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12th International Conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Mohanty, Vidya Panda, Amiya Ranjan Behera, Dayal Kumar Swetanisha, Subhra School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computing Trident Academy of Technology Bhubaneswar India

ISBN: (纸本)9789819601387

Urban growth forecasting is a critical component of sustainable city development, enabling policymakers to make informed decisions about land use and infrastructure planning. This study presents a comparative analysis of two prominent forecasting models, Auto ARIMA and Prophet, applied to time series data derived from the Landsat Normalized Difference Built-up Index (NDBI) for the Bhubaneswar-Cuttack twin city region. The dataset spans from January 1, 2000, to January 1, 2024, providing a comprehensive view of urban expansion over nearly two and a half decades. The Auto ARIMA model, known for its robustness in time series forecasting, was first employed to predict future urban growth patterns. Subsequently, the Prophet model, a decomposable time series model that accommodates seasonal fluctuations was applied to the same dataset. The performance of both models was evaluated based on their prediction accuracy and computational efficiency. Results indicate that the Prophet model outperformed Auto ARIMA in forecasting urban growth in the Bhubaneswar-Cuttack region. The findings serve as a foundation for further exploration into the integration of remote sensing data and predictive modeling for urban planning and management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sustainable city

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs

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ACM Transactions on Embedded computing Systems 2025年第3期24卷 1-22页

作者： Chang, Qiong Chen, Xinyuan Li, Xiang Wang, Weimin Miyazaki, Jun School of Computing Institute of Science Tokyo Meguro Tokyo Japan International School of Information Science and Engineering Dalian University of Technology Liaoning Dalian China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing China

The visual-based SLAM (Simultaneous Localization and Mapping) is a technology widely used in applications such as robotic navigation and virtual reality, which primarily focuses on detecting feature points from visual images to construct an unknown environmental map and simultaneously determines its own location. It usually imposes stringent requirements on hardware power consumption, processing speed, and accuracy. Currently, the ORB (Oriented FAST and Rotated BRIEF)-based SLAM systems have exhibited superior performance in terms of processing speed and robustness. However, they still fall short of meeting the demands for real-time processing on mobile platforms. This limitation is primarily due to the time-consuming Oriented FAST calculations accounting for approximately half of the entire SLAM system. This article presents two methods to accelerate the Oriented FAST feature detection on low-end embedded GPUs. These methods optimize the most time-consuming steps in Oriented FAST feature detection: FAST feature point detection and Harris corner detection, which is achieved by implementing a binary-level encoding strategy to determine candidate points quickly and a separable Harris detection strategy with efficient low-level GPU hardware-specific instructions. Extensive experiments on a Jetson TX2 embedded GPU demonstrate an average speedup of over 7.3 times compared to widely used OpenCV with GPU support. This significant improvement highlights its effectiveness and potential for real-time applications in mobile and resource-constrained environments. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Edge detection

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Vision-Based Automated Parking Monitoring Using Geo-Tagged UAV Images

Vision-Based Automated Parking Monitoring Using Geo-Tagged U...

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2024 International Conference on Green Energy, computing and Sustainable technology, GECOST 2024

作者： Lai, Chung Yi Jo, Hudyjaya Siswoyo Lai, Chean Hung Dunn, Michelle Swinburne University of Technology Sarawak Campus Faculty of Engineering Computing and Science Kuching Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn Campus Melbourne Australia

ISBN: (纸本)9798350357905

This paper proposes an automated solution for parking monitoring using geo-tagged UAV (Unmanned Aerial Vehicle) images. The parking monitoring system utilizes a UAV to patrol the parking lot. The UAV captures video footage that contains geolocation information. Geo-tagged images are extracted from the video footage and processed using the YOLOv8 object detection algorithm to recognize occupied and vacant parking spaces. The license plates of the parked vehicles in the occupied parking space are extracted using EasyOCR and stored in a database. Each parking space is labeled with a unique ID with its date, time, geolocation data, and occupancy status to facilitate integrated digital parking solutions for both the users and parking operators. © 2024 IEEE.

关键词： License plates (automobile)

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Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

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Computer Modeling in engineering & Sciences 2024年第1期138卷 301-320页

作者： Huafeng Chen Junxing Xue Hanyun Wen Yurong Hu Yudong Zhang School of Computer Engineering Jingchu University of TechnologyJingmen448000China School of Computer Science Yangtze UniversityJingzhou434023China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore *** deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of *** solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this *** core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship *** also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model *** compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called *** show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

关键词： Ship detection deep learning data augmentation object location object classification

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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

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Computer Systems Science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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Bandgap Engineered Double-Cation/Double-Halide Quasi-Cubic Perovskite for Highly Efficient (>36%) Indoor Photovoltaics

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IEEE Journal of Photovoltaics 2023年第6期13卷 858-865页

作者： Singh, Ranbir Kundar, Milon Pal, Suman Kalyan Sharma, Satinder K. Indian Institute of Technology Mandi School of Computing and Electrical Engineering Mandi175005 India Indian Institute of Technology Mandi School of Mechanical and Materials Engineering Mandi175005 India Indian Institute of Technology Mandi School of Physical Sciences Mandi175005 India

Double-cation- and double-halide-based perovskites are well known for their better structural stability and improved efficiency in photovoltaics compared to single-cation-based perovskites. Herein, we have investigated the effects of the addition of formamidinium bromide (FABr) in methylammonium lead iodide (MAPbI3) perovskite for the formation of a double-cation/double-halide (DCDH) perovskite structure, namely, MA1-xFAxPbI3-xBrx, which is specifically engineered for efficient indoor light harvesting. The indoor photovoltaics (IPVs) fabricated from the DCDH perovskite layer exhibited a distinctively higher short-circuit current density (JSC) of 172.64 μA·cm-2, an open-circuit voltage (VOC) of 0.91 V, and a power conversion efficiency (PCE) of 36.29% compared to the JSC of 159.03 μA·cm-2, VOC of 0.84 V, and PCE of 27.89% observed for the MAPbI3 IPVs under the simulated indoor LED light at 1000 lux. The use of formamidinium (FA+) cation and bromide (Br-) anion in the DCDH perovskite helps in stabilizing the perovskite crystalline phase from tetragonal to quasi-cubic, increases the bandgap, and complements the optical absorption in the visible region. Moreover, FA+ and Br- suppress the trap states and nonradiative recombination loss in the DCDH perovskite structure. The results from this study will help researchers understand the mechanism of DCDH-perovskite-based IPVs, which is well explained by studying the charge carrier recombination dynamics and its correlation with the crystal structure of the perovskites under indoor lighting conditions. In addition, the optimized FABr-incorporated device exhibits a PCE of 20.59% under standard 1-sun conditions, which is better than the reference MAPbI3 device (17.16%). © 2011-2012 IEEE.

关键词： Perovskite

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Tolerance-based Elite Mutation Firefly Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1406-1425页

作者： Chen, Juan Zeng, Ren-Xian Qiu, Shen-Yu Kang, Ping Zhao, Jia School of Information Engineering Nanchang Institute of Technology Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City China School of Information Engineering Nanchang Institute of Technology China College of Science Nanchang Institute of Technology China

For the firefly algorithm in the solution of complex functions are prone to low accuracy and "premature" phenomenon. In this paper, we propose a tolerance-based elite mutation firefly algorithm (MOFA-TEM). The algorithm employs non-dominated sorting and congestion distance to select the elite leader, which is fused with the individual historical optimal solution to form a more comprehensive integrated information of the population, and jointly guide the movement of the fireflies, which not only en-hances the interactivity of the information between fireflies in the population, but also improves the algorithm’s ability of global exploration. Secondly, a tolerance-based firefly state judgment mechanism is designed, which will exponentially increase the probability of updating the elite leader as the number of individual firefly stagnation increases, which both fully exploits the potential of ’s search for excellence and prevents ineffective search-ing of the population. Finally, if the firefly triggers the tolerance mechanism, the elite mutation strategy will be used to update the and select a new elite learning object to avoid the algorithm from stagnating. Experiments on 19 test functions and validation using Friedman’s test show that MOFA-TEM gives better results compared to other optimization algorithms. © 2023.

关键词： Bioluminescence

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The prevalence of Internet and smartphone addiction: A growing digital dependency in the modern age 29

The prevalence of Internet and smartphone addiction: A growi...

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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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SecureML: Machine Learning-Powered Malware Detection 2

SecureML: Machine Learning-Powered Malware Detection

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Awasthi, Animay Ahsan, Mohd Vanusha, D. Delsi Robinsha, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India

ISBN: (纸本)9798350383867

As malware threats become increasingly complex, the development of advanced detection systems is necessary to protect digital assets. SecureML Using Machine Learning presents an innovative approach to combat the escalating threat of malicious software by leveraging machine learning algorithms. This research paper outlines the design, implementation, and evaluation of SecureML, emphasizing its effectiveness in detecting both known and novel malware strains. Through comprehensive feature selection techniques and algorithmic comparison, SecureML identifies crucial features and selects the Random Forest algorithm as the most effective for malware detection, achieving an impressive accuracy rate of 99.40% on the test set. The system's low false positive and false negative rates validate its robustness and reliability in accurately distinguishing between benign and malicious files. SecureML demonstrates significant potential in bolstering cybersecurity defenses and safeguarding digital assets against evolving malware threats. © 2024 IEEE.

关键词： Adversarial machine learning

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