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25th International Workshop on Description Logics, DL 2012

作者： Queralt, Anna Artale, Alessandro Calvanese, Diego Teniente, Ernest KRDB Research Centre for Knowledge and Data Free University of Bozen-Bolzano Italy Dept. of Service and Information System Engineering UPC - BarcelonaTech Spain

UML has become a de facto standard in conceptual modeling. Class diagrams in UML allow one to model the data in the domain of interest by specifying a set of graphical constraints. However, in most cases one needs to provide the class diagram with additional semantics to completely specify the domain, and this is where OCL comes into play. While reasoning over class diagrams is decidable and has been investigated intensively, it is well known that checking the correctness of OCL constraints is undecidable. Thus, we introduce OCL-Lite, a fragment of the full OCL language and prove that reasoning over UML class diagrams with OCL-Lite constraints is in ExpTime by an encoding in the description logic ALCI. As a side result, DL techniques and tools can be used to reason on UML class diagrams annotated with arbitrary OCL-Lite constraints.

关键词： Semantics

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DigiFarmer: Precision Agriculture Empowered by Machine Learning for Sustainable Crop Management 2

DigiFarmer: Precision Agriculture Empowered by Machine Learn...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Patra, Karan Devanand Adesh Bhosle, Arya Kothari, Deepali Mukesh Nagarhalli, Tatwadarshi P. Vidyavardhini's College of Engineering and Technology Dept. Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350365269

In agriculture, the sector grapples with pressing challenges arising from climate change, water shortages, and pest infestations. Particularly affecting farmers in developing nations, these challenges amplify issues of food insecurity and poverty, necessitating innovative solutions for sustainable global agricultural progress. Addressing the complexities of climate variability, water scarcity, and pest outbreaks requires comprehensive tools and information. The absence of predictive insights intensifies crop losses and hampers overall profitability. In response, this research introduces DigiFarmer, an innovative solution employing advanced machine learning models. DigiFarmer adopts a holistic approach, encompassing crop disease detection, weed classification using Convolutional Neural Networks (CNNs), and crop quality analysis with a Light Gradient Boosting Machine (LGBM) classifier. Yield prediction is facilitated by the Random Forest algorithm, with real-time weather data integration enhancing efficacy. DigiFarmer stands out for empowering farmers with tools for sustainable and resilient agriculture. Leveraging cutting-edge technologies, DigiFarmer not only addresses immediate challenges but equips farmers with foresight to mitigate future risks. The ultimate goal is to contribute to the welfare of global farming communities by enabling informed decisions that enhance productivity, profitability, and sustainability in agriculture. © 2024 IEEE.

关键词： Profitability

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Decoding the News: An Integrated Approach for News Text Classification and Expert System Recommendations 18

Decoding the News: An Integrated Approach for News Text Clas...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prajapati, Vivek Mishra, Arpit Shinde, Prathmesh Raut, Neha Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

In the era of information overload on online platforms, efficient access to news content has become crucial for individuals leading busy lives. This paper addresses the need for categorizing vast amounts of online news data into specific sections like sports, entertainment, business, finance, etc., catering to users' personalized interests. Leveraging Machine Learning and Deep Learning algorithms, the study utilized a BBC News dataset from Kaggle and supplemented it with an additional dataset generated through web scraping using BeautifulSoup. With up to 20,000 rows covering sports, business, entertainment, politics, and technology categories, the research applied Random Forest, Support Vector Machine (SVM), Gaussian Naive Bayes (NB), and Multi-Head Attention Transformer algorithms. The findings and recommendations extend to technologies suitable for both news text classification and user-specific content recommendation in this evolving digital landscape. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： information overload machine learning media analysis news text classification trend identification

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Detection of Airports and Runways From Aerial Imagery Using Deep Learning 8

Detection of Airports and Runways From Aerial Imagery Using ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vasavi, S. Paul, Valeti Abhishek Abhilash, Vandanala Gnaneswar, Kotha Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331505462

Disaster management calls for as quick a detection of critical infrastructure-like airports and runways. Most of the traditional object detection models, which include CNNs, R-CNN s, SSDs, and earlier versions of YOLO, have high computational costs and low accuracy, particularly for smaller objects within aerial and satellite imagery. It presents the YOLOv9 object detection model with very significant improvements in feature extraction, bounding box prediction, and several modern techniques involving attention mechanisms and multi-scale feature fusion. For the purpose of improving model robustness, extensive data augmentation was adopted in training. The proposed model was tested and validated on the diverse dataset of aerial and satellite images, attaining an accuracy as high as 91.3% with a low false alarm rate of just 7.08%. These further improvements will make YOLOv9 highly scalable to support disaster management with faster infrastructure assessments, improvement in border surveillance, among others. © 2024 IEEE.

关键词： Disaster prevention

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Android Malware Detection

Android Malware Detection

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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Gagan, N. Sai Kumar, S. Keerthana, M. Vaidya, Samarth S Rastogi, Vanshika Dept. Artificial Intelligence and Data Science East Point College of Engineering and Technology Bangalore India

ISBN: (纸本)9798350311204

Since android smartphones are so popular, malware writers have found them to be lucrative targets, which poses a serious risk to user security and privacy. To address this problem, several malware detection strategies have been put out recently. In this review paper, we examine an efficient method for detecting malware on Android that makes use of machine learning techniques. Our method divides Android applications into benign and harmful categories using a combination of feature extraction methods and machine learning algorithms. Overall, our suggested method offers a dependable and efficient alternative for detecting Android malware, giving consumers a more dependable and safe environment for mobile computing. © 2023 IEEE.

关键词： Smartphones

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Analysis of route efficiency in city bus transportation vulnerable areas

Analysis of route efficiency in city bus transportation vuln...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Yoon, Namsik Choi, Innjun Kang, Sung Woo Inha University Dept. of Industrial Engineering Incheon Korea Republic of Inha University Dept. of Industrial Engineering&Data Science Incheon Korea Republic of

ISBN: (纸本)9781665480451

City buses are important transportation for citizens who especially living in transportation vulnerable areas. However, public transportation in these areas continuously decreases because of low population density. The low demand puts these areas behind service priorities, The backlog of service priorities exacerbates public transportation, hereby decreasing the population in these *** order to solve the deterioration of public transportation services in transportation vulnerable areas, the optimal route is calculated through the genetic algorithm-based approaches. The methodology automatically creates novel bus routes based on the demands and existing transportation resources. In this paper, each route is regarded as a living organism, and both demand networks and road networks are constructed as an evolutionary environment so that each organism can compete and evolve with each other. In order to verify the methodology, the case study is conducted based on actual data-sets from local government, bus companies, telecommunication company(SK Telecom) in Incheon, Republic of Korea. The second largest island (Yeongjongdo) in Incheon is selected as a transportation vulnerable area by the Incheon institute (public research center for Incheon metropolitan city). The optimal route from the methodology increases the route efficiency without supplementing additional buses to handle the demands, hereby improving the vicious cycle of transportation vulnerable areas. © 2022 IEEE.

关键词： Genetic algorithms

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Robust Sparse Approximations for Stochastic Dynamical Systems

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IFAC-PapersOnLine 2017年第1期50卷 14010-14015页

作者： Quinn, Christopher J. Pinar, Ali Gao, Jing Su, Lu School of Industrial Engineering Purdue University West LafayetteIN United States Data Sciences & Cyber Anal. Dept. Sandia National Laboratories LivermoreCA United States Dept. of Computer Science and Engineering University at Buffalo BuffaloNY United States

Inferring the exact topology of the interactions in a large, stochastic dynamical system from time-series data can often be prohibitive computationally and statistically without strong side information. One alternative is to seek approximations of the system topology that nonetheless describe the data well. In recent works, algorithms were proposed to identify sparse approximations which are optimal in terms of Kullback-Leibler divergence. Those algorithms relied on point estimates of statistics from the data. In this work, we investigate the more practical setting where point estimates are not reliable. We propose an algorithm to identify sparse, connected approximations that are robust to estimation error. © 2017

关键词： Approximation algorithms Dynamical systems Identification (control systems) Nonlinear systems Stochastic systems Time series Topology Errors in variables Model reduction Nonparametric methods Robust estimation Time series modeling

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Efficient method of formal event analysis

Efficient method of formal event analysis

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3rd International Conference on Information Computing and Applications, ICICA 2012

作者： Liu, Ying Liu, Zongtian Dept. of Knowledge Service Engineering Korea Advanced Institute of Science and Technology Korea Republic of School of Computer Engineering and Science Shanghai University Shanghai 200072 China

ISBN: (纸本)9783642340611

Based on the philosophic idea about static concept, Professor *** in Germany had provided the theory about formal concept analyses firstly in 1982, which opens out inwardness of static concept and relation between the concepts well. But the theory has evident limitation for representing dynamic event. The paper extends the theory to be a more generic model. On the basis of the definition of event proposed by us, more formalized description of event is given, a method to build formal event lattice from formal event context is suggested. Then the feasibility of the method is demonstrated from theory and instance. The model of Formal Event lattice repairs the limitation of formal concept lattice, so it can characterize the essence of the dynamic world very well. It will exert effect in the application in the fields of network resource management, Web servers, event ontology, robot control, marketing strategy, data mining and etc. © 2012 Springer-Verlag.

关键词： data mining

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Leveraging ReRAM Sequence Graphs for Efficient Mapping of Binary Adders in ReRAM Crossbars 28

Leveraging ReRAM Sequence Graphs for Efficient Mapping of Bi...

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28th International Symposium on VLSI Design and Test, VDAT 2024

作者： Dhal, Pragnya Deb, Arighna Das, Subrata Das, Debesh Kumar Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Supreme Knowledge Foundation Group of Institutions Dept. of Computer Science & Engineering Hooghly India Jadavpur University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350380101

Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and memory in high-speed applications. However, existing methods for implementing addition of two n-bit operands within ReRAM/memristor crossbar structures do not ensure minimal device utilization or minimal ReRAM/memristor read-write cycles. In this paper, we propose a novel approach for implementing n-bit addition in ReRAM crossbars to optimize the usage of the ReRAM device and minimize read-write cycles. We develop the ReRAM Sequence Graph (ReSG) to efficiently represent adder functionality and systematically allocate ReRAM devices to ReSG nodes. Furthermore, we present a scheduling mechanism for implementing ReSG nodes within ReRAM crossbars, accelerating the execution of the addition operation. Experimental evaluations show that our method surpasses current approaches, achieving better device utilization and reduced read-write cycles. © 2024 IEEE.

关键词： data transfer

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Prediction of New Associations between ncRNAs and Diseases Exploiting Multi-Type Hierarchical Clustering 28

Prediction of New Associations between ncRNAs and Diseases E...

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28th Italian Symposium on Advanced database Systems, SEBD 2020

作者： Barracchia, Emanuele Pio Pio, Gianvito D'Elia, Domenica Ceci, Michelangelo Dept. of Computer Science University of Bari Aldo Moro Bari Italy Dept. of Computer Science University of Bari Aldo Moro Bari Italy Big Data Laboratory Rome Italy Dept. of Knowledge Technologies Jouef Stefan Institute Ljubljana Slovenia

The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes. Such a complexity can be faced by representing the involved biological entities and their relationships as a network and by exploiting network-based computational approaches able to identify new associations. However, existing methods are limited to homogeneous networks or can exploit only a limited set of the features of biological entities. To overcome the limitations of existing approaches, we proposed the system LP-HCLUS, which analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts hierarchically organized, possibly overlapping, multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. Our experimental evaluation shows that, according to both quantitative (i.e., TPR@k, ROC and PR curves) and qualitative criteria, LP-HCLUS produces better results. Copyright © 2020 for this paper by its authors.

关键词： RNA

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