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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Ali, Asfand Irfan, Asim Raza, Ali Memon, Zainab Mehran University of Engineering and Technology Department of Telecommunication Engineering Jamshoro Pakistan Mehran University of Engineering and Technology Computer Systems Engineering Jamshoro Pakistan Quaid-e-Awam University of Engineering Science and Technology Computer Systems Engineering Nawabshah Pakistan

ISBN: (纸本)9798350373042

This research project focuses on utilizing machine learning techniques to predict loan default among applicants in the context of financial organizations. Loan approval decisions carry substantial risks, and not all applicants can be deemed trustworthy. The primary goal of this research is to determine the feasibility of accurately presenting loan defaults by analyzing a dataset sourced from *** project involves data analysis and the application of machine learning algorithms, including logistic regression, K Nearest Neighbor, and Decision tree. Implementation will be carried out using Python programming language within Visual Studio Code, with version control through GitHub, and eventual deployment on the AWS Cloud platform. The anticipated outcomes aim for high prediction accuracy and moderate training and validation data loss, contributing to more informed decision-making in the lending process. © 2024 IEEE.

关键词： Data handling

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Comprehensive Skin Disease Identification Using Augmented Data and Transfer Learning: Achieving Optimal Accuracy with Sequential Models

Comprehensive Skin Disease Identification Using Augmented Da...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Zaman, Snigdha Nur, Abdullah Hafez Butt, Usman Sulaiman, Rejwan Bin Farhan, Maruf International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh The British University in Dubai Dubai United Arab Emirates Northumbria University Department of Computer Science & Technology Northumbria United Kingdom Northumbria University Department of Computer Science and Engineering Northumbria United Kingdom

ISBN: (纸本)9798350367560

Skin diseases are a significant global concern, and effectively treating bacterial and fungal skin infections poses an enormous task. Furthermore, in more severe instances, it might occasionally result in a case of skin cancer. Diagnosing skin diseases from clinical photographs is challenging in medical image analysis. In addition, the process of diagnosing skin issues manually by medical professionals is both time-consuming and subjective. In this work, Deep learning and machine learning-based model has been designed for the identification of skin disease using the concept of transfer learning. For this, the VGG16 model, sequential, KNN, logistic regression, and decision tree classifiers are used. VGG16's basic architecture comprises 16 weight layers, with 13 convolutional and 3 completely linked. Because ability to accurately extract complex features from skin photos is a result of its simplicity and depth. The model's ability to generalize is enhanced by its pretrained training on huge image datasets such as ImageNet. Excellent performance with little medical data can be achieved by fine-tuning this pretrained model on specific skin disease datasets. Improving patient outcomes can be achieved through earlier diagnosis and treatment, which is made possible by its ability to detect subtle patterns and early indicators of skin illnesses that may not be visible to the human eye. These proposed models are evaluated on three datasets obtained from Kaggle. These datasets now include 246 of our own captured images, which are added as healthy classes. Data augmentation techniques are used to improve the randomness of the input dataset in order to ensure model stability. The sequential model works best with an overall accuracy of 96% with the Adam optimizer and 10 epochs, outperforming state-of-the-art techniques. This model will help dermatologists diagnose skin diseases early. © 2024 IEEE.

关键词： Deep learning

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal Data 4

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

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Deep Reinforcement Learning for AoI-aware Transmission Control in Energy-Harvesting Grant-Free NOMA IoT Networks 11

Deep Reinforcement Learning for AoI-aware Transmission Contr...

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11th International Symposium on telecommunication, IST 2024

作者： Sheikhi, Marzieh Hakami, Vesal Zarrabi, Houman Iran University of Science and Technology School of Computer Engineering Tehran Iran Iran Telecommunication Research Center Department of Communication Technologies Tehran Iran

ISBN: (纸本)9798350356250

The advent of Internet of Things (IoT) networks has revolutionized various real-time applications, such as healthcare, smart cities, and remote automation, by enabling seamless data exchange and real-time monitoring and control. Real-time applications require fresh data, which is quantified by a novel metric, Age of Information (AoI), defined as the time elapsed since the generation time of last successfully received data. Additionally, due to energy limitations in most IoT scenarios, the trade-off between age and energy becomes an important challenge in these networks. Motivated by the need to optimize both data freshness and energy efficiency, this paper considers a real-time scenario in energy-harvesting (EH) IoT networks that operate based on grant-free (GF) Non-Orthogonal Multiple Access (NOMA) mechanism. Given the foresighted nature of the transmission control problem due to the stochastic dynamics of the AoI and energy charging processes, we introduce a deep reinforcement learning (DRL) framework that dynamically manages transmission decisions and resource block allocation, minimizing the average sum AoI across IoT devices, while considering energy feasibility. Through extensive simulations, we demonstrate that our proposed DRL-based approach significantly outperforms existing methods in terms of expected cumulative cost, showcasing its potential for enhancing the performance of future IoT networks. © 2024 IEEE.

关键词： Resource allocation

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Advance Path Loss Model for Distance Estimation Using LoRaWAN Network's Received Signal Strength Indicator (RSSI)

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IEEE Access 2024年 12卷 83205-83216页

作者： Vo, Hoang Hoang Long Nguyen, Van Tran, Van Lic Ferrero, Fabien Lee, Fang-Yi Tsai, Meng-Hsun The University of Danang-University of Science and Technology Faculty of Advance Science and Technology Da Nang550000 Viet Nam The University of Danang-University of Science and Technology Department of Electronic and Telecommunication Engineering Da Nang550000 Viet Nam LEAT CNRS Universite Cote d'Azur Sophia Antipolis06903 France National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan

This work introduces a novel approach to improve the precision of distance estimation in localization systems by using existing LoRaWAN and RSSI-based techniques. Despite the benefits of range and power efficiency, these systems exhibit limited accuracy in practical situations. To address the limitation, this study provides an innovative technique that greatly improves the precision of distance estimations, particularly in urban environments. The fundamental basis of this approach lies in the use of a dynamic path loss model. An additional element is to accommodate the varied and dynamic conditions of signal transmission in metropolitan areas. A better Kalman filter is also used in the study. This is important because it reduces the effects of multipath fading and environmental noise that often make RSSI-based localization in LoRaWAN networks less accurate. The study further examines the influence of the environmental exponent, also known as the path loss exponent, on the RSSI results and the precision of the distance measurements. This methodology achieves the average error under 1 meters for indoor environments and under 7 meters for outdoor environments. Finally, the Cumulative Density Function (CDF) shows 90 % of the distance estimation algorithm error for indoor environment is lower than 1.08 meters while for outdoor environment is lower than 7.55 meters. Based on these improvements, the introduced methodology not only enhances and improves existing approaches but also optimizes the precision and dependability of urban localization technologies, with substantial implications for a variety of practical applications. © 2013 IEEE.

关键词： Kalman filters

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Random Number Generator for Securing Data Exchange in Smart Factory: Recent Trends and Best Practices 3

Random Number Generator for Securing Data Exchange in Smart ...

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3rd International Conference on computer Technologies, ICCTech 2024

作者： Sama, Andi Meyliana Heryadi, Yaya Sahroni, Taufik Roni Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information System Information System Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Jakarta Computer Science Department Jakarta11530 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350395419

IT/OT convergence in the form of a Cyber-Physical System (CPS) presents an opportunity to apply recent Information Technology (IT) advancements to Operational Technology (OT). The IT/OT convergence introduces cyber security threats for OT. The OT communication protocols, for example, have limited or no security capabilities for securing data exchange beyond basic authentication. The RNG (Random Number Generator) function is crucial in PLC for producing the encryption keys or the initial seed for security algorithms. The software-based PRNG (Pseudo RNG) is the most common approach for RNG in OT, instead of hardware-based RNG. This study provides a systematic literature review of the most recent evidence in digital transformation to secure data exchange in smart factories, within OT, or across OT and IT settings. It does this by looking at the PRNG on PLC and identifying the most recent trends and best practices. The findings include up-to-date data on author countries' distribution, published journals, research trends, industries, and industry types. The findings also address whether data flow is encrypted or shared in plaintext to ensure secure data transfer between IT and legacy OT environments. This paper concludes that while there is known RNG implementation in the actual embedded system in ICS, there is no evidence in the literature for using RNG in ICS networks for PLC implementation to secure data transfer with encryption. © 2024 IEEE.

关键词： Data transfer

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High-Performance Chip Design With Parallel Architecture for Magnetic Field Imaging System

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-13页

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Lin, Chun-Hung Yuan Ze University Department of Computer Science and Engineering Taoyuan320315 Taiwan National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung912301 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan International Games System Company Ltd. New Taipei City248 Taiwan

To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetoresistance (AMR) sensors. The core controller of the proposed system was designed using a pipelined parallelization algorithm and implemented on a high-speed field-programmable gate array (FPGA) in a fully hardware-based architecture. The designed MFI system can achieve parallel acquisition of magnetic field components from the 16-channel AMR sensor array, parallel data processing, parallel data transmission, and parallel computations in a single cycle of only 4 ms. Compared with typical detection systems, the proposed solution not only exhibits superior performance but can also provide dynamic visual representations of sensed information. © 1963-2012 IEEE.

关键词： Parallel architectures

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Lightweight security Mechanism for IoT Applications using Substitution-Box 4

Lightweight security Mechanism for IoT Applications using Su...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Hanif, Fozia Shams, Rehan Waheed, Urooj University of Karachi Department of Mathematics Karachi Pakistan Sir Syed University of Engineering and Technology Karachi Department of Telecommunication Pakistan DHA Suffa University Karachi Department of Computer Science Pakistan

ISBN: (纸本)9798331518882

In recent times the use of small computing devices for gathering information from the real world is increasing day by day. The devices like wireless sensors, RIFD tags, embedded devices and IoT devices are required to transmit huge amounts of confidential data for monitoring and controlling. IoT applications have now become an important factor across various industries. That is why the security of IoT device is always been an important issue, because they transmit private and sensitive data. For handling the security issue cryptography is one the way for handling the security issues of IoT devices but sometimes cryptographic algorithms are cracked and as well as very costly. Lightweight cryptography requires less memory and less computational cost. Substitution box is a very useful concept in generating the lightweight cryptographic algorithm that provide robust security in the process of encryption. This study produces lightweight cryptographic encryption and decryption algorithm with the help of substitution boxes. The provided scheme not only provides the encryption procedure but also generating a flexible key structure which enhances the security for IoT devices where lightweight image encryption is crucial. The provided technique will be beneficial for those places where confidential public data has to be secured. The aim of this paper is to recommend effective original and straightforward techniques of encryption and decryption by using the substitution box. Experimental results show the efficiency of the suggested technique in terms of execution time and throughput. © 2025 IEEE.

关键词： Encryption algorithms

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Brain Tumor Detection Using Xception Model

Brain Tumor Detection Using Xception Model

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Philip, Joshwa Joy Rani, Ruchi Kumar, Sumit School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Science and Engineering Pune India School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India Department of Electronics and Telecommunication Maharashtra Pune India

ISBN: (纸本)9798350388800

Numerous morphological and histological characteristics of brain tumors make diagnosis and treatment very challenging. Traditional diagnosis methods often depend on the analysis of medical images, which can lead to unclear conclusions and errors. In recent years, machine learning (ML) and deep learning techniques (DL) have shown great scope in improving the detection and classification of brain tumors. These methods extract the features and classify medical imaging data, such as MRI, CT, and PET scans. Convolutional and recurrent neural networks give better performance even though there are challenges in the dataset. In the proposed work, Xception architecture is used which is a type of convolution neural network. This model is pretrained on the ImageNet dataset. The proposed model achieves 99.90% accuracy in brain tumor detection. © 2024 IEEE.

关键词： Convolutional neural networks

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High-Throughput Independent Component Analysis Processor for Full Duplex Systems

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IEEE Journal on Selected Areas in Communications 2023年第9期41卷 2821-2832页

作者： Cheng, Jen-Hao Chang, Tien-Min Shen, Chung-An Fouda, Mohammed E. Eltawil, Ahmed M. National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan University of California at Irvine Electrical Engineering and Computer Science Department IrvineCA92697 United States King Abdullah University of Science and Technology Electrical and Computer Engineering Program Thuwal23955 Saudi Arabia

This paper presents the algorithm and very-large-scale integration (VLSI) architecture of a high-throughput and highly efficient independent component analysis (ICA) processor for self-interference cancellation (SIC) in in-band full-duplex (IBFD) systems. This is the first VLSI architecture reported in the literature based on the state-of-the-art entropy bound minimization (EBM) approach. A novel ICA algorithm is presented in this paper with momentum gradient descent optimization. Simulation results show that the number of iterations for the proposed algorithm is significantly reduced compared to the conventional ICA algorithms. Furthermore, a novel early-distribution estimation scheme is proposed in the designed ICA processor to compute multiple distribution functions with low latency and low complexity. The processing flow and the efficiency for the hardware utilization are specifically designed so that the processing speed is maximized with minimum employment of hardware components. The proposed ICA processor is designed and implemented based on the application-specific-integrated circuit (ASIC) flow. The post-layout estimations show that compared with the conventional EBM-based scheme, the proposed design improves the throughput and efficiency by 30x. In addition, compared to prior designs shown in the literature, the proposed ICA processor also demonstrates a significant enhancement in terms of throughput and efficiency. © 1983-2012 IEEE.

关键词： VLSI circuits

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