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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Abd-Elaziz, Mariam M. Elshafie, Basma M. Abdullah, Hagar Gerges, Karen Y. Ghanem, Ingy A. Fouad, Khaled M. El-Ghamry, Amir Moawad, Ibrahim F. El-Metwally, Sara New Mansoura University Faculty of Computer Science and Engineering Dakahlia Egypt Mansoura University New Mansoura University Faculty of Engineering Computer and Communication Program Faculty of Computer Science and Engineering Egypt Benha University Information Systems Faculty of Computers and Artificial Intelligence Egypt Mansoura University New Mansoura University Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Egypt School of Engineering and Computer Science University of Hertfordshire Gaf Egypt Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning © 2024 IEEE.

关键词： Adversarial machine learning

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Adopting Centroid and Bandwidth to Shape Security Line 4

Adopting Centroid and Bandwidth to Shape Security Line

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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Pratiwi, Heni Ispur Kartowisastro, Iman Herwidiana Soewito, Benfano Budiharto, Widodo Doctorate Program School of Computer Science Bina Nusantara University Jakarta Indonesia Bina Nusantara University Binus Graduate Program Doctorate Program of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta Indonesia Bina Nusantara University Computer Science Binus Graduate Program Computer Science Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350399073

The voice patterns of gender and age have been used as speaker's identity and implied into sectors such as smart cards, healthcare, banking, and other security access controls. However, age, illness, and ambient noise factors exist challenging issues of voice changes in speech recognition systems to be resolved. This paper presents experiment results of observing those issues, and its goal was obtaining the elements of voice features to secure the speaker's identity. Mel-frequency Cepstral Coefficient (MFCC) was used to conduct observations on spectral centroid and bandwidth, it has effectively performed capturing features of individual speech signal. The experiment was conducted with data split into three groups: under 13, range 13 to 19, and above 19 years old for both male and female. The results showed that both gender under 13 have centroid voices reached to 98%, age of 13 to 19 remain with centroid reached 98% of bandwidth for male and passing over 100% for female, then above 19 for both genders speak lower with centroid 50% of bandwidth, and male above age of 19 remains to keep centroid about 50% and female changes to 95%. MFCC fits to expose the speech spectrum for the purpose of an analysis frame on specific local spectral properties of voices, and the finding is to form a security line or an individual profiling adoption. © 2022 IEEE.

关键词： Bandwidth

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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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Homomorphic Encryption-Based Similarity Calculation for Mobile Data Usage Prediction

Homomorphic Encryption-Based Similarity Calculation for Mobi...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Abel C. H. Lin, Chia-Yu Lai, Yueh-Ting Chunghwa Telecom Co. Ltd. Telecommunication Laboratories Taoyuan Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350324174

For improving security, this study proposes a mobile data usage prediction method based on homomorphic encryption (HE)-based similarity calculation without plaintext storage. The cosine similarity, angular similarity, Tanimoto similarity, and Euclidean similarity based on HE according to ElGamal cryptography are proposed and combined in a machine learning method to consider the mobile data usage during the last periods for predicting the mobile data usage during the next period. © 2023 IEEE.

关键词： Forecasting

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An Adaptive and Scalable Framework for Resource-Efficient Deployment of Mixture of Experts in LLM-Based Intelligent IoT Networks

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IEEE Internet of Things Journal 2025年第13期12卷 22746-22757页

作者： Liu, Chengxu Li, Yangfan Chen, Cen Zou, Xiaofeng Kuang, HaiLan Ma, Xiaolin Lu, Zeyu Zhang, Zhaoyuan Liu, Jing Liu, Xinhua Central South University School of Computer Science and Engineering Changsha410083 China Wuhan University of Technology School of Information Engineering Wuhan430000 China South China University of Technology School of Future Technology Guangzhou510641 China Pazhou Laboratory Guangzhou510335 China School of Information Engineering of Wuhan University of Technology Wuhan430000 China Guilin University of Electronic Technology Computer and Information Security Department Guilin China Hunan Post and Telecommunication College China

The exponential growth of the Internet of Things (IoT) necessitates the deployment of large-scale models capable of processing the complex and diverse data generated by IoT devices. However, the substantial memory requirements of these models pose significant challenges, especially in scenarios where rapid decision-making and low-latency responses are critical. To address these challenges, we propose three innovative strategies for optimizing large model usage in IoT environments. The first strategy is an adaptive loading scheme, which enables dynamic loading of individual model experts. The second strategy involves an expert-by-expert loading approach, further enhancing the ability to load experts as needed, which optimizes memory usage and accelerates computations. The third strategy employs an inter-layer expert reuse mechanism, facilitating the efficient reuse of experts across different layers, thus enhancing response rates without compromising model accuracy. Importantly, these strategies can be directly applied to Mixture of Experts (MoE) large language models without requiring additional training, thereby providing a seamless and efficient solution for leveraging these models in memory-constrained, high-performance IoT environments. © 2014 IEEE.

关键词： Computational modeling Load modeling Adaptation models Memory management Loading Internet of Things Transformers Resource management Graphics processing units Decoding

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A Comparative Study of DeepLabCut and Other Open-Source Pupillometry Data Analysis Algorithms – Which to Choose?

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Machine Graphics and Vision 2024年第2期33卷 77-90页

作者： Badkul, Amitesh Mishra, Sonakshi Kommajosyula, Srinivasa P. Department of Electrical & Electronics Engineering Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Department of Pharmacy Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Ph.D. Program in Computer Science The Graduate Center The City University of New York New YorkNY United States

Pupillometry measures pupil size, and several open-source algorithms are available to analyse pupillometry data. However, only a few studies compared these algorithms’ accuracy and computational resources. This study aims to compare the accuracy of computer vision-based algorithms (Swirski, Starburst, PuRe, ElSe, ExCuSe algorithms) and the machine learning algorithm, DeepLabCut, to the double-blinded human examiners (gold-standard). Training of DeepLabCut with different architectures and a variable number of markers (2-9 markers) was done on an open-source dataset. The duration of training was statistically longer for the ResNet152 model compared to the MobileNet model. The pupil diameters in computer vision-based software such as PuRe, Starburst, and Swirski were statistically different from human measurements. MobileNet 2 and 3 marker models were the closest to the human measurements. In conclusion, this work highlights the efficiency of lower marker models based on MobileNet architecture in DeepLabCut, which consumes fewer computational resources and is more accurate. © 2024 Institute of Information Technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Adversarial machine learning

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Improved Skin Lesion Detection with Double Layer Concatenated DenseNet Using Transfer Learning and Attention Modules

Improved Skin Lesion Detection with Double Layer Concatenate...

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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： MD Mesbahuddin Hasib Md. Farukuzzaman Faruk S. M. Mahedy Hasan Md. Al Mamun Azmain Yakin Srizon Anwar Hossain Efat Md. Rakib Hossain Md. Nasif Khansur Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electronics & Telecommunication Engineering

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Skin that becomes unnaturally elevated, discoloured, or behaves in another unusual way may indicate a more serious health issue, such as skin cancer. Lesions on the skin, on the other hand, deviate from the skin's typical appearance and may point to more serious health issues. Accurate localization of diseased areas is challenging due to the complexity and expense of diagnostic tests; however, early lesion detection is essential for cancer prevention and diagnosis. The innovative automated method described in this article relies on Transfer Learning (TL), and its fundamental component is the use of DenseNet models. We have fine-tuned these models to repeatedly concatenate three attention modules, effectively extracting salient data for lesion identification. Before using concatenation to generate final features, it is necessary to fine-tune and incorporate attention mechanisms into all DenseNet models. For each version of DenseNet, the outcome of carrying out this method is Double Layer Concatenated DenseNet (DLCD). Using the ISIC 2019 dataset as an example, the proposed model reaches a remarkable 91.61% accuracy. Using GradCAM to pinpoint the precise region for categorization is one way to enhance the explainability of the architecture. We improve the study's accuracy while advancing the use of TL and other augmentation approaches. The results of this research provide techniques to improve the identification and early detection of skin lesions, perhaps resulting in a reduction in the incidence of skin cancer.

关键词： Location awareness Hands Accuracy Prevention and mitigation Transfer learning Feature extraction Skin Data models Lesions Skin cancer

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Optimal Resource Allocation for NOMA Wireless Networks

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computers, Materials & Continua 2023年第2期74卷 3249-3261页

作者： Fahad R.Albogamy M.A.Aiyashi Fazirul Hisyam Hashim Imran Khan Bong Jun Choi Turabah University College Computer Sciences ProgramTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Jazan UniversityJazanP.O.Box 218Saudi Arabia Department of Computer and Communication Systems Engineering Faculty of EngineeringUniversiti Putra Malaysia(UPM)43400SerdangMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarP.O.Box 814Pakistan School of Computer Science and Engineering Soongsil UniversitySeoulKorea

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user ***-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user *** its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power *** contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum ***,it will increase the complexity of its *** power allocation techniques will have a direct impact on the system’s *** a result,in order to boost the system capacity,an efficient power allocation mechanism must be *** research developed an efficient technique based on conjugate gradient to solve the problem of downlink power *** major goal is to maximize the users’maximum weighted sum *** suggested algorithm’s most notable feature is that it converges to the global optimal *** compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

关键词： Non-orthogonalmultiple access wireless networks power domain 5G networks

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Student Satisfaction Index in Synchronous e-Learning a Case Study 14

Student Satisfaction Index in Synchronous e-Learning a Case ...

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14th International Conference on eLearning, eLearning 2023

作者： Kasliwal, Prabha Sunil Gunjan, Reena Shete, Virendra School of Engineering MIT Art Design and Technology University Pune India School of Electronics and Telecommunication Engineering MIT Academy of Engineering India Department of Computer Science Engineering School of Engineering MIT Art Design and Technology University Pune India Department of Electronics and Telecommunication School of Engineering MIT Art Design and Technology University Pune India

Design-Based Learning (DBL) is an educational teaching pedagogy that combines inquiry-based learning with design thinking. It aims to engage students in real-time situations and hands-on learning experiences where they apply design principles to observe the activities and identify the tasks which can be done or solved by an alternate way. DBL encourages creativity, critical thinking, collaboration, and problem-solving skills. DBL experience fosters student engagement, motivation, and a sense of accomplishment, leading to greater satisfaction with the learning process. Positive emotions, such as excitement, curiosity, and enjoyment, are often associated with higher levels of satisfaction. When students feel motivated, interested, and connected to the course, they are more likely to be satisfied and enjoy their learning experience. Effective teaching practices, supportive e-learning environments, and addressing individual student queries can all contribute to enhancing student satisfaction and overall learning outcomes. In synchronous e-learning, design thinking approach is applied during the instructional design process. In this paper the systematic review and survey aimed to explore the relationship between emotions during design thinking course in synchronous e-learning and Student Satisfaction Index (SSI). The research methodology involved a mixed-methods approach, combining quantitative data through surveys and qualitative data through interviews. The activities were designed to enhance attentiveness in class, student autonomy, active involvement in every task, opportunities for collaboration, and a sense of ownership over the learning process and maximize the learning in synchronous e-learning mode. The case study findings are investigation of activities done in the "Design Thinking" course at the First year engineering. The learner’s response to the online survey as individual and as offer valuable insights that activities conducted proved to be an effective pe

关键词： Students

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Automated classification of big X-ray diffraction data using deep learning models

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npj Computational Materials 2023年第1期9卷 169-180页

作者： Jerardo E.Salgado Samuel Lerman Zhaotong Du Chenliang Xu Niaz Abdolrahim Materials Science program University of RochesterRochesterNew York 14627USA Department of Computer Science University of RochesterRochesterNew York 14627USA Department of Mechanical Engineering University of RochesterRochesterNew York 14627USA Laboratory for Laser Energetics University of RochesterRochesterNew York 14627USA

In current in situ X-ray diffraction(XRD)techniques,data generation surpasses human analytical capabilities,potentially leading to the loss of *** techniques require human intervention,and lack the performance and adaptability required for material *** the critical need for high-throughput automated XRD pattern analysis,we present a generalized deep learning model to classify a diverse set of materials’crystal systems and space *** our approach,we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal *** also employ an expedited learning technique to refine our model’s expertise to experimental *** addition,we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s *** evaluate our models using experimental data,materials unseen in training,and altered cubic crystals,where we observe state-of-the-art performance and even greater advances in space group classification.

关键词： Automate classify cubic

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