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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Marc Wanner Laura Lewis Chiranjib Bhattacharyya Devdatt Dubhashi Alexandru Gheorghiu Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Computer Science and Automation Indian Institute of Science Bangalore India

ISBN: (纸本)9798331314385

A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians. A number of recent works gave provably efficient machine learning (ML) algorithms for learning ground states. Specifically, Huang et al. in [1], introduced an approach for learning properties of the ground state of an n-qubit gapped local Hamiltonian H from only nO(1) data points sampled from Hamiltonians in the same phase of matter. This was subsequently improved by Lewis et al. in [2], to O(log n) samples when the geometry of the n-qubit system is known. In this work, we introduce two approaches that achieve a constant sample complexity, independent of system size n, for learning ground state properties. Our first algorithm consists of a simple modification of the ML model used by Lewis et al. and applies to a property of interest known in advance. Our second algorithm, which applies even if a description of the property is not known, is a deep neural network model. While empirical results showing the performance of neural networks have been demonstrated, to our knowledge, this is the first rigorous sample complexity bound on a neural network model for predicting ground state properties. We also perform numerical experiments on systems of up to 45 qubits that confirm the improved scaling of our approach compared to [1, 2].

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Propagation of EM Waves Through Biomaterial Based on Harvest Waste - Influence of Biomaterial Density, Moisture and Temperature 33rd

Propagation of EM Waves Through Biomaterial Based on Harvest...

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Mandrić, Vanja Rupčić, Slavko Pavković, Bogdan Bogdanović, Nataša Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Department of Information and Communication Technologies Academy of Applied Technical and Preschool Studies Niš Serbia

ISBN: (纸本)9783031805967

This paper deals with the simulation calculation of the transmission (S21) parameter in a biomaterial calculated parametrically on the basis harvest waste (residues) as a function of the density, moisture and temperature of the material. Since this material is essentially wood, empirical and semi-empirical models are used to determine the electromagnetic parameters of the wood material as a function of the density, moisture and temperature of the material according to the available literature. The structural - geometric and environmental parameters of the material that are the subject of the research are: density, moisture and temperature of the material, and the electromagnetic parameters that "describe" them are: relative permittivity Εr, loss tangent tan δ and electrical conductivity σ. The simulation model is based on a system for measuring coupling parameters in free space, which consists of two antennas, between which a 600 × 600 mm sample of the biomaterial is placed, and a network analyzer. With increasing density and moisture significantly increases the relative dielectric constant, the loss tangent and the electrical conductivity of the measured biosamples. The temperature change in a wide temperature range (20 ℃–100 ℃) has no significant influence on the electromagnetic parameters. The measured results were used to verify the simulation model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electric conductivity

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Cuisine Image Recognition and Recommender System using Convolutional Neural Network 5

Cuisine Image Recognition and Recommender System using Convo...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Wu, Kuan-Yun Kao, Hao-Xiang Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Ph.D. Program in Engineering Science Hsinchu Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung Taiwan Chung Hua University Program of Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

The fusion of technology and culinary exploration has allowed for the emergence of advanced online customer service systems. We developed a novel approach to enhance the dining experience. We used a Cuisine Image Recognition and Recommender System (CIRRS) powered by Convolutional Neural Network (CNN) to identify and suggest diverse cuisines based on visual inputs. CIRRS swiftly identified diverse cuisines from user-captured images, offering information on origin, ingredients, and variations by crawling websites. The system enhanced dining experiences by suggesting personalized menus based on individual preferences and past selections. Extensively tested across various culinary genres, CIRRS consistently demonstrated accuracy and adaptability. User feedback validated its potential to simplify dining choices and elevate satisfaction. This innovative system enriched dining experiences as a valuable tool for culinary enthusiasts, chefs, and restaurateurs, bridging the gap between technology and gastronomy, It also offers a unique way to explore Taiwanese cuisines. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system web crawler

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Vision-guided Text Mining for Unsupervised Cross-modal Hashing with Community Similarity Quantization 39

Vision-guided Text Mining for Unsupervised Cross-modal Hashi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fan, Haozhi Cao, Yuan School of Engineering and Applied Science University of Pennsylvania United States School of Computer Science and Technology Ocean University of China China

ISBN: (纸本)157735897X

Cross-modal retrieval, as an emerging field within multimedia research, has gained significant attention in recent years. Unsupervised cross-modal hashing methods are attractive due to their ability to capture latent relationships within the data without label supervision and to produce compact hash codes for high search efficiency. However, the text modality exhibits worse representation ability compared with the image modality, leading to weak guidance to construct the joint similarity matrix. Moreover, most unsupervised cross-modal hashing methods are based on pairwise similarities for training, resulting in non-aggregating data distribution in the hash space. In this paper, we propose a novel Vision-guided Text Mining for Unsupervised Cross-modal Hashing via Community Similarity Quantization, termed VTM-UCH. Specifically, we first find the one-to-one correspondence between each word and each vision (image or object) based on the Contrastive Language-Image Pre-training (CLIP) model and compute the text similarities according to the clustering of their corresponding visions. Then, we define the fine-grained object-level image similarities and design the joint similarity matrix based on the text and image similarities. Accordingly, we construct an undirected graph to compute the communities as the pseudo-centers and adjust the pairwise similarities to improve the hash codes distribution. The experimental results on two common datasets verify the accuracy improvements in comparison with state-of-the-art baselines. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Text mining

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An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc Network Using Mine Blast Algorithm

An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc ...

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2023 International Conference in Advances in Power, Signal, and Information technology, APSIT 2023

作者： Goswami, Subhrananda Mondal, Sukumar Johardar, Subhankar Das, Chandan Bikash Research Centre of Natural and Applied Sciences Raja Narendra Lal Khan Women's College Department of Computer Science India Raja Narendra Lal Khan Women's College Department of Mathematics India Haldia Institute of Technology Department of Computer Science India Tamralipta Mahavidyalaya Department of Mathematics India

ISBN: (纸本)9798350339369

The collection of wireless mobile nodes dynamically in a temporary network is termed a Mobile Ad Hoc Network (MANET). Here, energy consumption is an important complication in MANET because the mobile node doesn't offer a permanent power supply to the network, and they highly depend on batteries. The lifetime of the batteries is exhausted quickly as the nodes are traveled and change their positions frequently in the MANET network. The key factor for MANET is efficient energy to improve the network lifetime. Moreover, the efficacies of MANET are increased by different criteria like residual energy and link stability, which are always required to optimize. The major goal of multi-objective optimization issues is to reduce the energy consumption rate and also enhance the lifetime of the network. Based on the battery-dependent nodes, the efficacy of MANET is reduced. So, it is more need to select multiple optimal node disjoint paths among the destination and source to transfer the data. Further, multi-objective functions are utilized to choose the optimal path that offers the optimum outcome regarding traffic load, hop and energy consumption. So, efficient routing protocols for energy efficiency improvement in MANET are developed by utilizing the Mine Blast Algorithm (MBA). All the sensor nodes of the MANET are taken for experimentation purposes. The shortest paths in the MANET model are optimized by implementing the MBA. The parameters optimized by MBA are link cost, path loss, consumption of energy, an energy that is still available in the battery, throughput, packet energy cost, end-to-delay, routing overhead ratio, and packet delivery ratio to evaluate the overall efficiency of the network. Parametric studies of the proposed MBA are done. The developed energy-efficient MBA-MANET systems are compared with various existing techniques to verify the improved energy efficiency of the developed MBA-MANET model. © 2023 IEEE.

关键词： Sensor nodes

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K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

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computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

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BEARING INTELLIGENT FAULT DIAGNOSIS METHOD BASED ON MCNN-BILSTM-SVM 13

BEARING INTELLIGENT FAULT DIAGNOSIS METHOD BASED ON MCNN-BIL...

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13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2023

作者： Chen, Ronghua Gu, Yiliu Li, Jing Qiu, Guangqi Gu, Yingkui School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China College of Computer Scienceand Electronic Engineering Hunan University Changsha China School of Automotive Jiangxi College of Applied Technology Ganzhou China

ISBN: (纸本)9781839539848

The conventional convolutional neural network (CNN) adopts the softmax function as a classifier, which has problems of overflow and underflow. To address this issue, CNN and bi-directional long short-term memory (BiLSTM) are combined with multi-scales (MCNN-BiLSTM) as the feature extractor to improve feature extraction capability, and then support vector machine (SVM) is employed as the classifier to improve classification performance. Thus, an intelligent fault diagnosis method combined with MCNN-BiLSTM and SVM (MCNN-BiLSTM-SVM) is proposed. The wavelet threshold denoising algorithm is adopted for signal preprocessing. Transfer learning is used in MCNN-BiLSTM-SVM for different conditions. According to the bearing fault diagnosis experiments, the proposed MCNN-BiLSTM-SVM fault diagnosis method has higher diagnostic accuracy, stronger anti-noise performance and better stability under different conditions than other diagnostic methods. © The Institution of Engineering & technology 2023.

关键词： Support vector machines

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Enhancing Email Spam Detection Using Advanced AI Techniques

Enhancing Email Spam Detection Using Advanced AI Techniques

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Decision Aid sciences and Application (DASA), International Conference on

作者： Nidal A. Al-Dmour Shagufta Kousar Areeba Khan Anaum Ihsan Tahir Abbas Ali Q Saeed Department of Computer Engineering College of Engineering Mutah University Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science The TIMES Institute Multan Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan Computer Center Northern Technical University Ninevah Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This review paper investigates the advances of artificial intelligence (AI) in the field of email spam detection. The study addresses AI-based techniques used for email spam filtering by classifying threats such as keywords, content-based, sender IDs, and phrases. Email spam remains one of the most pressing issues today, with spammers sending malicious links in junk folders to compromise confidential information. As spam filtering becomes increasingly complex, AI and machine learning techniques like Naïve Bayes, Support Vector Machine (SVM), and Random Forest are commonly used to mitigate these threats. This study proposes a novel framework that combines traditional machine learning models with deep neural networks to develop a robust and flexible spam detection system. The framework is designed to enhance the detection capabilities of the classifier by applying a series of key sequential steps, each intended to improve the system's ability to distinguish between legitimate emails and spam. The paper details the methodology, providing a step-by-step explanation of each phase to demonstrate how the framework advances email spam detection.

关键词： Support vector machines Machine learning algorithms Filtering Reviews Unsolicited e-mail Biological system modeling Ecosystems Pressing Telephone sets Random forests

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Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

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computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad Zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

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Exploring the Impact of Quantum Internet on Telemedicine technology: Enhancing Healthcare Delivery, Speed, and Data Security 1

Exploring the Impact of Quantum Internet on Telemedicine Tec...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Mohammed, Awadh Ali Abdo Obaid, Abdulrahman Mohammed Hussein Alsabry, Ayman Filist Sergey, A. Alhaj, Yousif A. Department of Biomedical Engineering 21 September University of Medical and Applied Sciences Sana’a University Sana’a Yemen Department of Information Technology 21 September University of Medical and Applied Sciences Sana’a Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen Department of Biomedical Engineering Southwest State University Kursk Russia

ISBN: (纸本)9798331533557

The development of telemedicine technology indicates a shift in access to healthcare services and patients’ desire for more convenient healthcare makes it a key tool in healthcare delivery. However, the process of transmitting and processing data with a high level of speed, security, privacy, performance, and reliability is an issue of utmost importance. Recently, researchers have focused on the quantum internet, which represents the pinnacle of technological progress, opens up new horizons and forms a fundamental pillar in improving the quality of healthcare services and providing healthcare constantly. This research aims to explore the impact of the quantum internet on the functions, processes, and applications of telemedicine technology. The importance of this topic is to reshape the ways of interaction with healthcare. By exploring this impact, the improvements brought by integrating of quantum internet with telemedicine technologies are highlighted to ensure the speed of transmission, processing, security of medical information, and data exchange. The results show the quantum internet improves processes, functions, and applications of telemedicine technology to provide medical services at quantum speed. Finally, the authors discuss the integration challenges of quantum internet with telemedicine technology to provide researchers with a wide area of research. © 2024 IEEE.

关键词： Telemedicine

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