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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Yalcin, Kadir Dogan, Ramazan Ozgur Dogan, Hulya Bingol, Ozkan Graduate Education Institute Gumushane University Department of Artificial Intelligence and Intelligent Systems Gumushane Turkey Trabzon University Faculty of Computer and Information Sciences Department of Artificial Intelligence Engineering Trabzon Turkey Karadeniz Technical University Faculty of Engineering Department of Software Engineering Trabzon Turkey Gumushane University Faculty of Eng. and Natural Sciences Department of Software Engineering Gumushane Turkey

ISBN: (纸本)9798331540104

Biometric technologies have been widely adopted in various commercial products, ranging from security systems to personal devices, due to their exceptional reliability and user-friendliness. Among these, palmprint biometrics has gained increasing attention for its advantages, such as ease of use, a larger surface area, and the ability to capture more features compared to other biometric methods. A typical palmprint biometric system involves five key stages: (a) Image Acquisition, (b) Hand Segmentation, (c) Pattern Generation, (d) Feature Extraction, and (e) Verification/Identification. The overall success of the system depends on the effectiveness of each stage, with hand segmentation being crucial for identifying the most relevant features for accurate verification. This study aims to achieve high-accuracy biometric recognition by segmenting hands from images captured in uncontrolled environments with complex backgrounds. Accuracy (ACC), Intersection over Union (IoU), F1-Score, and Dice Coefficient are the four critical metrics used to assess the performance of segmentation techniques. The findings demonstrate that the U-Net + ViT and MA-Net + ResNet34 techniques outperform traditional auto-encoder methods in both quantitative performance metrics and visual analysis, leading to significantly improved hand segmentation for biometric systems. © 2024 IEEE.

关键词： Image acquisition

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Unmasking Fake News with Emotional Cues: A Comparative Study of Sentiment Reasoning Models 4

Unmasking Fake News with Emotional Cues: A Comparative Study...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Kumara, B.T.G.S. Ravikumar, Nirubikaa Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Australia Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka

ISBN: (纸本)9798350384864

Discerning truth from deception requires innovative tools in an era besieged by misinformation. Fake news is a form of misleading information delivered as reliable news. It is mainly made up of intentionally manipulating public opinion, decision bias, and exploiting general credulity. Various supervised learning-based fake news detection and classifications only focus on the semantic values due to their subjectivity in the current context. Sentiment analysis is also a common interpretation mechanism of information mining. This research explores the role of sentiment analysis in the context of fake news detection. Sentiment analysis, a facet of natural language processing, delves into the nuanced sentiments within the text, unveiling hidden narratives and differentiating between authentic reporting and carefully crafted deception. The study employs various sentiment reasoning models, including VADER, TextBlob, and Roberta, focusing on comparative analysis. The VADER model, complemented by a Bag of Words approach, emerges as the most effective, outperforming, compared to TextBlob and RoBERTa. Especially BERT sentiment reasoner model underperformed with uneven distribution in this fake news domain. The best VADER sentiments are taken for the transitions of sequential neural network architectures such as Simple Neural Network (SNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) featuring Long Short-Term Memory (LSTM). The LSTM model captures intricate patterns, dependencies, and contextual information. Evaluation metrics, encompassing accuracy, precision, recall, and F1 score, provide a comprehensive understanding of models' performance. The RNN LSTM performed well compared to the other sequential models. It primely emphasised the multifaceted landscape of sentiment analysis, presenting a robust strategy for combating the proliferation of fake news in an information-abundant age. Combining Sentiment Reasoning in VADER and RNN LSTM presents a rob

关键词： Sentiment analysis

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Categorizing methods for integrating machine learning with executable specifications

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Science China(information Sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of Software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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Blood Oxygen Saturation Estimation from Fingertip Videos Using Residual Convolutional Neural Network 4

Blood Oxygen Saturation Estimation from Fingertip Videos Usi...

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4th International Conference on Electronic information engineering and Computer Communication, EIECC 2024

作者： Yang, Yuting Yu, Jiong Chu, Zheng Feng, Xinlong Xinjiang University College of Software Xinjiang China Xinjiang University School of Information Science and Engineering Xinjiang China Xinjiang University College of Mathematics and Systems Science Xinjiang China

ISBN: (纸本)9798331534622

This paper proposes a novel method for estimating peripheral blood oxygen saturation (SpO2) using RGB signals extracted from fingertip videos captured by a smartphone camera. SpO2 is a critical indicator for assessing blood oxygen levels, which can significantly decrease in respiratory conditions such as asthma, chronic obstructive pulmonary disease (COPD), and COVID-19. Inspired by the photophysiological model, this method employs a convolutional neural network (CNN) with cascaded residual blocks to extract RGB signals from fingertip videos for SpO2 estimation. Compared to facial videos, which are susceptible to motion artifacts and ambient light variations, fingertip video signals are more stable. Experimental results on two public datasets demonstrate that the proposed method effectively estimates SpO2 and outperforms comparable methods. The feasibility and accuracy of SpO2 estimation based on RGB signals were also validated. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Ingredients-based Recipe Recommendation software using Machine Learning 11

Improved Ingredients-based Recipe Recommendation Software us...

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11th IEEE International Conference on Intelligent Computing and information systems, ICICIS 2023

作者： Ashraf, Mariam Tarek, Khadija Fathy, Naglaa Mahmoud, Mariam Emad, Rana Khaled, Khadija Amr, Noran Ain Shams University Faculty of Computer and Information Science Software Engineering Department Cairo Egypt Ain Shams University Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences. © 2023 IEEE.

关键词： Image recognition

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Inclusive Gamification: An Exploratory Study in software Development Enterprises 35

Inclusive Gamification: An Exploratory Study in Software Dev...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Elsalmy, Fayrouz Sherief, Nada Hany Abdelmoez, Walid Information Systems College of Computing and Information Technology Arab Academy for Science and Technology Alexandria Egypt Software engineering College of Computing and Information Technology Arab Academy for Science and Technology Alexandria Egypt

Gamification is the incorporation of game elements into non-game settings. software designers increase user motivation by introducing adequately engaging elements, such as leaderboards and badges, into an existing system. In recent years, few studies have examined the risks associated with gamification inside the business systems sector, especially within software development organizations. Yet, this issue must be addressed more systematically to build gamified solutions that take individual gamification risks into account. This research introduces "Inclusive Gamification" as contextually aware gamification that highlights individual risks. The purpose of this paper is twofold: 1) to identify the gamification risk factors that could cause the emergence of risks in software development organizations;and 2) to develop a list of risks with consideration of various contextual factors, namely tasks, personality type, adopted game element, and organizational cultural aspects. 15 employees from three software development companies participated in an exploratory study in which their perspectives on the negative effects of gamification were collected. As a result, we were able to identify several risk factors and associated risks. Our findings assist software engineers throughout the design process in identifying potential threats. Thus, create gamified solutions that better situate employees at their workplaces. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Personnel

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ODRL: Reinforcement Learning in Priority Scheduling for Running Cost Optimization 29

ODRL: Reinforcement Learning in Priority Scheduling for Runn...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Kuang, Cheng Duan, Meng Lv, Tao Wu, Yingjun Li, Li Wang, Luping Software Technology Co. Ltd Cloud Native Platform Team Suzhou China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou China

ISBN: (纸本)9798350330717

Due to the heterogeneity of workloads and the randomness and complexity of hybrid scheduling environment, How to address the challenge of minimizing data center running cost while ensuring workloads SLA has emerged as a significant research problem. To address this, we propose an Optimal Deep Reinforcement Learning Model for Running Cost Optimization in Hybrid Environments (ODRL). Firstly, we propose Running Cost Model (RCM) that analyzes data center running costs from two perspectives based on different running patterns of the workload and node affinity constraints, minimizing computing node utilization and resource running costs. Subsequently, we propose a Priority-Aware Scheduling Algorithm based on Deep Reinforcement Learning(PASD) that use AHP-TOPSIS to quantify workload characterization, get real-time feedback for environment and update historical experience through prioritized experience replay. Finally, extensive experimental results validate the effectiveness of the proposed model. In comparison to Kube-Scheduler, ODRL achieves a significant 15% improvement in reducing data center running cost. Additionally, there is a 7.8% increase in CPU utilization and a 16.7% increase in memory utilization. © 2023 IEEE.

关键词： Cloud computing Priority scheduling Reinforcement learning Running cost

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Smart Robot: Artificial Intelligent Cleaning Robot with Wifi Boundary Sensing

Smart Robot: Artificial Intelligent Cleaning Robot with Wifi...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Tannous, Gresmarteneanos Ame, Ibrahim Kian, Nafiseh Yousefi Al-Turjman, Fadi Mlimwa, Cliford Peter Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Research Centre for AI and IoT Nicosia Cyprus Research Centre for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Cyprus Department of Computer Engineering Cyprus International University Nicosia Cyprus

ISBN: (纸本)9783031782756

The environment surrounds our daily lives, whether we want to or not. When we have a clean environment, we can enjoy very healthy lifestyles. However, when we have dirty environments, it can cause a myriad of diseases, health risks, and worse, global pandemics like plagues. This became very apparent during the years of the COVID-19 pandemic when we had to sanitize our hands on an hourly basis after touching suspicious items that might have had human contact. This study entails a way to keep our environment clean by picking up trash and items that are left on the floor by other people. A smart robot that uses artificial intelligence to be able to not only move around and understand its surroundings, but also know if there is an object that is identified as trash and needs to be removed from that area. The smart robot is fitted with a high-definition camera for object detection and can see them from 3–4 m. From the image, it will scan the angle direction of the object, change its course, and follow it. After reaching the said location, it will begin its procedure of picking up and storing it in its trash compartment. The robot can be tuned to only clean a certain area and not cross its boundary by comparing fingerprints of registered Wi-Fi RSSIs. This method is most accurate for indoor boundary setting. For outdoors, a GPS method is available to track and bind the robot within a map circumference. Finally, all these metrics and location services can be viewed on a custom-made mobile application that complements the robot's features. What ties all this together is a cloud-based server that feeds the app data from the robot and stores it for future review. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wi-Fi

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Half space object classification via incident angle based fusion of radar and infrared sensors

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Journal of systems engineering and Electronics 2022年第5期33卷 1025-1031页

作者： HE Zhenyu ZHUGE Xiaodong WANG Junxiang YU Shihao XIE Yongjun ZHAO Yuxiong School of Electronic Information and Engineering Beihang UniversityBeijing 100191China Beijing Institute of Electronic System Engineering Beijing 100854China Beijing Institute of Astronautical Systems Engineering Beijing 100076China Shenzhen Institute of Beihang University Shenzhen 518038China School of Software Beihang UniversityBeijing 100191China

In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this *** radar sensors,convolutional operation is introduced into the autoencoder,a“winner-take-all(WTA)”convolutional autoencoder(CAE)is used to improve the recognition rate of the radar high resolution range profile(HRRP).Moreover,different from the free space,the HRRP in half space is more *** order to get closer to the real situation,the half space HRRP is simulated as the *** recognition rate has a growth more than 7%com-pared with the traditional CAE or denoised sparse autoencoder(DSAE).For infrared sensor,a convolutional neural network(CNN)is used for infrared image ***,we com-bine the two results with the Dempster-Shafer(D-S)evidence theory,and the discounting operation is introduced in the fusion to improve the recognition *** recognition rate after fusion has a growth more than 7%compared with a single *** the discounting operation,the accuracy rate has been improved by 1.5%,which validates the effectiveness of the proposed method.

关键词： convolutional autoencoder(CAE) half space high-resolution range profile(HRRP) incident angle based fusion tar-get recognition

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Prediction of the Mechanical Characteristic of Bamboo Using Deep Neural Network

Prediction of the Mechanical Characteristic of Bamboo Using ...

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2023 International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Ramful, Raviduth Casseem, Mohammad Shoaib University of Mauritius Mechanical & Production Engineering Department Réduit Mauritius University of Mauritius Department of Software and Information Systems Réduit Mauritius

ISBN: (纸本)9798350322972

The prediction of the mechanical characteristics of bio-based materials, which are indispensable in engineering-related applications, are restrained by numerous natural-occurring and environmental factors. The aim of this study is to use artificial intelligence (AI) technology to estimate with a high level of accuracy the mechanical characteristics of bamboo materials for use in construction based on their inherent physical characteristics. The biological-inspired technique of deep neural network (DNN) was considered as the specific technology in the major sub-fields of AI and the Adam optimizer was selected as optimization function in the proposed algorithm. The model was trained and conceptualized to predict the mechanical properties of bamboo materials in construction by considering extensive datasets based on the condition assessment and physical properties of the materials in context. A dataset consisting of a total of 600 entries was considered for this study. The proposed model in this study, which is still in its developmental stage, has revealed glimpses of the powerful ability of the DNN model to predict the mechanical characteristics of bio-based materials such as bamboo. © 2023 IEEE.

关键词： Deep neural networks

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