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IoT-Based Smart Wearable Devices Using Very Large Scale Integration (VLSI) Technology 1

6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Ashwin, M. Naidu, R. Ch. A. Ramamoorthy, Raghu Kumar, E. Saravana Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Computer Science and Engineering The Oxford College of Engineering Karnataka Bengaluru India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

People’s usage of smart wearable devices and sensors plays a crucial role in VLSI technology. The wearable devices are embedded in clothes, smartwatches, and accessories. The wear gadgets like smart rings, smartwatches, and smart spectacles are associated with human healthcare monitoring, actual period location discovery and portable online games, etc. This paper presents a survey of the recent usage of wearable devices and sensors with VLSI technology. Three key features considered to make user-friendly smart wearable devices: They are security, performance, and deployment cost. Almost all wearable devices are equipped with Bluetooth communication and screen displays like smartwatches and smartphones. The primary goal of this research is to minimize the power consumption for wearable devices. Smart wearable devices are utilized for a wide range of applications like medicine, sports, fitness, business, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wearable sensors

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Effort-aware cross-project just-in-time defect prediction framework for mobile apps

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Frontiers of computer science 2022年第6期16卷 15-29页

作者： Tian CHENG Kunsong ZHAO Song SUN Muhammad MATEEN Junhao WEN School of Big Data and Software Engineering Chongqing UniversityChongqing401331China School of Computer Science Wuhan UniversityWuhan430072China Department of Computer Science Air University Multan CampusMultan60000Pakistan

As the boom of mobile devices,Android mobile apps play an irreplaceable roles in people’s daily life,which have the characteristics of frequent updates involving in many code commits to meet new ***-in-Time(JIT)defect prediction aims to identify whether the commit instances will bring defects into the new release of apps and provides immediate feedback to developers,which is more suitable to mobile *** the within-app defect prediction needs sufficient historical data to label the commit instances,which is inadequate in practice,one alternative method is to use the cross-project *** this work,we propose a novel method,called KAL,for cross-project JIT defect prediction task in the context of Android mobile *** specifically,KAL first transforms the commit instances into a high-dimensional feature space using kernel-based principal component analysis technique to obtain the representative ***,the adversarial learning technique is used to extract the common feature embedding for the model *** conduct experiments on 14 Android mobile apps and employ four effort-aware indicators for performance *** results on 182 cross-project pairs demonstrate that our proposed KAL method obtains better performance than 20 comparative methods.

关键词： kernel-based principal component analysis adversarial learning just-in-time defect prediction cross-project model

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Recent advances in tip-enhanced Raman spectroscopy probe designs

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Nano Research 2023年第4期16卷 5555-5571页

作者： Da Xu Boqun Liang Yaodong Xu Ming Liu Department of Electrical and Computer Engineering University of California-RiversideRiversideCalifornia 92521USA Materials Science and Engineering program University of California-RiversideRiversideCalifornia 92521USA

Tip-enhanced Raman spectroscopy(TERS)imaging is a super-resolution imaging technique that features the merits of both surface-enhanced Raman spectroscopy(SERS)and scanning probe microscopy(SPM),such as the high chemical sensitivity from the former and the nanoscale spatial resolution from the *** advantages make TERS an essential nanospectroscopic characterization technique for chemical analysis,materials science,bio-sensing,*** probes,the most critical factor determining the TERS imaging quality,are expected to provide a highly confined electromagnetic hotspot with a minimized scattering background for the generation of Raman signals with high spatial *** two decades of development,numerous probe design concepts have been proposed and *** review provides a comprehensive overview of the state-of-the-art TERS probe designs,from the working mechanism to the practical *** start with reviewing the recent development of TERS configurations and the corresponding working mechanisms,including the SPM platforms,optical excitation/collection techniques,and probe preparation *** then review the emerging novel TERS probe designs,including the remote-excitation probes,the waveguide-based nanofocusing probes,the metal-coated nanofocusing probes,the nanowire-assisted selective-coupling probes,and the tapered metal-insulator-metal *** discussion focuses on a few critical aspects,including the surface-plasmon-polariton(SPP)hotspot excitation technique,conversion efficiency,working frequency,and *** the end,we review the latest TERS applications and give a perspective on the future of TERS.

关键词： tip-enhanced Raman spectroscopy plasmonics scanning probe microscopy nanoscale imaging nanophotonics nanofocusing

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Navigating Complex Multiclass Classification in High-Dimensional Spaces: A Hybrid Approach 7

Navigating Complex Multiclass Classification in High-Dimensi...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Nemani, Praneeth Sundar Vadali, Venkata Surya Medi, Prathistith Raj Marisetty, Ashish Vollala, Satyanarayana Iiit Naya Raipur Department of Computer Science and Engineering India Iiit Naya Raipur Department of Data Science and Artificial Intelligence India

ISBN: (纸本)9798350343144

Large-scale tabular data classification is a critical task and the complexity arises from the vast amount of structured data generated in these fields, coupled with the challenges of high dimensionality and limited sample sizes. To address these challenges, advanced machine learning algorithms are required to analyze and categorize instances within these datasets effectively. In this work, we propose the usage of a state-of-the-art classifier called XBNet, which combines the strengths of tree-based classifiers and neural networks to tackle large tabular datasets. Our methodology is validated on a dataset with 64 dimensions and 11 classes, showcasing the model's capability to detect patterns and extract relevant features automatically. Furthermore, we employ K-Fold cross-validation to assess the model's performance, achieving an impressive training accuracy of 61.9% and a validation accuracy of 31.7%. These results surpass those of competing algorithms, confirming the superiority of our proposed methodology in handling large-scale tabular data classification tasks. © 2023 IEEE.

关键词： Classification (of information)

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Enhanced Clickbait Detection through Ensemble Machine Learning Techniques

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Procedia computer science 2025年 258卷 599-608页

作者： Neetu Sardana Deepika Varshney Seema Luthra Department of Computer Science & Engineering-Data Science ABES Engineering College Ghaziabad India

Nowadays online news websites are one of the quickest ways to get information. However, the credibility of news from these sources is sometimes questioned. One common problem with online news is the prevalence of clickbait. Clickbait uses exaggerated headlines to lure people to click the suspected link, but the content often disappoints the reader and degrades user experience it may also hamper public emotions. The proposed work aims to examine diverse set of models for clickbait detection. The models are formed by integration of Machine learning (ML) and Ensemble learning methods (EL) with Term Frequency and Inverse Document Frequency (TF-IDF) & Embedding technique. Five ML and three EL are analysed &compared. Random Forest along with TF-IDF gave the best results of 85%. The resultant model shows significant improvements with a minimal false-positives.

关键词： Clickbait Detection Machine Learning text classification NLP Ensemble learning Stacking classifir

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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ASAnalyzer: Attention based Sentiment analyzer for Real-world Sentiment Analysis 20

ASAnalyzer: Attention based Sentiment analyzer for Real-worl...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Hussain, Khadim Azhar, Muhammad Lee, Bumshik Iqbal, Asma Affan, Muhammad Ullah Khan, Sajid Comsats University Islamabad Department of Computer Science Sahiwal Pakistan Hong Kong Shue Yan University Department of Applied Data Science Hong Kong Hong Kong Chosun University Department of Computer Science and Software Engineering Korea Republic of Tsinghua University Department of Computer Science China University of Lakki Marwat Department of Computer Science Pakistan

ISBN: (纸本)9798350395785

In this era of big data, a lot of data is produced in various forms every second through various sources. Text data is one of those types that is produced mainly through social media like Twitter, Facebook, YouTube comments, WhatsApp, etc. To know the public's point of view on a specific issue, we can perform sentiment analysis on the text data collected from the above sites. Even though various algorithms have been proposed for sentiment analysis, these algorithms have issues of high time complexity and low context awareness, leading to low classification accuracy. To resolve the above issues, we propose an attention-based sentiment analyzer for real-world sentiment analysis named 'ASAnalyzer', which uses CNN+Attention-Based BiGRU. CNN is used to extract local features from the tweets, and then these features are used by Attention-Based BiGRU to learn the contextual information of the tweets and the long-term dependencies in both directions of the text, which helps to improve accuracy. To validate our algorithm, we used tweet data about the anti-COVID-19 vaccine from Twitter, and the results have shown that our method outperformed other state-of-the-art methods. © 2023 IEEE.

关键词： Classification (of information)

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Managing Vulnerabilities in Software Projects: the Case of NTT data 49

Managing Vulnerabilities in Software Projects: the Case of N...

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49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023

作者： Nocera, Sabato Romano, Simone Francese, Rita Burlon, Riccardo Scanniello, Giuseppe University of Salerno Department of Computer Science Fisciano Italy Ntt Data Salerno Italy

ISBN: (纸本)9798350342352

Background: Software vulnerabilities are flaws in application source code that can be exploited to cause harm, hence companies must devise strategies to manage ***: We want to understand how software vulnerabilities are managed in a big IT (Information Technology) service and consulting company like NTT ***: We conducted a focus group involving six software professionals working at NTT data and analyzed the gathered data through a thematic analysis ***: We found that application security standards are defined based on the needs of the clients (i.e., companies that commissioned NTT data the software to be developed) and the projects' nature (i.e., the development of greenfield projects vs. maintenance of existing ones). Also, to detect software vulnerabilities, SAST (Static Application Security Testing) tools are mainly used;among these, SonarLint and SonarQube appear to be the de-facto standards for NTT data. Finally, not all software vulnerabilities are fixed;for example, the presence of some software vulnerabilities is tolerated by the clients, who take on the responsibility of not removing these ***: It seems that developers and NTT data clients are not averse to securing their code. NTT data follows the application security standards established with their clients. To detect software vulnerabilities, SonarLint and SonarQube appear to be the de-facto standards, so explaining to some extent the increasing attention on these tools by the software engineering research community. © 2023 IEEE.

关键词： Software testing

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Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders

Machine Learning for Mental Health: A Systematic Study of Se...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Nadeem, Muhammad Rashid, Junaid Moon, Hyeonjoon Dosset, Arailym Sejong Uviversity Department of Computer Science and Engineering Seoul Korea Republic of Sejong University Department of Data Science Seoul Korea Republic of

ISBN: (纸本)9798350326413

Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health. © 2023 IEEE.

关键词： Support vector machines

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Smart Services Implementation in Smart Cities: A Comprehensive Review of State-of-the-art Technologies 23

Smart Services Implementation in Smart Cities: A Comprehensi...

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5th International Conference on Management science and Industrial Engineering, MSIE 2023

作者： Arin, Ikrar Adinata Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Indonesia Smart City and Community Innovation Center Institut Teknologi Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Indonesia

ISBN: (纸本)9798400708381

This paper discusses comprehensively the advanced technologies used in the implementation of smart services, which are key elements of a smart city. Smart cities are becoming eco-friendly, livable, and sustainable to deliver essential services to their citizens by leveraging internet and communication technology advancements. Starting from a short literature review about smart cities and smart services, a recent trend in the technology used to implement smart services is collected. Smart service can be defined as a service conveyed using intelligent products that possess awareness and connectivity. All kinds of smart services can be connected in smart cities to offer citizens comprehensive solutions. Pre-emptive services, self-services, or highly interactive services, for example, have the potential to be implemented using current technology such as the internet of things, artificial intelligence, cloud computing, big data, machine learning, machine-to-machine communications, and the digital twin. Therefore, there is scope for research and implementation of other technology for smart services in the future. © 2023 ACM.

关键词： Smart city

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