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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Fathima, Chahath A. Suhas, G.K. Bhagappa Deepak, N.R. Research center for Computer & Information Sciences Akshaya Institute of Technology Affiliated to Visvesvaraya Technological University Tumkur India Research center for Computer & Information Sciences Brindavan College of Engineering Affiliated to visvesvaraya technological university Bangalore India Department of Computer Science Engineering Atria Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9798331300579

Although well-maintained roads are an important source of transportation, their economic impact is rarely felt in a ***, drivers who aren't convinced of their safety are seriously at risk from potholes and speed ***, poor night vision, inaccurate speed breaker signage, and the driver's carelessness all contributed to this *** of these elements harm the vehicle's suspension system in addition to having an impact on drivers who are on the *** days, cars with reduced ground clearance have trouble on uneven *** this paper, we proposed to use a Recurrent Neural network (RNN) in a machine learning method to create a device that can identify bumps and *** device uses an ESP32 GPS Speedometer to locate these bumps and potholes and transmits the position, along with speed, latitude, and longitude values, to a database via *** device also chooses to save the data it collects in the *** the aid of machine learning, it can be classified into various categories based on *** also has a speech module that allows audio notifications to be played over the *** research suggests using a Recurrent Neural network (RNN), a machine learning method, to train parameters autonomously in order to enhance vehicle suspension health and prevent driver fatigue. © Grenze Scientific Society, 2024.

关键词： Recurrent neural networks

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Automatic Pricing and Replenishment Decision of Vegetable Commodities: A Multi-model Perspective 5

Automatic Pricing and Replenishment Decision of Vegetable Co...

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5th International Seminar on Artificial Intelligence, networking and information Technology, AINIT 2024

作者： Ma, Yueyue Li, Boyu Wu, Qing School of Computer Science and Engineering Central South University Changsha China School of Electronic Information Central South University Changsha China Central South University Information and Network Center Changsha China

ISBN: (纸本)9798350385557

In fresh food supermarkets, the shelf life of vegetable products is relatively short. In order to make reliable and reasonable replenishment and pricing decisions, and achieve maximum sales revenue and profit, this article first studies the Pearson correlation coefficient model to study the relationship between various categories of vegetables and the sales volume of individual products;Then, a time series model is established from a vertical perspective combined with ARIMA, and a random forest model is established from a horizontal perspective. Then, the final strategy for predicting the daily replenishment volume and pricing strategy for the next week is obtained by integrating the two models;Finally, considering the limited sales space of vegetable supermarkets, we optimized the machine learning model using gradient descent and proposed a daily replenishment and pricing strategy to maximize profits. © 2024 IEEE.

关键词： Vegetables

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NCU-IISR: Enhancing Biomedical Question Answering with GPT-4 and Retrieval Augmented Generation in BioASQ 12b Phase B 25

NCU-IISR: Enhancing Biomedical Question Answering with GPT-4...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Chih, Bing-Chen Han, Jen-Chieh Tsai, Richard Tzong-Han Department of Computer Science and Information Engineering National Central University Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taiwan

In this paper, we introduce our system and submissions in BioASQ 12b phase b [1], highlighting a significant improvement with GPT-4 and the integration of Retrieval Augmented Generation (RAG) techniques. We describe our prompt engineering methods and the experimental procedures followed. Because GPT-4 has proven effectiveness in generating answers and its ability in the biological domain, our system utilizes GPT-4 to address biomedical question-answering (QA). Leveraging OpenAI's ChatCompletions API, we refined previous prompt engineering approaches [2] for BioASQ 11b phase b. This year, the addition of RAG techniques significantly improved the information retrieval capabilities of our system. Consequently, our latest submission employed what we experimented to be the most effective prompts and techniques, achieving excellent performance across multiple metrics in the fourth batch. © 2024 Copyright for this paper by its authors.

关键词： Question answering

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural network(CNN) RNN ResNet-50

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Performance Analysis of Intelligent Reflecting Surface-aided NR-V2X Sidelink Communications 100

Performance Analysis of Intelligent Reflecting Surface-aided...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Rehman, Abdul Di Marco, Piergiuseppe Valentini, Roberto Santucci, Fortunato University of L'Aquila Center of Excellence EX-EMERGE Department of Information Engineering Computer Science and Mathematics L'Aquila67100 Italy

ISBN: (纸本)9798331517786

Intelligent Reflective Surfaces (IRSs) enable controlling the radio propagation environment to enhance wireless communications and are particularly relevant for vehicle-to-everything (V2X) applications. This paper explores the integration of IRSs for virtual line-of-sight (VLOS) propagation in urban vehicle platoons that operate with New Radio (NR)-V2X mode 2 sidelink communications and proposes a semi-analytical framework to assess the performance of such systems. The framework considers the availability of resources, the number of interference vehicles, and their relative positions. A Nakagami-Lognormal composite channel model is assumed to describe the propagation environment. This approach provides a cost-effective and accurate estimate of packet success rates, facilitating optimal configuration and parameter adjustments for vehicle platoons. We demonstrate that under typical inter-vehicle distances and with large numbers of vehicles, VLOS communication via IRS can even surpass the performance of conventional Line-of-Sight (LOS) scenarios. © 2024 IEEE.

关键词： Radio communication

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Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Non-Hermitian polarization quantized by time-reversal symmetry

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science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 141-147页

作者： Jing Cheng Jinbing Hu Zhigao Hu Lin Chen Minghui Lu Songlin Zhuang Technical Center for Multifunctional Magneto-Optical Spectroscopy(Shanghai) Engineering Research Center of Nanophotonics&Advanced Instrument(Ministry of Education)Department of Physics and Electronic ScienceEast of China Normal UniversityShanghai200241China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China National Laboratory of Solid State Microstructures Science and Engineering Nanjing UniversityNanjing210093China

It is well known that in one-dimensional(1D) crystalline insulators,the electric polarization is a manifestation of Berry phase,which can not be quantized by time-reversal symmetry(TRS) as in Hermitian physics TRS does not induce any topological phase in one *** this paper we report that even though associated with complex eigenenergies a 1D non-Hermitian insulator obeying only TRS is capable of presenting quantized bulk *** underlying physical reason is unveiled:TRS guarantees the complex energies to come in pair(E,E*),and the corresponding decaying and amplifying wave functions also come in pair and have the same variation rate,hence,giving rise to a stable wannier *** electron transport is performed by means of charge pumping process,which verifies the physical mechanism *** last,we discuss the possible experimental implementation of the proposed model by means of twisted-π gauge flux.

关键词： non-Hermitian polarization time-reversal symmetry complex hopping

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Enhancing Wireless Sensor network Efficiency through Al-Biruni Earth Radius Optimization

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computers, Materials & Continua 2024年第6期79卷 3549-3568页

作者： Reem Ibrahim Alkanhel Doaa Sami Khafaga Ahmed Mohamed Zaki Marwa M.Eid Abdyalaziz A.Al-Mooneam Abdelhameed Ibrahim S.K.Towfek Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVirginia24060USA Delta Higher Institute for Engineering and Technology Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Artificial Intelligence and Computational Intelligence Research Center MeridenConnecticut06450USA School of ICT Faculty of EngineeringDesign and Information&Communications Technology(EDICT)Bahrain PolytechnicP.O.Box 33349Isa TownBahrain

The networks of wireless sensors provide the ground for a range of applications,including environmental moni-toring and industrial *** the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network *** infrastructure planning should be focused on increasing performance,and it should be affected by the detailed data about node *** work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location,which will contribute to better network planning and *** using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization,our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and *** implementing adaptive protocols according to varying environments and sensor constraints,our study aspires to improve overall network *** compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization,Particle Swarm Optimization,Genetic Algorithms,and Whale Optimization about performance on real-world *** the most efficient in the optimization process,Biruni displays the lowest error rate at *** two other statistical techniques,like ANOVA,are also useful in discovering the factors influencing the nature of sensor data and network-specific *** to the multi-faceted support the comprehensive approach promotes,there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be *** delivering better performance and reliability for various in-situ applications,this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

关键词： Wireless sensor networks optimization ARIMA model BER algorithm metaheuristic algorithms

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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