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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

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Numerical study of valence band states evolution in AlxGa1-xAs QDs systems

PeerJ Materials Science

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PeerJ Materials science 2024年 6卷 e32-e32页

作者： Lazarev, Mikhail Faculty of Computer Science Laboratory of Methods for Big Data Analysis HSE University Moscow Russia

Quantum dots (QDs) are very attractive nanostructures from an application point of view due to their unique optical properties. Optical properties and valence band (VB) state character was numerically investigated with respect to the effects of nanostructure geometry and composition. Numerical simulation was carried out using the Luttinger–Kohn model adapted to the particular case of QDs in inverted pyramids. We present the source code of the 4-band Luttinger–Kohn model that can be used to model AlGaAs or InGaAs nanostructures. The work focuses on the optical properties of GaAs/AlGaAs [111] QDs and quantum dot molecules (QDMs). We examine the dependence of Ground State (GS) optical properties on the structural parameters and predict optimal parameters of the QD/QDM systems to achieve dynamic control of GS polarization by an applied electric field. © 2024 Lazarev.

关键词： Polarization

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A Model Integrating the 2-Tuple Linguistic Model and the CRITIC-AHP Method for Hotel Classification

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SN computer science 2024年第1期5卷 9页

作者： Shu, Ziwei Carrasco González, Ramón Alberto García-Miguel, Javier Portela Sánchez-Montañés, Manuel Department of Statistics and Data Science Faculty of Statistics Complutense University of Madrid Avenida Puerta de Hierro s/n Madrid 28040 Spain Department of Marketing Faculty of Statistics Complutense University of Madrid Avenida Puerta de Hierro s/n Madrid 28040 Spain Department of Computer Science Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid 28049 Spain

Hotel classification is essential for hotel managers and customers. It can assist hotel managers in better understanding the needs of their customers and in improving various aspects of the hotel through relevant strategies. It also aids customers in choosing appropriate accommodations according to their preferences regarding hotel location, services, and other aspects. This paper aims to improve our previous model by incorporating expert opinions into the weight calculation, thereby increasing its practical applicability. The extended model combines the analytical hierarchy process (AHP) and the CRiteria Importance Through Intercriteria Correlation (CRITIC) methods, introducing a novel approach for calculating the weights of each aspect. The 2-tuple linguistic model is retained in the extended model to resolve the problem of information loss in linguistic information fusion. Finally, various hotel segments are obtained with the weighted K-means clustering. A dataset with over fifty million hotel reviews from TripAdvisor has been applied to evaluate the extended model. The results show that the extended model achieves denser and better separated hotel clusters than our previous model, while maintaining the same advantages. This model is more likely to help hotel managers create better strategies to tackle hotel weaknesses or gain competitive advantages, as it combines two types of weights that improve clustering results: the quantity of information in each hotel aspect and the expert judgment of each aspect's importance in hotel development. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： 2-Tuple linguistic model Analytical hierarchy process (AHP) method CRiteria Importance through Intercriteria Correlation (CRITIC) method Hotel classification Multi-criteria decision making (MCDM) Weighted K-means clustering

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation

arXiv

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arXiv 2024年

作者： Vu, Doan Nam Long Igamberdiev, Timour Habernal, Ivan Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security The University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice of granularity at which DP is applied is often neglected. For example, neural machine translation (NMT) typically operates on the sentence-level granularity. From the perspective of DP, this setup assumes that each sentence belongs to a single person and any two sentences in the training dataset are independent. This assumption is however violated in many real-world NMT datasets, e.g., those including dialogues. For proper application of DP we thus must shift from sentences to entire documents. In this paper, we investigate NMT at both the sentence and document levels, analyzing the privacy/utility trade-off for both scenarios, and evaluating the risks of not using the appropriate privacy granularity in terms of leaking personally identifiable information (PII). Our findings indicate that the document-level NMT system is more resistant to membership inference attacks, emphasizing the significance of using the appropriate granularity when working with DP.1 © 2024, CC BY-SA.

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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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Comprehensive Review of Capsule Networks with a Case Study on Potato Leaf Disease Detection Using CapsNet and Attention Mechanism 8th

Comprehensive Review of Capsule Networks with a Case Stud...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Shenbaga Moorthy, Rajalakshmi Arikumar, K.S. Prathiba, Sahaya Beni Pabitha, P. Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai600 116 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Centre for Cyber Physical Systems School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Department of Computer Technology Anna University Madras Institute of Technology Campus Chennai600044 India

ISBN: (纸本)9789819721467

Accurate solutions are needed for a variety of computer vision applications, including medical imaging, object detection, and recognition. Such complicated challenges are beyond the capabilities of artificial intelligence and machine learning, which mainly rely on data and algorithms to learn. The path to Deep Learning (DL) is thus paved. Even though deep learning is incredibly effective at solving complex problems, it is invariant, meaning that it ignores the spatial relationship between the features and instead searches for features only. This can cause the model’s performance to suffer. Additionally, because the model is invariant, gathering a large volume of training data is a difficult undertaking. To address this, the Capsule Network (CapsNet), whose performance outperforms Deep Learning techniques, was introduced. Despite its success, applications of CapsNet and working remain a mystery. Thus, the performance of CapsNet across many applications and obstacles is examined in this study together with the predecessors of CapsNet. Additionally, this paper explores the use of Capsule Networks with an attention mechanism (CapsNet-ATM) for predicting potato leaf diseases. The performance of CapsNet-ATM in this crucial agricultural application is thoroughly assessed and contrasted with the well-known models VGG-16 and VGG-19, providing insight into the system’s efficacy and promise as a reliable tool for the detection and prediction of potato leaf disease. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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When Artificial Intelligence meets Navarasas - Facial Expression Classification in Kathakali Using DSFD and CNN

When Artificial Intelligence meets Navarasas - Facial Expres...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Iyer, Ganesh Neelakanta Saengarunvong, Nattapong Le, Minh Hien School of Computing National University of Singapore Department of Computer Science Singapore Faculty of Engineering King Mongkut's University of Technology Department of Computer Engineering Thonburi Bangkok Thailand Case School of Engineering Case Western Reserve University Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798350376135

Kathakali, an ancient art form that dates back to the 17th century, is renowned for its intricate hand gestures, dance movements, musical accompaniment, elaborate costumes, and makeup. Mastering such an art form is a challenge even for the most dedicated enthusiasts, given its unconventional methods of expression. However, with current advanced technology, we can use this to aid the learning of unfamiliar subjects by classifying factors to help the new learners. In this paper, we focus on classifying one aspect of the play, which is the expressions on the actor's face given a picture using computer vision methods. To achieve the final model, we use a combination of Dual Shot Face Detector (DSFD) and Convolution Neural Network (CNN) and finally tuning up the epochs of the training process. With our dataset, the proposed model reached an accuracy of 93% among nine classes. This project hopes to introduce Kathakali to a wider audience and facilitate the learning process of this art form. © 2024 IEEE.

关键词： Convolutional neural networks

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Cycle Based Link Prediction Method for Cross-Granularity Contrast Learning

Cycle Based Link Prediction Method for Cross-Granularity Con...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Zehua Zhang Feihu Zheng Bin Wang Surui Yan Faculty of Computer Science and Technology (School of Big Data) Taiyuan University of Technology Jinzhong China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

In the process of network evolution, interactions be-tween network nodes often extend beyond simple binary interactions to include group interactions. Examples include social networks like those found on Facebook and research collaboration networks formed through scientific partnerships. Link prediction in research collaboration networks aims to forecast collaborative relationships among scientists, thereby enhancing our understanding and opti-mizing the structure and dynamics of these networks. However, net-work data are frequently sparse, and prediction based solely on at-tributes or structural features often yields inconsistent results. In complex networks, higher-order structures often contain more comprehensive information. Capturing the cycle structure in the graph enriches the representation of nodes as well as reduces the infor-mation loss during the information transfer process, thus improving the accuracy of link prediction between nodes. Specifically, our approach utilizes the cycle to calculate its significance and reconstructs ‘fat node’ information, which replaces node representations within the original cycle. We introduce a cycle scale module to de-termine the optimal cycle size for the graph. Moreover, by constructing the clustering coefficients of the multivariate cycle, we identify node and cluster aggregations within the graph, thereby reduce the complexity of finding cycle scales. Subsequently, a graph compari-son learning model is employed to establish a mapping between the newly generated ‘fat nodes’ and link relationships. Through experi-mental validation, our method demonstrates strong link prediction performance across various real network datasets.

关键词： Accuracy Social networking (online) Computational modeling Collaboration Predictive models Prediction algorithms Complexity theory Computational efficiency Convolutional neural networks Optimization

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Acceleration of FM-Index Queries Through Prefix-Free Parsing 23

Acceleration of FM-Index Queries Through Prefix-Free Parsing

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23rd International Workshop on Algorithms in Bioinformatics, WABI 2023

作者： Hong, Aaron Oliva, Marco Köppl, Dominik Bannai, Hideo Boucher, Christina Gagie, Travis Department of Computer and Information Science and Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Institut für Informatik Univeristät Münster Germany M&D Data Science Center Tokyo Medical and Dental University Japan Faculty of Computer Science Dalhousie University Halifax Canada

ISBN: (纸本)9783959772945

FM-indexes are a crucial data structure in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [6] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. Last year, Deng et al. [4] proposed parsing genomic data by induced suffix sorting, and showed the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing - which takes parameters that let us tune the average length of the phrases - instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38. And was consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it is very clear that our method accelerates the performance of count over all state-of-the-art methods with a minor increase in the memory. © Aaron Hong, Marco Oliva, Dominik Köppl, Hideo Bannai, Christina Boucher, and Travis Gagie;licensed under Creative Commons License CC-BY 4.0.

关键词： Indexing (of information)

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