Kaunas University of Technology Lithuania Sales Business and Services Transformation Dell technologies EMEA Egypt Computer Science and Artificial Intelligence Programs Faculty of Computer Science and Engineering King Salma International university KSIU

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed.

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Ensemble Learning for Modeling Luminance in Plasmonic Nanoellipse for Tunable Spaser 11

Ensemble Learning for Modeling Luminance in Plasmonic Nanoel...

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11th IEEE International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Jamil, Saqib Jamil, Saima Farooq, Waqas Ardabili, Sina Khan, Adnan Daud Khalil, Usman Khan Mosavi, Amir Sarhad University of Science and Information Technology Department of Electrical Engineering Peshawar Pakistan Virtual University of Pakistan Pakistan University of Mohaghegh Ardabili Department of Biosystem Engineering Ardabil Iran US-Pak Center for Advanced Studies in Energy Uet Peshawar Peshawar Pakistan Cecos University Department of Electrical Engineering Peshawar Pakistan Obuda University John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

ISBN: (纸本)9798350350142

in a recent study we provided a theoretical framework for plasmonic coupled elliptical nanostructure capable of producing high-intensity spasers or nanolasers. The nanostructure which consists of a gold film substrate, underwent testing for various light polarizations where we selected a simple elliptical shape and then modified it to explore different geometries. The structure exhibited strong coupled resonance modes, particularly localized surface plasmon (LSP). Through finite-element method (FEM) simulations, we investigated the localized surface plasmon resonance (LSPR) of the system. Results revealed that introducing symmetry-breaking techniques, such as creating a symmetry-broken nano-ellipse (SBE), significantly increased both LSPR peaks and local field intensity (LFI). By selecting appropriate gain media, the nanostructure could support various plasmonic resonance modes across different wavelengths. Moreover, it demonstrated potential for developing a compact nanolaser with a broad tunability range. Adjusting light polarization and making structural modifications led to the observation of Surface Plasmon Amplification by Stimulated Emission of Radiation (SPASER) phenomena. The proposed model, with its substantial LSPR values, shows promise for applications like surface-enhanced Raman spectroscopy and sensing. The application of the data-driven methods in modeling the above phenomena has not been explored yet. Due to the complex nature of the phenomena, it is expected that the data-driven models can provide promising results. Consequently, in this article the primary results of proposing ensemble machine learning in modeling the above phenomenon are presented. In this work random forest (RF) and support vector machine (SVM) were employed for modeling and analysis of outcomes. © 2024 IEEE.

关键词： Surface plasmon resonance

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