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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

The increasing popularity and accessibility of the Internet and smartphones have prompted studies examining the potential adverse effects of excessive use. The researcher is investigating problematic smartphone use and dependence (PSUD) and its implications for individuals' wellbeing. PSUD, also known as Internet addiction or smartphone addiction, refers to the compulsive use of smartphones and the Internet, negatively impacting an individual's life. Considering smartphones' modern and advanced nature, they serve as versatile devices encompassing various internet uses, making them the most common technological devices used to access the internet. Therefore, when researchers address technology or internet use-related issues, they often focus on smartphones and examine PSUD, which can also cover other technology-related problems. This interchangeability of terminologies allows researchers to study a broad spectrum of technology use issues through smartphone analysis. Research has shown that PSUD can lead to various negative consequences, including psychological, behavioural, and social problems. These issues encompass anxiety, depression, poor sleep quality, academic and occupational challenges, relationship difficulties, and impaired social and emotional functioning. The factors contributing to PSUD's development are complicated and involve personal, environmental, technological, and socio-demographic elements. Individual factors may include mental health problems, poor coping skills, low self-esteem, and a lack of social support. Environmental factors may involve a lack of parental supervision, peer pressure, and cultural norms encouraging excessive internet and smartphone use. Technological aspects may encompass app and website designs that promote addictive behaviours. Socio-demographic factors, such as age, gender, and socioeconomic status, can also influence smartphone use patterns and contribute to PSUD. The effects of PSUD are both psychological and behavioura

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QViLa: Quantum Infused Vision-Language Model for Enhanced Multimodal Understanding

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SN Computer science 2024年第8期5卷 1023页

作者： Mukesh, K. Jayaprakash, S.L. Kumar, R. Prasanna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamilnadu Chennai 601103 India

Vision-language models have emerged as transformative tools, revolutionizing the integration of visual and textual information, forging pathways for nuanced interpretations across various applications. The evolution of these models underscores the challenge of achieving seamless modality fusion, particularly in aligning raw pixel values of images with high-level semantics of text, a bottleneck that often hinders optimal cross-modal representations. Addressing this, our research introduces a quantum-enhanced multimodal framework. The main objective of our proposed work is the integration of classical vision-language transformers with quantum-augmented layer, aimed at enhancing the fusion of extracted feature embeddings, thereby bridging the modality gap. The quantum computing techniques, offers an innovative approach to information processing, which paves the way for richer and more intricate visual-textual representations. Furthermore, the shared self-attention mechanism accentuates the model’s ability to detect complex modality interactions. The quantum-enhanced framework is empirically evaluated on the VQA v2 dataset. This evaluation not only considers accuracy across diverse question categories but also the model’s computational efficiency, emphasizing the pivotal contributions of quantum computations in achieving heightened accuracy levels. Further exploration into the influence of different quantum feature maps aided in identifying the most optimal model variant. Our findings highlight the quantum layer’s pivotal role in improving the efficacy of classical vision language models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multimodal learning Quantum computing Vision language model Visual reasoning

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Customer Segmentation Analysis Using Clustering Algorithms 3rd

Customer Segmentation Analysis Using Clustering Algorithms

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Reddy, Biyyapu Sri Vardhan Rishikeshan, C.A. Dagumati, VishnuVardhan Prasad, Ashwani Singh, Bhavya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819939312

Customer segmentation has been deployed as a prudent marketing strategy by companies to ensure that their investments are less risky and more judicious. Segmenting customers helps the companies to divide the customers into groups that reflect similarity and maximize the value of each customer to the business. The main goal of this research is to use a machine learning clustering approach called K-means clustering to accomplish consumer segmentation. Besides, the research work is also focused on performing exploratory data analysis on the given dataset. To group the customers into clusters, a K-means clustering algorithm is performed on the customer dataset. To achieve optimization and validation of the clusters, popular heuristic, interpretation, and approximation methods have been included in this paper. Further, for analyzing and visualizing the important facets of the customer dataset and the operation of the K-means algorithm, the paper presents some colorful and informative representations. The implementation of this research work has been done in the R programming language. The outcome of this work includes visualizing the segments of the mall customers in the form of clusters based on their spending scores and annual incomes. Furthermore, a better customer segmentation could be achieved by taking product reviews and customer feedback into consideration. Nevertheless, customer segmentation remains a prospective topic for many researchers and companies due to dynamic customer behavior. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

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Zero-trust-based security model against data breaches in the banking sector: A blockchain consensus algorithm

IET Blockchain

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IET Blockchain 2023年第2期3卷 98-115页

作者： Chaudhry, Umair B. Hydros, Aysha K. M. School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing Engineering and Information Sciences Northumbria University London United Kingdom

Cyber security in the banking sector is of high importance nowadays. The rate of cyberattacks is spiking every year, and the implementation of strong cybersecurity models is required to ensure the confidentiality and integrity of data. Since protecting a bank requires a wide range of security practices, this paper focuses on protecting the bank resources from malicious actors and securing the transactions using a blockchain consensus mechanism that uses a zero-trust security approach among the participants in the transaction. In addition to the framework, an algorithm for blockchain-based online transactions was designed to make use of practical implementation in the future. The ideas formulated during the research and literature review were integrated to design the framework and the algorithm. The proposed framework ensures that the security of the banking sector can be enhanced by adopting the zero-trust concept and blockchain technology. The consensus algorithms used for the transaction make it immutable and decentralized. Zero-trust principles adopted in the model ensure the confidentiality and integrity of the banking system. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Cybersecurity

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Cooperative Target Tracking of Multiple Autonomous Surface Vehicles Under Switching Interaction Topologies

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 673-684页

作者： Lang Ma Yu-Long Wang Qing-Long Han Shanghai Key Laboratory of Power Station Automation Technology and the School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 200444China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia IEEE

This paper is concerned with the cooperative target tracking of multiple autonomous surface vehicles(ASVs)under switching interaction *** the target to be tracked,only its position can be measured/received by some of the ASVs,and its velocity is unavailable to all the ASVs.A distributed extended state observer taking into consideration switching topologies is designed to integrally estimate unknown target dynamics and neighboring ASVs'***,a novel kinematic controller is designed,which takes full advantage of known information and avoids the approximation of some virtual control ***,a disturbance observer is presented to estimate unknown time-varying environmental ***,a distributed dynamic controller is designed to regulate the involved ASVs to cooperatively track the *** enables each ASV to adjust its forces and moments according to the received information from its *** effectiveness of the derived results is demonstrated through cooperative target tracking performance analysis for a tracking system composed of five interacting ASVs.

关键词： Autonomous surface vehicles(ASVs) cooperative target tracking distributed extended state observer switching topologies

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Revolutionizing Potato Farming: A CNN-Powered Approach for Early Blight and Late Blight Detection to Ensure Global Food Security

Revolutionizing Potato Farming: A CNN-Powered Approach for E...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2024

作者： Ponnuru, Mahesh Datta Sai Amasala, Likhitha Srm Institute of Science and Technology School of Computing Chennai India Vellore Institute of Technology School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9798350348460

Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN for early disease detection in potato crops. Our objective is to empower farmers with rapid and accurate disease identification, facilitating timely treatments to prevent economic losses and reduce agricultural waste. Our CNN model achieved a remarkable validation accuracy of 99.59%, outperforming conventional methods. With 183,747 trainable parameters, our efficient and accurate model is set to revolutionize agricultural practices. Furthermore, we are deploying our model to Google Cloud and developing a React Native mobile app for potato prediction. This integration aims to provide farmers with real-time disease insights, preventing economic losses and ensuring global food security, thereby enhancing economic stability in the potato farming sector. © 2024 IEEE.

关键词： Agriculture Convolutional Neural Networks (CNNs) Crop protection Food security Early blight Economic stability Late blight Potato disease detection

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Smart Health Diagnosis:Harnessing Data Mining for Predictive Healthcare 1

Smart Health Diagnosis:Harnessing Data Mining for Predictive...

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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Indrakumari, R. Pandey, Vishal Sinha, Ashish Kumar Kumar, Ankesh Galgotias University School of Computer Science and Engineering India Galgotias University School of Computing Application and Technology India

ISBN: (纸本)9798350356816

The use of Smart Health Diagnosis: Data Mining for Predictive Healthcare is to provide a user- friendly and easily accessible platform where individuals can get instant health advice through an intelligent online healthcare system. By the analysis of the reported symptoms, the system can predict potential future diseases and provide relevant recommendations to healthcare providers on courses of action. Intelligent methods accurately determine potential interferences based on patient symptoms. Medical record database was used and symptoms were assigned to underlying diseases using Naive Bayesian calculations. Fast, reliable access to health care is a cornerstone of individual and social health but many people face barriers in receiving timely care. We are trying to solve these problems by introducing Health Prediction System, a revolutionary online healthcare solution that uses data mining for quick support and customized recommendations. © 2024 IEEE.

关键词： Electronic health record

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The emergence of technological dependencies: A critical review of problematic smartphone use and dependence (PSUD) 29

The emergence of technological dependencies: A critical revi...

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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

Problematic smartphone use has emerged as a growing concern in modern society. However, research in this field is still in its early stages and faces several controversies and limitations. A significant challenge lies in the need for a standardised definition of smartphone dependence and addiction, making it difficult to compare results across studies and generalise findings to the larger population. To address this issue, I advocate using the umbrella term Problematic Smartphone Use and Dependence (PSUD) to encompass all conditions related to excessive and problematic use, addiction, and smartphone dependence. An ongoing debate centres on whether addiction and dependence models are appropriate for understanding PSUD, leading researchers to explore different approaches to this complex phenomenon. Some studies have relied on self-reported methods, while others have used objective criteria, such as tracking the time spent on specific apps. The causal relationship between smartphone use and adverse outcomes, such as anxiety, depression, and sleep disturbances, is still inadequately understood. Some recent investigations have observed strong correlations, while others have found weak or inconsistent associations. Not all high smartphone use is problematic. Practical reasons, such as the requirements of particular roles or daily responsibilities, may drive high levels of smartphone use. High use levels appear to increase as more smartphone apps become available, and adoption is now widespread. Differentiating high use (for legitimate reasons) from problematic use (e.g. uncontrolled or unnecessary use) requires new terminologies and tools. In the past, use of smartphones for practical reasons has been referred to as ‘productive use’ (or similar), generally referring to use for work purposes. However, today practical or productive use may extend beyond just work purposes, including parenting, social organisation and even legitimate forms of entertainment. To encapsulate th

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