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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Chitla, Vikas Benhur Lakshminarasimhan, P.S. Manoj Kumar, Mv Prashanth, B.S. Nitte Meenakshi Institute of Technology Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

The COVID-19 pandemic has unfolded with unprecedented complexity, marked by shifting epidemiological trends, evolving treatment protocols, and the emergence of new variants. Such inherent dynamism necessitates a thorough examination of data stability over time. In this study, our focus extends beyond univariate analysis, incorporating a multivariate approach to capture interdependencies among key health parameters of generated COVID-19 datasets over six years to provide critical insights into evolving symptoms and vital signs through the identification of concept drift. Our findings enhance the understanding of the pandemic's trajectory, informing healthcare practitioners, policymakers, and researchers about temporal variations in symptoms and vital signs. Concept drift implications in COVID-19 datasets include re-calibrating predictive models, refining treatment strategies, and enhancing decision support systems. This underscores the importance of adaptive analytics in navigating infectious disease dynamics amidst evolving global responses to the pandemic. © 2024 IEEE.

关键词： COVID-19

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Explore the Impact of Quantum Computing to Enhance Cryptographic Protocols and Network Security Measures 5

Explore the Impact of Quantum Computing to Enhance Cryptogra...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Periyasamy, P.N. Kamal, N. E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Hindusthan Institute of Technology Department of Computer Science and Engineering India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350383522

The revolutionary effects of quantum computing on security procedures for networks and cryptographic protocols are the focus of this study. Our research shows that conventional cryptographic algorithms are susceptible to quantum assaults;specifically, RSA and ECC encryption are very vulnerable. On the other hand, cryptographic algorithms that are immune to quantum attacks, such as multivariate polynomial cryptography and lattice-based algorithms, show promise in meeting these challenges. Key exchange rates between 200 kbps and 450 kbps and error rates as low as 0.5% are some of the significant achievements shown by Quantum Key Distribution (QKD) implementations. These applications show that QKD is a viable solution for protecting communication channels up to 200 km away. On top of that, there are remarkable outcomes when network security systems incorporate quantum advancements. The detection accuracy of Quantum-Enhanced Intrusion Detection Systems (QIDS) is 98.5% and the false positive rate is minimally 0.2%. The detection accuracy of quantum-secured communication systems is 99.2% and the false positive rate is an extremely low 0.1%. While quantum firewalls keep a detection accuracy of 97.8% and a false positive rate of 0.3%, quantum-safe cryptographic modules show a low latency of 3 ms. © 2024 IEEE.

关键词： cryptographic protocol data security quantum key distribution quantum-enhanced intrusion detection

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Crop Yields Prediction in Bangladesh: A Hybrid Machine Learning and DNN Approach

Crop Yields Prediction in Bangladesh: A Hybrid Machine Learn...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Meem, Rudaiba Haque Noor Turna, Taskin Pabna University of Science and Technology Dept. of Information and Communication Engineering Pabna Bangladesh

ISBN: (纸本)9798331529765

In todays world, machine learning and artificial intelligence are gaining popularity. In today's digital age, many tasks are being transformed through artificial intelligence and machine learning. Agriculture, as a vital component of human sustenance, remains The principal source of livelihood for many. Bangladesh is an agrarian economy due to its geographical location. Bangladeshs economy is heavily dependent on agriculture as it contributes a lot to the countrys GDP. Even though artificial intelligence and machine learning are being used to digitize every industry in this day and age, the agricultural sector is falling behind. In Bangladesh agricultural work is mostly done in old ways and manually. From crop selection to fertilizer selection, all work is done by farmers manually based on their experience. In addition, agricultural institutions in Bangladesh use manual data, which is not a proper solution to accurately select crops and yield prediction. To address these problems, there is a need for modern and innovative approaches to yield prediction and crop selection. This paper demonstrates the most efficient and cost-efficient approach to predicting crop yield utilizing deep neural networks (DNN) and machine learning techniques. Artificial Neural Network and Machine Learning are considered powerful tools for modeling and prediction. The effectiveness of the algorithms that were assessed in this study are Deep Neural Network- Gradient Boosting Regressor(DNN-GBR),Support vector machine(SVR), K-Nearest Neighbors Algorithm. The goal of this algorithm is to improve output and prediction. The performance of the proposed hybrid DNN-GBR model was evaluated using MSE and R-squared (coefficient of determination) metrics. In this paper, we have suggested using the hybrid DNN-GBR model for agricultural yield prediction and crop selection. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust Fetal Head Circumference Calculation on Ultrasound Images Using Just-in-Time Compiled Ellipse Fitting

Robust Fetal Head Circumference Calculation on Ultrasound Im...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Zumarsyah, Pandega Abyan Solaeman, Achmad Alvindo, Fhareza Nugroho, Hanung Adi Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350350821

Calculation of fetal head circumference (HC) is essential for various purposes, particularly in evaluating fetal health during prenatal care. However, performing HC calculations manually is labor intensive, prone to errors, and requires specific expertise. Thus, there is a pressing demand for automated methods for HC calculation. Recent automation methods for head circumference calculation focus on using deep learning that is often slow and memory hog due to the high computational complexity. This study presents a novel approach for fast and efficient automatic HC calculation from 2D ultrasound images by leveraging just-in-time compiled ellipse fitting based on Randomized Hough Transform. Before ellipse fitting, there are downscaling, blurring, and clean edge detection to increase robustness. After ellipse fitting, there is circumference calculation using the Ramanujan approximation. Our approach achieved a remarkable speed of 0.41 s per image with less than 800 kB of memory. It demonstrated robust accuracy with a Hausdorff distance of 3.88 mm, a Dice score of 0.93, and an absolute difference of 7.24 mm, which is comparable to existing methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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Smart Traffic Management System using Background Subtraction 4

Smart Traffic Management System using Background Subtraction

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Kottha, Bharath Reddy Anuradha, T. Penumacha, Naveen Kumar Gadde, Harsha Vardhan Velagapudi Ramakrishna Siddhartha Engineering College dept. of Information Technology Kanuru India

ISBN: (纸本)9798350385793

The development of an intelligent and adaptive traffic signal management system lies in the challenges associated with urban traffic congestion and the need for efficient emergency vehicle passage. Urbanization and population growth have led to increased vehicular traffic, resulting in congestion during peak hours. An automated traffic monitoring system at the junctions with the count of the vehicles can help to achieve a dynamic allocation of timing for vehicles for achieving a smooth and congestion - free traffic movement. The proposed system can help in adjusting traffic signal timings based on the density of vehicles in each lane while prioritizing emergency vehicles. A common approach for vehicle detection is background subtraction. Background subtraction is a popular computer vision technique used to extract the foreground objects from a given video or image sequence. The main goal of this process is to differentiate the moving objects from a relatively static background. Detecting the approach of emergency vehicles can be done using RFID reader and tags. The primary goal of this system is to reduce traffic congestion by allocating time slots for each lane based on their current density, thereby optimizing overall traffic flow. Additionally, the system is designed to prioritize the passage of emergency vehicles, like ambulances, through intelligent detection mechanisms, ensuring swift and unobstructed access to critical locations. © 2024 IEEE.

关键词： Traffic congestion

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Identification of Visitors at Tourist Destinations in Yogyakarta Using GPS Data

Identification of Visitors at Tourist Destinations in Yogyak...

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2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Nisa, Khairun Zahri Rakhman, Arkham Rizky Pratama, Azkario Widyawan Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350379839

This study utilized GPS data from telecommunications operators to analyze tourist behavior in Yogyakarta, Indonesia. A rule-based identification technique was developed to differentiate domestic tourist activity. The analysis revealed significant visitor pattern variations and highlighted priority destinations such as Gembira Loka and Parangtritis Beach. Preprocessing methods and rule-based classification categorized visitors into tourists (individuals from different sub-districts at tourist locations), working residents (spending 40 hours a week at tourist locations), visitors (spending an average of 3.3 hours at tourist sites), and ordinary residents (not fitting the previous categories). This study offers an essential reference for accurately assessing tourism's economic impact, emphasizing that non-local tourists contribute more significantly to local revenue through spending on accommodation, food, and attractions. The findings assist in planning visits and developing effective tourism policies. The study underscores the importance of precise visitor identification techniques to understand travel patterns and enhance tourism development. The results demonstrate GPS data's potential in tourism research, informing marketing strategies and product development. Future research could integrate additional data sources, such as social media check-ins or transaction records, to improve visitor profiling accuracy. Moreover, studies could investigate the long-term impacts of tourism on local economies and the environment. In Indonesia, working hours regulated by Law No. 13 of 2003 ensure a balance between productivity and leisure, influencing tourist behavior. This study advances the understanding of visitor behavior in Yogyakarta and promotes sustainable tourism development. © 2024 IEEE.

关键词： Product development

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T-type Inverter in both Continuous and Discontinuous Current Mode for Active Power Decoupling Capability

T-type Inverter in both Continuous and Discontinuous Current...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Higashide, Ryohei Kusui, Rintaro Watanabe, Hiroki Nakata, Yuki Itoh, Jun-Ichi Nagaoka University of Technology Dept. of Electrical Electronics and Information Engineering Nagaoka Japan

ISBN: (纸本)9798350376067

This paper proposes an active power decoupling (APD) method with both continuous and discontinuous current modes for a T-type inverter, which does not require additional components. The operation in continuous current mode (CCM) has uncontrollable periods of neutral point current for APD. On the other hand, the operation in discontinuous current mode (DCM) decreases efficiency due to an increase in the RMS value of the inductor current. The proposed method switches from CCM to DCM in the uncontrollable periods of the neutral current to reduce the inductor current RMS. However, the efficiency improvement effect is limited because the losses are too large in the DCM period. Therefore, the efficiency improvement method is applied to the CCM/DCM operation. The experimental result indicates that the efficiency improvement method improves the efficiency from 90.1% to 92.5%. In addition, the proposed CCM/DCM control reduces the second harmonic components of the input current by 90.1% compared with not using the APD operation. © 2024 IEEE.

关键词： Active power decoupling Continuous current mode Discontinuous current mode T-type inverter

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

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2024 IEEE International Conference on Signal Processing, information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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FACE UNLOCK SYSTEM FOR VEHICLES 2

FACE UNLOCK SYSTEM FOR VEHICLES

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Navya, Soleti Suresh, Juttu Sangeetha, A. Praveen, G. Nabi, Shaik Abdul Kumar, Dilip Mlr Institute of Technology Department of Computer Science and Information Technology India Malla Reddy Engineering College and Management Sciences Department of Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Cse Dept Avniet Jntuh India Mlr Institute of Technology Dept. of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350370188

Currently, all vehicles are key operated. Our intention is therefore to develop an intelligent unlocking system based on facial recognition technology. In Today's era of automation and smart devices, privacy and security are important issues in information technology so to information system, so it is urgent to change system security measures. In previous systems, many vehicle doors had mechanical locks with a limited number of keys. To enhance security, we propose an intelligent vehicle door unlocking system based on facial recognition the system uses camera sensor to recognize faces and image matching algorithms to recognize authenticated faces. Only people with matching faces can open the door this system not only provides additional security but also useful for systems without physical keys. © 2024 IEEE.

关键词： Network security

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