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Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network

SN computer Science 2024年第2期5卷 223页

作者： Singh, Onkar Kashyap, Kanchan Lata Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Computing Science Engineering Indian Institute of Information Technology Kottayam Kerala India

The greatest significant contributor to cancer-related morbidity and mortality worldwide is malignant lung tumors. Lung cancer frequency has been seen to be on the rise recently. Lung cancer histopathology diagnosis is a crucial part of the patient’s treatment. The current study aims to demonstrate the efficiency of convolutional neural networks for the identification of squamous cell carcinoma and adenocarcinoma of the lung and colon by investigating the diagnosis of histopathology images. Five state-of-the-art pre-trained (ImageNet) convolutional neural network architectures, VGG-19, InceptionResNetV2, DenseNet201, EfficientNetB6, and MobileNetV2, are employed in this investigation to tri-categorize lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma), together with colon cancer images (normal and adenocarcinoma). Regularization strategies have been applied to fine-tune the learning rate for improving accuracy. The LC25000 dataset has been used to validate the proposed method. EfficientNetB6, VGG19, InceptionResNetV2, DenseNet201, and MobileNetV2 accuracy on test data is reported to be 93.12, 98.00, 97.92, 99.12, and 99.32 percent respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DenseNet201 EfficientNetB6 Histopathology images InceptionResNetV2 Lung cancer MobileNetV2 VGG19

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computer-Aided Leaf Disease Classification Based on Logistic Regression and Transfer Learning Approach

Computer-Aided Leaf Disease Classification Based on Logistic...

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International Conference on Machine vision and Augmented Intelligence, MAI 2022

作者： Saikia, Trishna Hansdah, Malho Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur India

ISBN: (纸本)9789819901883

Over 60% of the total Indian population depends on agriculture. It is one of the key fields of Indian Economy which comes up with 17%–18% of total GDP. Therefore, it is very important to detect and classify plant leaf diseases properly in agricultural field. Sometimes, it is difficult to detect and identify the disease correctly by naked eye. So, a computer-aided system is very much necessary to identify the disease accurately. Moreover, it is also helpful in large farms of crops as it reduces great work of monitoring each plant individually. In this research study, we have implemented three different techniques, among those one is Logistic Regression (LR) and another two techniques are the combination of transfer learning method with Support Vector Machine (SVM). For all the techniques, we have used Adam optimizer. For our experiment, we have utilized a subset of PlantVillage datasets. We have compared all the methods and listed in a table. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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A 4.5 μW Miniaturized 3-Channel Wireless Intra-Cardiac Acquisition System

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IEEE Transactions on Biomedical Circuits and Systems 2023年第5期17卷 1097-1110页

作者： Rezaeiyan, Yasser Koolivand, Yarallah Zamani, Milad Shoaei, Omid Akbari, Meysam Moradi, Farshad Tang, Kea-Tiong Integrated Circuits and Electronics Laboratory Department of Engineering Aarhus University Aarhus8200 Denmark Electrical Engineering KN Toosi University of Technology Tehran15418-49611 Iran Bio-Integrated Systems Lab School of Electrical and Computer Engineering University of Tehran Tehran*** Iran Department of Electrical Engineering University of Kurdistan Sanandaj*** Iran Department of Electrical Engineering National TsingHua University Hsinchu30013 Taiwan

This article presents a chip designed for wireless intra-cardiac monitoring systems. The design consists of a three-channel analog front-end, a pulse-width modulator featuring output-frequency offset and temperature calibration, and inductive data telemetry. By employing a resistance boosting technique in the instrumentation amplifier feedback, the pseudo-resistor exhibits lower non-linearity, leading to a total harmonic distortion of below 0.1%. Furthermore, the boosting technique enhances the feedback resistance, leading to a reduction in the size of the feedback capacitor and, consequently, the overall size. To make the modulator's output frequency resilient to temperature and process changes, coarse and fine-tuning algorithms are used. The front-end channel is capable of extracting the intra-cardiac signal with an effective number of bits of 8.9, while exhibiting an input-referred noise of less than 2.7 μ Vrms, and consuming 200 nW per channel. The front-end output is encoded by an ASK-PWM modulator, which drives an on-chip transmitter at 13.56 MHz. The proposed System-on-Chip (SoC) is fabricated in a 0.18 μ m standard CMOS technology and consumes 4.5 μW while occupying 1.125 mm2. © 2023 IEEE.

关键词： Transmitters

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Corona Inception and Breakdown in High-Altitude, Low-Pressure Air under Negative DC Voltage for Aircraft Electrification Applications

Corona Inception and Breakdown in High-Altitude, Low-Pressur...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Kalakonda, Sai Pavan Hamidieh, Mohammad Ghassemi, Mona The University of Texas at Dallas ZEROES lab Department of Electrical and Computer Science Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

The achievement of the net-zero emission goal is considered contingent upon the implementation of transportation electrification. Presently, all-electric commercial vehicles are restricted to land and water transport. The prospect of complete electrification of aircraft, particularly the plausible realization of all-electric aircraft (AEA), is acknowledged as being imminent. Within the electric power system (EPS) designs proposed for AEA, medium DC voltage systems in the order of up to a few tens of kilovolts are employed. Challenges manifest in the management of such voltages within a low-pressure environment. One of the emerging challenges pertains to the partial discharge (PD) behavior under DC stress, a phenomenon acknowledged to exhibit pressure- and field-dependent variations. The importance of studying the dynamics of pre-breakdown and breakdown phenomena in such environments is underscored by the fact that air serves as a crucial insulator for the EPS in AEA. Accordingly, this experimental study aims to comprehend the behavior of air subjected to high negative DC voltages and offer insights into the PD onset dynamics, specifically corona inception and breakdown. This investigation utilizes a needle-to-plane electrode configuration, examining its performance under a standard range of aviation-specific pressures across varying air gaps. The findings of this study contribute to the development of reliable electrical insulation systems tailored to withstand the demanding conditions encountered during aircraft operation. © 2024 IEEE.

关键词： Partial discharges

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Developing an MVDC Bipolar Coaxial Cable System for Lunar Power Transmission

Developing an MVDC Bipolar Coaxial Cable System for Lunar Po...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Saha, Anoy Ghassemi, Mona The University of Texas at Dallas ZEROES lab Department of Electrical and Computer Science Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

The increasing need for power on the lunar surface, expected to reach megawatt levels, aligns with NASA's goal of establishing a sustainable human presence on the Moon, able to support over 100 personnel indefinitely. As humanity explores sustained lunar habitation and aims to establish bases, efficient and reliable power transmission becomes crucial. Power lines with high power density are needed, focusing on weight and volume while ensuring safety and cost-effectiveness in the harsh lunar environment. There are three possible locations for power lines on the lunar surface: above, on, or below the surface. Due to harsh lunar conditions like temperature fluctuations, micro meteoroid impacts, and radiation exposure, the initial two methods have limited effectiveness in transferring power on the moon. Underground deployment is considered safe and convenient;however, low soil thermal conductivity could pose a challenge. However, a temperature of -23.15 °C at a depth of 30 cm proves beneficial for waste heat. This study employs COMSOL Multiphysics to propose a novel method for developing and analyzing bipolar MVDC cables for power transmission beneath the lunar surface. Designed for ±10 kV voltage and 2 MW power, these cables offer an efficient solution for lunar power transmission and are resilient to harsh conditions. © 2024 IEEE.

关键词： Waste heat

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Calculation of Television Interference (TVI) for Unconventional High Surge Impedance Loading (HSIL) Transmission Lines

Calculation of Television Interference (TVI) for Unconventio...

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2024 IEEE Conference on electrical Insulation and Dielectric Phenomena, CEIDP 2024

作者： Khan, Mushfiqul Abedin Ghassemi, Mona The University of Texas at Dallas ZEROES lab Department of Electrical and Computer Science Engineering RichardsonTX United States

ISBN: (纸本)9798350366679

Television Interference (TVI) is a pivotal factor in high voltage transmission line design. TVI occurs because of corona discharges along the transmission line, causing signal deterioration and power loss. Conventional lines have the bundled conductors symmetrically placed on a circle, and the bundle radius is chosen such that the corona discharge effects are reduced. High Surge Impedance Loading (HSIL) transmission lines consist of a higher number of subconductors and a larger bundle radius, increasing line loadability. Conductor costs are reduced by using subconductors with smaller radii. The novel 500 kV unconventional HSIL line designed in this paper exhibits a potential to enhance the natural power of the line and differ from the conventional HSIL and the conventional lines in having asymmetrical bundled conductor configurations. For this unconventional HSIL line, TVI produced due to corona discharges on and around the line are calculated and compared to the conventional line. As a result of this study, we are taking a step forward toward the development of unconventional HSIL lines for modern power systems that will revolutionize power delivery. © 2024 IEEE.

关键词： Television interference

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Model Predictive Control for Hybrid Microgrid Energy Management System Implementing V2G

Model Predictive Control for Hybrid Microgrid Energy Managem...

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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Ponce, Giovanni Mandai, Paras Lab. Department of Electrical and Computer Engineering The University of Texas El PasoTX79968 United States

This paper presents a control strategy for an energy management system (EMS) on a hybrid microgrid with the integration of a participating Vehicle-to-Grid (V2G) electric charging station. The proposed control strategy consists of an incremental Model Predictive Control (MPC) for managing the charging and discharging operations of the MG battery energy storage system (BESS), Electric Vehicle (EV) charging management system, and for providing optimal scheduling to satisfy a variable load demand in a MG connected with the distribution grid. A comparative analysis between the economic performance of MPC and Fixed Horizon Control (FHC) involving different tunning parameters is conducted to test the system performance under disturbances. Our proposed EMS control scheme provides economic scheduling dispatch by lowering the overall MG costs as well as by promoting maximum renewable energy resources (RES) self-energy consumption while considering different weather conditions. © 2024 IEEE.

关键词： Predictive control systems

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

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Artificial Intelligence in Agriculture 2024年第4期14卷 99-114页

作者： Mohammad Amin Razavi A.Pouyan Nejadhashemi Babak Majidi Hoda S.Razavi Josue Kpodo Rasu Eeswaran Ignacio Ciampitti P.V.Vara Prasad School of Electrical and Computer Engineering University of TehranTehranIran Department of Biosystems and Agricuttural Engineering Michigan State UniversityMIUSA Department of Computer Engineering Khatam UniversityTehranIran Department of Computer Science and Engineering Michigan State UniversityEast LansingMIUSA Department of Agronomy Faculty of AgricultureUniversity of JafinaKilinochchSri Lanka Department of Agronomy Kansas State UniversityManhattanKSUA Feed the Future Sustainable Intensification Innovation Lab Kansas State UniversityManhattanKSUA

In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these features influence crop yields by utilizing remotely sensed *** methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in *** optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model *** top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive *** results demonstrated that XGBoost and CatBoost perform better than the other *** introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural *** achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in *** approach distinguishes itself by creating a flexible model applicable to various crops *** integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance *** findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.

关键词： Crop yield prediction Variational auto encoder Pattern recognition on spatiotemporal and physiographical variables Synthetic tabular data generation Ensemble learning

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Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1386-1397页

作者： Vahidian, Saeed Morafah, Mahdi Chen, Chen Shah, Mubarak Lin, Bill Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States Center for Research in Computer Vision University of Central Florida OrlandoFL32816-2365 United States Center for Research in ComputerVision University of Central Florida OrlandoFL32816 United States

Though successful, federated learning (FL) presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises. To cope with the statistical heterogeneity, previous works incorporated a proximal term in local optimization or modified the model aggregation scheme at the server side or advocated clustered federated learning approaches, where the central server groups agent population into clusters with jointly trainable data distributions to take the advantage of a certain level of personalization. While effective, they lack a deep elaboration on what kind of data heterogeneity and how the data heterogeneity impacts the accuracy performance of the participating clients. In contrast to many of the prior FL approaches,wedemonstrate not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for *** are intuitive: 1) Dissimilar labels of clients (label skew) are not necessarily considered data heterogeneity, and 2) the principal angle between the clients' data subspaces spanned by their corresponding principal vectors of data is a better estimate of the data heterogeneity. Impact Statement-FL is becoming a compelling learning paradigm in the artificial intelligence (AI) area. However, FL suffers from a notorious issue which is the existence of statistical Non-IID data across different distributed clients. Due to diverse participants, severe data heterogeneity can be present in different clients data, which has been demonstrated to result in unstable and slow convergence. For instance, training a global model across hospitals to identify brain/cancer tumors where every hospitals? images come froma different domain/distribution. To simulate this statistical data heterogeneity, data heterogeneity has been simply modeled as Non-IID label skewwhich tends to be a rigid data partitioning and is hardly representative and th

关键词： Artificial intelligence

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