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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Choudhury, Tanupriya Rohini, A. Mahdi, Hussain Falih Uttarakhnad Dehradun248007 India Graphic Era Hill University Dehradun248002 India Dept. of Computer Science & Engg Visakhapatnam531162 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350337525

The advancement of agriculture in food production is an increasing concern. It is one of the major challenges in contemporary farming. The development methods of soil farming have increased the fabrication of crops and food. It emphasizes the vital role of sustainability in soil. Different technologies were used to grow and flourish in soil to supply the nutrition's. It buffers and delicate the plant roots from fluctuations in temperature. Hydroponically grown fruits can produce rich nutrition's like traditional grown fruits. Proposed research has focused on the IoT in Hydroponics for Growing Tomato Plants. The Nutrient Film Technique (NFT) is needed for the present farmers for monitor and managing the crop system. For managing and monitoring the crop to execute the method scientifically and take the decision about the crops in time. The integration of IoT has given the solutions to solve the contemporary problems of calcium deficiency, lack of airflow around plants and so on. This proposal has developed a smart hydroponic system with technology enabled by IoT system. © 2023 IEEE.

关键词： Crops

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Detection of Ophthalmic Disorders Through Deep Feature Fusion

Detection of Ophthalmic Disorders Through Deep Feature Fusio...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Hussain, Muhammad Toheed Faraz, Muhammad Aziz, Sumair Iqtidar, Khushbakht Khan, Muhammad Umar Montes, Gabriel Axel University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences and Tech. Dept. of Computer and Software Engineering Islamabad Pakistan University of Newcastle CallaghanNSW Australia

ISBN: (纸本)9798350349719

Given the rising prevalence of ophthalmic disorders, this study aims to meet the urgent need for an automated technique capable of diagnosing and treating these conditions. Our approach combines deep learning and machine learning techniques to develop a diagnostic system that can detect and classify seven distinct ophthalmic disorders. Manual diagnosis of ophthalmic disorders is labor-intensive and slow, worsened by a shortage of medical experts. In this paper, we propose a medical diagnostic system that utilizes deep features extracted from a ResNet-50 CNN, which has been trained on retinal images enhanced through contrast-limited adaptive histogram equalization (CLAHE). These deep features serve as input for the machine learning model. To address the class imbalance problem, we employed ADASYN to generate synthetic features for all classes. An Ensemble subspace KNN classifier, trained on these deep features, achieved a remarkable mean accuracy of 99.18% compared to other classifiers evaluated in this study. The proposed technique has the potential to assist medical experts in achieving timely and accurate diagnoses of ophthalmic diseases. © 2024 IEEE.

关键词： Ophthalmology

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Driver Drowsiness Detection with Region-of-Interest Selection Based Spatio-Temporal Deep Convolutional-LSTM 16

Driver Drowsiness Detection with Region-of-Interest Selectio...

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Basit, Muhammad Saif Ahmad, Usman Ahmad, Jameel Ijaz, Khalid Ali, Syed Farooq Sst Umt Dept. of Computer Science Lahore Pakistan Sen Umt Dept. of Electrical Engineering Lahore Pakistan Sst Umt Dept. of Software Engineering Lahore Pakistan

ISBN: (纸本)9781665464772

Driver fatigue and drowsiness instigate road traffic accidents while driving throughout the years. to reduce road traffic injuries and fatality cases, a real-time drowsiness detection system is needed by using artificial intelligence algorithms to detect drivers' tiredness and drowsiness at an early stage. This study proposes an automatic region-of-interest selection based stacked spatio-temporal convolution-long short-term memory (ConvLSTM) drowsiness detection neural network for an in-vehicle surveillance and security system. Haar Cascade classifiers are used to select the region-of-interest on the human face. A ConvLSTM model is implemented to extract spatio-temporal features from the selected region-of-interest and to predict the drowsiness state of the driver. The performance of the proposed model is compared with various pre-trained deep learning models such as CNN, VGG-16, VGG-19, ResNet-50 and MobileNet. The proposed model is trained on the Yawn Eye and MRL benchmarked image datasets. The proposed approach achieves an accuracy of 99.44% on the Yawn Eye dataset and 90.12% on the MRL dataset. The model is further tested and validated using a live feed camera. © 2022 IEEE.

关键词： Convolution

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A Comparative Study of ML Models on Large-Scale Turkish Sentiment Datasets

A Comparative Study of ML Models on Large-Scale Turkish Sent...

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Innovations in Intelligent Systems and Applications Conference

作者： Rukiye Savran Kiziltepe Ercan Ezin Murat Karakus Dept. of Software Engineering Karadeniz Tech. University Trabzon Türkiye Dept. of Computer Engineering Harran University Sanliurfa Türkiye Dept. of Software Engineering Ankara University Ankara Türkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

This study investigates the performance of traditional Machine Learning (ML) models for Sentiment Analysis (SA) in the Turkish language, addressing the growing need for robust Natural Language Processing (NLP) techniques in low-resource languages. We compare the efficacy of Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers on two recently developed large-scale Turkish sentiment datasets: “Vitamins and Supplements Customer Review” (VSCR) and “Turkish Sentiment Analysis version I” (TRSAv1). Our research leverages advanced hyperparameter tuning techniques to optimize these models, providing benchmarks and practical insights for Turkish SA. The study contributes to the field by establishing performance baselines on new open-source datasets, enhancing understanding of NLP models for agglutinative languages, and analyzing factors affecting classifier performance in binary and multi-class scenarios. Our findings reveal the strengths and limitations of each model within the context of Turkish language SA, offering valuable guidance for future research and applications in this domain.

关键词： Support vector machines Analytical models Sentiment analysis Technological innovation Systematics Computational modeling Linguistics Benchmark testing Tuning Classification tree analysis

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Robust and Reliable Liveness Detection Models for Facial Recognition Systems 20

Robust and Reliable Liveness Detection Models for Facial Rec...

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

作者： Anjum, Haris Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Siddique, Abu Bakar Imad, Muhammad Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain

ISBN: (纸本)9798350395785

Facial Liveness Detection is instrumental in combating fraudulent practices and identity theft by differentiating genuine faces from forgeries. Given that facial recognition is now an integral part of many sectors like banking and law enforcement, liveness detection has become a vital aspect for maintaining the trustworthiness of these applications. Unfortunately, the utility of prevalent facial liveness detection models is constrained by their lack of generalizability to novel, unseen data, which presents a substantial impediment to practical applications. To mitigate this issue, our research endeavored to construct a reliable and precise facial liveness detection model that can effectively navigate this challenge and ensure consistent performance across diverse datasets. The devised model exhibited excellent accuracy, which attests to its robustness. This research underscores the advantages of employing deep learning methods such as Convolutional Neural Networks (CNNs) as they significantly enhance the model's accuracy. We intend for our findings to drive further research and progress in this critical field, by laying the groundwork for future work that integrates multiple liveness detection methods and collects more varied training data to develop an even more resilient and efficient system. © 2023 IEEE.

关键词： Feature extraction

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Utilizing Machine Learning to Enhance Infrastructure Resilience in Cold Regions 20

Utilizing Machine Learning to Enhance Infrastructure Resilie...

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20th International Conference on Cold Regions engineering: Sustainable and Resilient engineering Solutions for Changing Cold Regions, ICCRE 2024

作者： Shohel Rana, Md Gudla, Charan Ahmed, Feroz Nobi, Mohammad Nur Dept. of Computing and Software Engineering Florida Gulf Coast Univ. Fort MyersFL United States Dept. of Computer Science and Engineering Mississippi State Univ. StarkvilleMS United States Prediction 3D Technologies BiloxiMS United States Dept. of Computer Science Univ. of Texas at San Antonio San AntonioTX United States

ISBN: (纸本)9780784485460

In the challenging domain of engineering, where cold regions present formidable challenges, we confront the relentless forces of nature. From sub-zero temperatures to the unpredictable dance of snowfall and the silent buildup of ice, these regions demand innovative solutions to fortify the resilience of critical infrastructure. This initiative harnesses the potential of cutting-edge technology and leverages the extensive historical weather data tapestry. It introduces a pioneering strategy by integrating machine learning algorithms with extensive weather data, steering cold region engineering into an era defined by foresight and adaptability. This paper studies a transformative approach designed to forecast, prevent, and ultimately enhance infrastructure resilience in the face of rigid cold. Addressing the distinct challenges of cold region engineering, arising from harsh winter conditions such as extreme cold temperature, snowfall, and ice accumulation, we offer a comprehensive study using machine learning algorithms applied to historical weather data to construct a deeper analysis model capable of highlighting adverse weather effects. This, in turn, covers the way for optimized resource allocation, streamlined maintenance planning, and design enhancements. Our proposed study follows a systematic process, encompassing meticulous data collection, appropriate feature selection, and aiming seamless integration of the model into existing infrastructure management systems. Additionally, it facilitates the implementation of efficient and proactive measures to mitigate the impact of severe weather conditions on infrastructure. The paper also conducts three different hypotheses testing: temperature impact hypothesis, precipitation influence hypothesis, and ice accumulation and infrastructure resilience hypothesis, propelling engineering practices to new heights, particularly in the face of challenging cold environments. © ASCE.

关键词： Snow

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Synthetic Data: Revisiting the Privacy-Utility Trade-off

arXiv

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arXiv 2024年

作者： Sarmin, Fatima Jahan Sarkar, Atiquer Rahman Wang, Yang Mohammed, Noman Dept. of Computer Science University of Manitoba Canada Dept. of Computer Science and Software Engineering Concordia University Canada

Synthetic data has been considered a better privacy-preserving alternative to traditionally sanitized data across various applications. However, a recent article challenges this notion, stating that synthetic data does not provide a better trade-off between privacy and utility than traditional anonymization techniques, and that it leads to unpredictable utility loss and highly unpredictable privacy gain. The article also claims to have identified a breach in the differential privacy guarantees provided by PATE-GAN and PrivBayes. When a study claims to refute or invalidate prior findings, it is crucial to verify and validate the study. In our work, we analyzed the implementation of the privacy game described in the article and found that it operated in a highly specialized and constrained environment, which limits the applicability of its findings to general cases. Our exploration also revealed that the game did not satisfy a crucial precondition concerning data distributions, which contributed to the perceived violation of the differential privacy guarantees offered by PATE-GAN and PrivBayes. We also conducted a privacy-utility tradeoff analysis in a more general and unconstrained environment. Our experimentation demonstrated that synthetic data indeed achieves a more favorable privacy-utility trade-off compared to the provided implementation of k-anonymization, thereby reaffirming earlier conclusions. © 2024, CC BY.

关键词： Anonymity

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Comparing Emotion Detection Methods in Online Classrooms: YOLO Models, Multimodal LLM, and Human Baseline

Comparing Emotion Detection Methods in Online Classrooms: YO...

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IEEE Education engineering (EDUCON)

作者： Medha Mohan Ambali Parambil Salah Bouktif Munkhjargal Gochoo Fady Alnajjar Dept. Computer Science and Software Engineering United Arab Emirates University Al Ain UAE

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

The COVID-19 pandemic has transformed learning environments, challenging educators to understand students' behaviors during the online mode of learning, in particular emotions associated with students' attention during virtual classrooms. As learning transitions between physical and virtual spaces, the ability to interpret student attention and engagement has become complex. In response to this challenge, our research investigates the use of GPT-4o, a multimodal large language model, for identifying student emotions by analyzing images in diverse learning settings. The study involved analyzing online classroom images featuring 149 faces, utilizing three distinct approaches: a computer vision model (YOLO), the multimodal LLM (GPT-4o), and a human-annotated baseline. The analysis systematically categorized facial expressions into eight emotional categories: Happy, Sad, Angry, Neutral, Contempt, Disgust, Fear, and Surprise. The findings indicate that multimodal LLMs can effectively detect student emotions, achieving an average accuracy of 93.8%, which aligns with the human baseline accuracy of 97.0%. In contrast, YOLO models maintained an average accuracy of 81.9%, performing well for basic emotions but struggling with subtle expressions. This research contributes to enhancing educational practices by providing valuable insights regarding the application of multimodal LLMs to assist educators in comprehending student emotions within both physical and digital classroom settings.

关键词： YOLO Emotion recognition Privacy Accuracy Annotations Pandemics Scalability Large language models Real-time systems Faces

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Web Accessibility of the Cyprus Island Food Retailers' Websites 5

Web Accessibility of the Cyprus Island Food Retailers' Websi...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Iseri, Erkut Inan Uyar, Kaan Ilhan, Umit Near East University Dept. of Physics TRNC Mersin 10 Nicosia Turkey Near East University Dept. of Software Engineering TRNC Mersin 10 Nicosia Turkey Near East University Dept. of Computer Engineering TRNC Mersin 10 Nicosia Turkey

ISBN: (纸本)9798350337525

Accessibility is the process of making information and electronic communication environments meaningful and usable for most people including those with disabilities. It is not always an easy task to provide all user communities with different areas of interests and needs with exact styles and contents. It is a good practice to employ responsible designs and a certain degree of interaction that provide equitable conditions to suit the needs of the most user communities. It became evident that in the passed two years the world went thru a terrifying Coronavirus disease which made the accessibility of web sites of retailers even more challenging. Most of the food supplying companies introduced shop-to door deliveries of food products with web based applications. Although, technically speaking, these web sites accomplished the task for most of the people but with some accessibility issues. These issues can be identified and solved by following the international standards to gain world wide acceptance. This study covers the investigation of the accessibility of the Food Retailers' websites in the whole of Cyprus Island. Web Content Accessibility Guidelines 2.1 (WCAG 2.1) published by the World Wide Web Consortium (W3C) is followed in the process. Three testing software are used to determine the degree of compliance of the food retailers with the WCAG 2.1 in Cyprus Island. The aim of this study is to raise awareness about web accessibility among general public. The findings are not so promising and the sites examined are not in compliance with the standard of the guidelines defined in WCAG. © 2023 IEEE.

关键词： Websites

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Towards the Utilization of Parallel Programming to Speed Up RAM Forensics 10

Towards the Utilization of Parallel Programming to Speed Up ...

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10th International Conference on software Defined Systems, SDS 2023

作者： Al-Sharif, Ziad A. Alhamouri, Rahaf Jararweh, Yaser Lewis University Dept. of Engineering Computing and Mathematical Sciences Romeoville United States Jordan University of Sci. and Tech. Dept. of Software Engineering Irbid Jordan Jordan University of Sci. and Tech. Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350382235

Memory forensics uses volatile digital artifacts as evidence about criminal activities. Analyzing captured memory dumps for volatile data requires time and effort. This paper studies the utilization of parallel programming to speed up RAM forensics. It presents a performance-based evaluation of parallel programming in the domain of memory forensics and compares sequential and parallel approaches to speed up the memory analysis process. First, it evaluates the sequential approach and uses it as a base case for further parallel approaches. Second, it evaluates two of the parallel approaches that can be performed on a typical user machine. Our experiments evaluate the use of two parallel programming paradigms: the in-process parallelization approach using OpenMP, and the inter-process parallelization approach using MPI. Our results compare the performance of the sequential approach, OpenMP thread-based approach, and MPI process-based approach. Experimentations compare the performance of three scenarios using six files of different sizes and various numbers of threads and/or processes. The results show that the use of MPI is slightly better than OpenMP approaches on the use of 2 and 4 processes/threads. However, when the number of processors/threads is increased to 8 and 16, OpenMP slightly outperforms the MPI approach. Additionally, the parallelization approach using OpenMP and MPI provides 3X to 5X speed up over the traditional sequential approach. Moreover, it is worth mentioning that this speed-up is achieved on traditional user machines without the use of HPC computers. © 2023 IEEE.

关键词： Parallel programming

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