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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Monitoring of Individual Household electrical Appliances and Prediction of Energy Consumption Using Machine Learning 5

Monitoring of Individual Household Electrical Appliances and...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Selvam, N. Seenivasan, D. Naveen, G. Sudahar, R. Janagan, P. Dhanushmathi, S. M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350385649

The study suggests a scalable method for estimating residential power usage. To increase the forecasting accuracy, an SVM (support vector machine) based technique combines many machine learning models, such as random forests, decision trees, and linear regression. Real-world data from Dutch families were used in the study, and the findings demonstrate that the suggested strategy performs better than conventional single-model techniques and attains higher accuracy in predicting energy use. The method is suited for usage in real-world applications since it is scalable and applies to huge datasets. An approach for machine learning used in load forecasting is called SVM (Support Vector Machine). Applications for long-term individual household forecasting include calculating advance payments to customers. However, there is a dearth of research on this type of forecasting;most techniques either concentrate on long-term predictions at the aggregate level or on individual family-level short-term projections. In order to close this distance, we provide a technique that uses only a few months' worth of current year's consumption data to anticipate each monthly consumption throughout the next year. Even with little data, utility suppliers may use this technique to predict any customer's consumption for the upcoming year. Data power use in the future may be predicted using linear regression. The linear regression process is used to forecast data in the future. © 2024 IEEE.

关键词： Forecasting

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Binary Codes for Correcting Asymmetric Adjacent Transpositions and Deletions

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IEEE Transactions on Communications 2024年第6期73卷 3735-3749页

作者： Wang, Shuche Vu, Van Khu Tan, Vincent Y. F. National University Of Singapore Singapore Institute Of Operations Research And Analytics Singapore National University Of Singapore Singapore Department Of Industrial Systems Engineering And Management Singapore National University Of Singapore Singapore Department Of Mathematics Singapore National University Of Singapore Singapore Department Of Electrical And Computer Engineering Singapore

Codes in the Damerau-Levenshtein metric have received some attention by the research community recently owing to their applications in DNA-based data storage. In particular, Gabrys, Yaakobi, and Milenkovic (2017) designed a length-n code correcting a single deletion and s adjacent transpositions with at most (1+2s) log n bits of redundancy. In this work, we consider a new setting where both deletions and asymmetric adjacent transpositions may occur. For asymmetric transpositions, at most s+ 0-right shifts (i.e., 01 → 10) and at most s- 0-left shifts (i.e., 10 → 01) may occur. We present several constructions of the binary codes correcting these errors in various cases. In particular, we design a code correcting a single deletion, s+ right-shift, and s- left-shift errors with at most (1 + s) log(n + s + 1) + 1 bits of redundancy where s = s+ + s-. In addition, we investigate uniquely-decodable codes correcting t 0-deletions and s adjacent transpositions with at most (t + 2s) log n + o(log n) bits of redundancy. Then, we study the code for correcting t 0-deletions, s+ right-shift, and s- left-shift errors with list-decoding algorithms. Our main contribution here is the construction of a list-decodable code with list size O(ns) and with at most (max{t, s + 1}) log n + O(1) bits of redundancy, where s = s+ + s-. We construct non-systematic codes for correcting tb blocks of 0-deletions with l-limited magnitude and s adjacent transpositions with redundancy at most (2(tb+2s)+1) log(n+1)+O(1) bits and systematic codes with at most ((2(tb + 2s) + 1)(1 + 1/(log(tbl + 4))) log(N + 1) + O(log log N) redundant bits in an N-length codeword. © 1972-2012 IEEE.

关键词： Binary codes

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Compact deep-groove-assisted bends on SiN platform for monolithic integrated laser

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Optics Express 2025年第3期33卷 4383-4395页

作者： Zhang, Ziyao Skandalos, Ilias Ma, Rui Chen, Minjia Sun, Bohao Zhong, Chuan Gardes, Frederic Cheng, Qixiang Penty, Richard Centre for Photonic Systems Electrical Engineering Division Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

To build a photonic integration platform that incorporates monolithically integrated light sources which require minimized loss and reflection, a micro-meter scale passive layer is needed in addition to the III-V layer. Low temperature plasma-enhanced chemical vapor deposition (PECVD) silicon nitride (SiN) is a promising candidate, owing to its back-end-of-line (BEOL) integration capabilities, along with its amorphous structure that allows the growth of defect-free thick layers. In view of the III-V layer stack, the optimized SiN waveguides are thick and in a rib formation. However, bends on this thick SiN platform suffer from high radiation losses, resulting in the need for a bending radius as large as 800 µm that poses challenges for compact photonic integrated circuits (PICs). This paper describes and demonstrates a novel SiN bending structure with a deep etched groove along the outer side. This structure significantly reduces the bending radii to 37 µm with a bending loss of 0.1 dB/90°. A compact micro-ring resonator (MRR) with deep etched grooves is also investigated, exhibiting a substantial enhancement in the free spectral range (FSR) compared with the rib waveguide MRRs, while offering a passband of 39.1 GHz. These notable performance improvements pave the way for compact integrated emitters on a single substrate. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Silicon nitride

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An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

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computers, Materials & Continua 2022年第2期70卷 2923-2936页

作者： Abdul Kadir Hamid Fahd N.Al-Wesabi Nadhem Nemri Ammar Zahary Imran Khan Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySanaaYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPaki

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

关键词： 5G device-to-device communication wireless networks spectrum

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TopoEEG: A Timesformer-Based Topographic Image Representation Method for Single-Trial Early Detection of P300 22

TopoEEG: A Timesformer-Based Topographic Image Representatio...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Hong, Jiazhen Najafizadeh, Laleh Rutgers University Integrated Systems and NeuroImaging Laboratory Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331520526

Detecting P300 event-related potentials (ERPs) from electroencephalography (EEG) finds applications in several domains including brain computer interfaces (BCIs). Due to the low signal-to-noise ratio (SNR), however, detecting P300 from single trials has been challenging. As a result, multiple repetitions are typically needed, extending the BCI decision time. To address this problem, in this paper, we present TopoEEG, a novel method that generates a sequence of topography-based images from EEG, to capture both spatial and temporal information. These sequences are subsequently processed using the state-of-the-art video classification technique, TimeSformer. Evaluated using the BCI Competition II (IIb) dataset, results demonstrate that TopoEEG achieves single-trial P300 detection with F1-scores of 0.6830 and 0.7134 within the first 500 ms and 600 ms, respectively. These results highlight TopoEEG's potential to enhance the efficiency and speed of BCIs by reducing the need for multiple repetitions and the time required for decision-making in single-trial P300 detection. © 2025 IEEE.

关键词： Image enhancement

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Comparative analysis of bioinspired algorithms for tuning a flatness-based controller: A ball and beam case study

Comparative analysis of bioinspired algorithms for tuning a ...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pastrana, M.A. Filho, Jose Oniram De A. Limaverde Oliveira, Luiz Henrique Bautista, Jaider Fortaleza, Eugenio Munoz, Daniel M. Department of Mechanical Engineering Brasília Brazil University of Brasília Department of Electrical Engineering Brasília Brazil University of the Amazonia Faculty of Engineering Systems Undergraduate Program Florencia Colombia

ISBN: (纸本)9798350374575

This paper provides a comprehensive comparative analysis of the performance of four bioinspired algorithms (Differential Evolution, Grasshopper Optimization Algorithm, Moth-flame Optimization, and Particle Swarm Optimization) in designing a flatness-based controller for a ball and beam system simulated in MATLAB/Simulink environment. The assessment involves the tuning of feedback gains utilizing four distinct fitness functions: Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, and an ad-hoc cost function. Hypothesis tests are employed to choose the optimal controllers from each combination for every bioinspired algorithm and a fitness function. Subsequently, a control performance analysis of these controllers for the ball and beam system is conducted, focusing on critical parameters such as overshoot, settling time, and steady-state error. This analysis demonstrates that all the controllers achieve a zero steady-state error. Noteworthy is the flatness-based controller tuned with the bioinspired Grasshopper Optimization Algorithm and utilizing the Integral Time Absolute Error fitness function, which stands out for achieving the fastest response with the lowest overshoot. This particular controller emerges as the most recommended choice for the specified application. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Mobile Edge Computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computIng reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computIng resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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SPD Miscoordination in PV systems Due to Lightning Strikes 16

SPD Miscoordination in PV Systems Due to Lightning Strikes

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Baluja, Osmel Perez Leon Avila, Bernardo Yaser Garcia Vazquez, Carlos A. Cotfas, Daniel Tudor Cotfas, Petru Adrian Breffe, Orlys E. Torres Transilvania University of Braşov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Brasov Romania Technological University of Havana Department of Electric Power Systems Faculty of Electrical Engineering Havana Cuba

ISBN: (纸本)9798350388107

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes. © 2024 IEEE.

关键词： DC transformers

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