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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Swathi, M. Prithika, N.G. Sri Aishwariya, N. Jayaram, B. Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

This paper aims to investigate a system that uses various machine-learning algorithms to predict symptoms and deep-learning techniques for image processing that leads to early disease prediction, an essential aspect of today's healthcare industry. The main objective of this system is to enable both the healthcare professionals and the patients to interpret the results at a faster rate. Existing systems provide results based on only symptoms which is not very effective in real-time but our system works well with both symptoms and image-based specifications. This investigation explores several algorithms like Random Forest, Decision tree, XGBoost, Logistic regression, Long-term short memory (LSTM), and Naïve Bayes for symptom-based prediction [14]. CNN (Convolutional Neural Network) deep learning techniques, notably DenseNet121, are used for image processing. The use of image processing bridges the gap between the user who feels symptom-based prediction to be a bit difficult. Furthermore, diverse experiments are done on multiple datasets to establish more accurate results. This system also ensures the safeguarding of sensitive information. The proposed disease prediction system helps empower healthcare professionals and individuals with proactive insights into potential health risks by giving accurate predictions for early intervention of the disease. © 2024 IEEE.

关键词： Forecasting

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Autonomous Social Amenity, Pothole Detection and Reporting 4

Autonomous Social Amenity, Pothole Detection and Reporting

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Mosela, David Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. Computer Systems Engineering Tshwane South Africa Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. This study provides an in-depth exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing. © 2024 IEEE.

关键词： Highway administration

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Broadband single-polarization single-mode low confinement loss hollow core anti-resonant fiber with semi-elliptical nested tube

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Optical and Quantum Electronics 2025年第4期57卷 1-12页

作者： Lin, Wen Fei, Yihong Wang, Ning Jia, Hongzhi Engineering Research Center of Optical Instruments and Systems Ministry of Education Shanghai Key Laboratory of Modern Optical Systems School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

In this paper, a hollow-core anti-resonant optical fibre containing a semi-elliptical nested tube is proposed, which has the characteristics of single-polarization, large bandwidth, single-mode and low confinement loss. By optimizing the structural design, the polarization extinction ratio (PER) of the fiber reaches 21,0183 at a wavelength of 1.55 μm, and the y-polarized fundamental mode loss is only 0.00086 dB/m. By changing the material and thickness of the semi-ellipsoidal nested tube, a large bandwidth of 280 nm was achieved in the wavelength range of 1.51-1.78 μm, with loss limited to less than 0.001 dB/m within the bandwidth range. The PER and higher-order mode extinction ratio of 4870, 1487, respectively. The loss remains below 0.01 dB/m when the bending radius exceeds 4.5 cm. Therefore, it is expected that this optical fiber can be used in optical devices such as fiber optic sensors, fiber optic lasers and fiber optic gyroscopes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Single mode fibers

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ROUTESENSE - An AI powered solution to Car pooling systems 10

ROUTESENSE - An AI powered solution to Car pooling systems

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Poornimathi, K. Deepak Kumar, A. Srinithi, A. Rajalakshmi Engineering College Department of Computer Science and Business Systems India

ISBN: (纸本)9798350353068

Revolutionizing the urban commuting by introducing a ground-breaking AI-powered carpooling portal. This comprehensive platform provides an ultimate solution combining all the latest technologies for transportation challenges. The system ensures effortless scalability and real-time synchronization of user profiles, tip histories and route information across devices. Using the integration of emerging technologies such as Artificial intelligence and Machine learning algorithms, commuters are given a unique advantage of having a seamless travel and an optimized cost for the travel using AI and ML models and they are matched based on their preferences, schedules and historical data that makes the travel more convenient. The system also provides enhanced route security by analyzing the geospatial information to suggest the safest and most efficient routes for carpooling groups. It also streamlines the billing process by introducing Robotic process automation (RPA), ensuring accurate and tamper-free billing process for the carpooling groups. This combination of different technologies makes carpooling into a sophisticated, seamless and efficient mode of sustainable transportation that also is secure while delivering unparalleled convenience and peace of mind to its users. © 2024 IEEE.

关键词： Robotics

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Convolutional Neural Network (CNN) Classifiers Used in Land Use/Land Cover Monitoring and Classification: A Review 23th

Convolutional Neural Network (CNN) Classifiers Used in Land ...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Prabukumar, M. Agilandeeswari, L. Prabhavalkar, Sushant Prashant School of Computer Science Engineering and Information Systems VIT Vellore India

ISBN: (纸本)9783031647758

Land Use/Land Cover Analysis is conducted to perform continuous monitoring and assessment of land resources in order to manage the Land Usage and Land Utilization and to take the right decisions to protect the environment. This article discusses various techniques/algorithms used to build robust models to perform Land Use/Land Cover Classification. The article includes various architecture models including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Residual Networks (RN), etc. It also includes various Accuracy Measurement Metrics, proposed models, and corresponding issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

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Online Mechanism Design for Differentially Private Data Acquisition 63

Online Mechanism Design for Differentially Private Data Acqu...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Anjarlekar, Ameya Etesami, Rasoul Srikant, R. Uiuc Department of Electrical and Computer Engineering United States Uiuc Faculty of Electrical and Computer Engineering and Industrial and Systems Engineering United States Uiuc Faculty of Electrical and Computer Engineering United States

ISBN: (纸本)9798350316339

We address a problem involving a buyer seeking to train a logistic regression model by acquiring data from privacy-sensitive sellers. Along with compensating the sellers for their data, the buyer provides differential privacy guarantees to them where the payments depend on the privacy guarantees. In addition, each seller has a different privacy sensitivity associated with their data, which is the cost per unit of loss of privacy. The buyer transacts sequentially with the sellers, wherein the seller will disclose their privacy sensitivity, and the buyer immediately provides a payment and guarantees differential privacy. After receiving the payment, the seller provides their data to the buyer. The buyer's goal is to optimize a weighted combination of test loss and payments, i.e., achieve a tradeoff between getting a good ML model and limiting its payments. Additionally, the buyer must design the payments and differential privacy guarantees in an online fashion. Further, the online problem is historydependent, which adds to the challenge. Consequently, we design a payment mechanism that ensures incentive compatibility and individual rationality and is asymptotically optimal. Additionally, we also provide experimental results to validate our findings. © 2024 IEEE.

关键词： Logistic regression

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Revitalizing Paddy Yields with computer Vision 2

Revitalizing Paddy Yields with Computer Vision

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Siva M.e, K.P. Vibin, K.C. Kaarnika, A. Ramkumar, T.R. Priyanka, D. SriEshwarCollege of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350327533

A groundbreaking innovation in the field of plant science revolutionizes the precise identification of nutrient deficiencies in paddy plants through meticulous leaf analysis. The methodology commences by breaking down input leaf images into manageable blocks, subsequently subjecting them to rigorous evaluation using specialized convolutional neural networks (CNNs), each meticulously trained to discern specific nutrient deficiencies. The advanced network architecture systematically scrutinizes these blocks for characteristic deficiency symptoms. To achieve an all-encompassing evaluation of the leaf's condition, the responses generated by all CNNs are aggregated, and a multi-layer perceptron synthesizes them into a singular block response. The proposed approach results in a comprehensive assessment of the overall leaf health. In a comprehensive study encompassing seven different deficiency types - including Nitrogen (N), Phosphorus (P), Potassium (K), and Zinc (Zn) deficiencies - a vast dataset comprising 2,750 carefully collected leaf images was employed. The results demonstrate the proposed method's superiority, surpassing the accuracy of expert human assessments in identifying various nutrient deficiencies. The proposed method harnesses the power of CNNs, effectively merging the image analysis, machine learning, and Convolutional Neural Networks (CNNs) to propel nutrient deficiency analysis in paddy plants to unprecedented heights. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Segmentation Leaf Analysis Machine Learning Nutrient Deficiency

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Current and Voltage Scaling for Maximum Transferable Active Power Improvement of Weak-Grid-Tied Grid-Following Inverters 10

Current and Voltage Scaling for Maximum Transferable Active ...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Zhou, Weihua Bahrani, Behrooz Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350351330

The grid-following inverter's dq admittance model manifests a negative resistance in the low-frequency range due to the phase-locked loop, potentially leading to low-frequency instabilities and limiting the maximum transferable active power (MTAP), especially in weak grid scenarios. This paper introduces a simple and efficient current and voltage scaling scheme to counteract the phase-locked loop's adverse effects, enabling the grid-following inverter to inject per-unit active power even under extremely weak grid conditions. Complemented by a detailed design procedure, this proposed method stands out for its simplicity and effectiveness. Time-domain simulations validate the proposed scheme's efficacy in MTAP enhancement. © 2024 IEEE.

关键词： Voltage scaling

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TRANSIENT STABILITY ENHANCEMENT OF GRID-FORMING TYPE-3 WIND TURBINE GENERATORS 23

TRANSIENT STABILITY ENHANCEMENT OF GRID-FORMING TYPE-3 WIND ...

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23rd Wind and Solar Integration Workshop, WIW 2024

作者： Hajtaleb, Salah Bahrani, Behrooz Electrical and Computer Systems Engineering Department Monash University Melbourne Australia

ISBN: (纸本)9781837242689

Grid-forming control is considered to be essential in the transition to renewables-dominated power systems. However, grid-forming Type-3 wind turbine generators present unique challenges due to their electromechanical coupling with the grid and the limited kinetic energy in their drive-trains. This prevents active power control methods that emulate synchronous-machine behaviour from delivering robust and stable performance in Type-3 turbines. These limitations become more evident during recovery from severe grid disturbances. To overcome these limitations, this paper proposes a lead-compensator (LC)-based approach for synchronisation and active-power control as an alternative to virtual-synchronous-generators (VSG). The proposed approach is intended to demonstrate that the system damping and the overall transient stability can be improved solely by altering the control structure. Therefore, several time-domain studies have been conducted under different grid-strength levels and current-limitation mechanisms, and the comparison of post-fault recovery performance of the two controllers are presented. The results show that excessive active power oscillations and rotor speed deviations observed with VSG control are substantially mitigated using the proposed LC approach. More importantly, this is achieved without compromising inertia and droop settings, or utilising frequency measurements. Reduced turbine mechanical stress and enhanced robustness are among the benefits of this approach. © Energynautics GmbH.

关键词： Kinetic energy

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