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5th International Conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Neha Ahlawat, Anil Chaudhary, Himanshu Department of Computer Science Engineering KIET Group of Institutions Delhi NCR Ghaziabad India Department of Electronics and Communication Engineering KIET Group of Institutions Delhi NCR Ghaziabad India

ISBN: (纸本)9781032642987

Making supportive decisions for crop production, such as crop name recommendations and forecasts for crop production, requires machine learning implementation. Numerous machine learning classifiers and algorithms are employed in this. Climatic factors (temperature, rainfall, precipitation, sun radiation, and humidity), edaphic factors (pH, kind, and proportions of nitrogen, phosphorus, and potassium in the soil), and physiographic factors (altitude, steepness of slope, exposure to light, and wind) are some of the variables or factors that influence crop production. However, the most common variables are temperature, soil pH, precipitation, and humidity, and the most common algorithms are Examples of artificial neural networks and deep learning algorithms include Random Forest, Support Vector Machine, Linear Regression, Long Short-Term Memory, Convolution Neural Network, and Deep Neural Network. As part of this study, we reviewed the literature to ascertain the techniques and influencing factors that have been used in agricultural output prediction. © 2024 The Author(s).

关键词： Support vector machines

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Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis 6th

Enhancing Android Malware Detection: CFS Based Texture Featu...

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6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023

作者： Sharma, Tejpal Rattan, Dhavleesh Kaur, Parneet Gupta, Anuj Kumar Gill, Jagbir Singh Department of Computer Science and Engineering Chandigarh Group of Colleges Landran Punjab Mohali India Department of Computer Science and Engineering Punjabi University Punjab Patiala India Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031530845

Smartphone usage is currently expanding at an explosive rate. Malware attacks can target these smartphones. Android smart devices contribute the major share in smartphones. So, malware authors focus on developing android malicious applications. In this study, a malware detection technique that is suitable for Android smart devices is proposed. It is a static technique based on visualization of android applications. In this proposed method, android applications are converted into grayscale images. Furthermore, the image’s texture features are retrieved using Grey Level Co-occurrence Matrix (GLCM) and Local binary pattern (LBP) techniques. Then feature filtration is performed using Correlation-based Feature Selection (CFS) to reduce the feature set. The number of extracted features is reduced to thirteen by performing feature filtration after feature extraction. It is observed that when the classification is applied to selected features, better results are obtained from filtered features rather than applying classification on features extracted through GLCM or LBP alone. This technique also reduced the prediction time of the detection system due to the small number of features. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Android malware

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Enhancing urban thermal understanding via digital twin integration of thermal radiance mapping and geospatial analysis 9

Enhancing urban thermal understanding via digital twin integ...

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Remote Sensing Technologies and Applications in Urban Environments IX 2024

作者： Halder, Dyutisree Harshit, Harshit Garg, Rahul Dev Geomatics Engineering Group Department Indian Institute of Technology Roorkee India Dept. of Computer Science IT University Copenhagen Denmark

ISBN: (纸本)9781510681040

Urban environments are increasingly experiencing heat-related challenges due to climate change and rapid urbanization. To address these challenges, it is essential to improve our understanding of urban thermal dynamics. Digital twin technologies provide an innovative way to integrate multiple data sources and generate high-fidelity, real-time models of urban landscapes, allowing for deeper insights into urban heat distribution. In this study, we showcase three distinct workflows for generating digital twin environments for urban settings with varying degrees of complexity and visualization fidelity, focusing on thermal radiance mapping and geospatial analysis. The first workflow presents a low-level integration utilizing OpenStreetMap (OSM) building footprints to create a fundamental digital twin. Here, OSM data is leveraged to map urban geometries, providing the basic framework for thermal radiance analysis by associating building shapes and layouts with thermal data. This workflow is ideal for lightweight, accessible applications that focus on simple 2D urban heat mapping with readily available open-source tools. In the second workflow, we integrate Cesium Ion into the QGIS environment to create an enhanced 3D urban digital twin. Cesium Ion's 3D tiling capabilities are used to visualize urban geometries in three dimensions, enabling more detailed geospatial analysis. Combined with QGIS's robust spatial data processing, this workflow facilitates advanced urban thermal analysis, including the impact of building heights and materials on heat distribution. Finally, the third workflow demonstrates a cutting-edge approach utilizing NVIDIA Omniverse's implementation of Open Universal Scene Description (OpenUSD) to create highly detailed and realistic 3D urban environments. This state-of-the-art framework allows for the development of photorealistic urban digital twins, capable of supporting complex simulations of thermal dynamics and interactions. With high-definition re

关键词： Thermal modeling

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Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

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computers, Materials & Continua 2023年第1期74卷 141-152页

作者： Nidhi Agarwal M.Gokilavani S.Nagarajan S.Saranya Hadeel Alsolai Sami Dhahbi Amira Sayed Abdelaziz Department of Information Technology KIET Group of InstitutionsDelhi201206India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for WomenNew DelhiDelhi110006India Department of Computer Science and Engineering KL UniversityVaddeswaramAndhra Pradesh522502India Department of Electronics&Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial *** nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor *** goal of any NL scheme is to improve the localization accuracy and reduce the localization error *** this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for *** presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the *** addition,the IAOAB-NLS model is stimulated by the behaviour of *** IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the *** guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS *** resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

关键词： Aquila optimizer node localization WSN intelligent models unknown nodes anchor nodes

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Light the Sprite: Pixel Art Dynamic Light Map Generation

Light the Sprite: Pixel Art Dynamic Light Map Generation

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46th Annual Conference on European Association for computer graphics, EUROgraphics 2025

作者： Nikolov, I. Computer Graphics Group Department of Architecture Design and Media Technology Aalborg University Denmark

ISBN: (纸本)9783038682684

Correct lighting and shading are vital for pixel art design. Automating texture generation, such as normal, depth, and occlusion maps, has been a long-standing focus. We extend this by proposing a deep learning model that generates point and directional light maps from RGB pixel art sprites and specified light vectors. Our approach modifies a UNet architecture with CIN layers to incorporate positional and directional information, using ZoeDepth for training depth data. Testing on a popular pixel art dataset shows that the generated light maps closely match those from depth or normal maps, as well as from manual programs. The model effectively relights complex sprites across styles and functions in real time, enhancing artist workflows. The code and dataset are here - https://***/IvanNik17/light-sprite. © 2025 The Authors.

关键词： Mapping

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Role of internet of things in protecting different wearable gadgets and materials

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Materials Today: Proceedings 2022年 56卷 3387-3393页

作者： Prasant, P. Bhardwaj, Shashank Gupta, Monisha Srivastava, Mahima Singh, Jitendra Maurya, Rajesh Kumar Computer Science and Technology Khalsa College of Engineering and Technology Amritsar India Department of Computer Applications KIET Group of Institutions Ghaziabad India Computer Science and Engineering Chandigarh University Punjab India College Uttarakhand Dehradun India Department of Computer Science Engineering SRM Institute of Science and Technology UP Ghaziabad India MCA ABES Engineering College Ghaziabad India

Out of the most entrusted technological revolutions applied for the advancement of living standards and creation of a more convenient operation in the professional world, the Internet of Things (IoT) has been progressing at the highest projected pace. The interconnectivity of gadgets and devices over the internet to produce smarter control, communication, and a swift and easy lifestyle makes it an amazing tech introduction. However, the IOT also brings along a huge flow of privacy and security concerns whilst considering the huge inflow of data breaches and cyberattacks in the network data. The advancements in the security protocols of the data thus bring along mistrust among the users of IoT networks who might then no longer wish to be a part of this technological revolution. Therefore, it is important to maintain security assurance in the IoT based connection networks of wearable gadgets. This paper discusses the symmetric and asymmetric approaches of encryption over IoT which can help protect the network of smart phones connected to the wearable to assure a safe and smart data commute and avoid access to private data over the same internet connection. © 2021

关键词： Network security

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The Complexity of Optimizing Atomic Congestion 38

The Complexity of Optimizing Atomic Congestion

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Brand, Cornelius Ganian, Robert Kalyanasundaram, Subrahmanyam Inerney, Fionn Mc Algorithms & Complexity Theory Group Regensburg University Germany Algorithms and Complexity Group TU Wien Austria Department of Computer Science and Engineering IIT Hyderabad India

ISBN: (纸本)1577358872

Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computational complexity of computing a system-optimal set of strategies—that is, a centrally planned routing that minimizes the average cost of agents—is severely understudied in the literature. We close this gap by identifying the exact boundaries of tractability for the problem through the lens of the parameterized complexity paradigm. After showing that the problem remains highly intractable even on extremely simple networks, we obtain a set of results which demonstrate that the structural parameters which control the computational (in)tractability of the problem are not vertex-separator based in nature (such as, e.g., treewidth), but rather based on edge separators. We conclude by extending our analysis towards the (even more challenging) min-max variant of the problem. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Computational complexity

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Fast K-Medoids With the l1-Norm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1769-1777页

作者： Capo, Marco Perez, Aritz Lozano, Jose A. Basque Center of Applied Mathematics Bilbao48009 Spain University of the Basque Country UPV/EHU Intelligent Systems Group Department of Computer Science and Artificial Intelligence San Sebastián20018 Spain

K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of the obtained solutions strongly depends upon their initialization phase. In this work, we propose an algorithm for the K-medoids problem on the l1-norm/Manhattan distance with a computational complexity of O(n max lbrace log n, Kd), along with theoretical guarantees in terms of the accuracy of the obtained approximation. In addition, we propose a cheap split-merge mechanism that can be used to restart the proposed algorithm after its convergence to a fixed point. Under some mild assumptions, we prove that such a restart procedure reduces the error of the given fixed point. The work also includes an extensive experimentation, in which we compare our method to the most popular approaches for the K-medoids problem: PAM, CLARA, and Park's K-medoids. The obtained empirical results show the proposed algorithm to consistently converge to the solutions with the lowest errors, up to two orders of magnitude of relative error lower than the previously mentioned methods, while also requiring the lowest computational running times among them: up to three orders of magnitude lower. © 2020 IEEE.

关键词： Clustering algorithms

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A Multitask Deep Learning Approach for Staples and Wound Segmentation in Abdominal Post-surgical Images 1

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Fuzzy Logic and Technology, and Aggregation Operators 13th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2023, and 12th International Summer School on Aggregation Operators, AGOP 2023

作者： Moyà-Alcover, Gabriel Miró-Nicolau, Miquel Munar, Marc González-Hidalgo, Manuel Research Group Department of Mathematics and Computer Science University of the Balearic Islands Palma Spain Research Group Department of Mathematics and Computer Science University of the Balearic Islands Palma Spain University of the Balearic Islands Palma Spain Palma Spain

ISBN: (数字)9783031399657

ISBN: (纸本)9783031399640

Deep learning techniques provide a powerful and versatile tool in different areas, such as object segmentation in medical images. In this paper, we propose a network based on the U-Net architecture to perform the segmentation of wounds and staples in abdominal surgery images. Moreover, since both tasks are highly interdependent, we propose a multitask architecture that allows to simultaneously obtain, in the same network evaluation, the masks with the staples and wound location of the image. When performing this multitasking, it is necessary to formulate a global loss function that linearly combines the losses of both partial tasks. This is why the study also involves the GradNorm algorithm to determine which weight is associated to each loss function during each training step. The main conclusion of the study is that multitask segmentation offers superior performance compared to segmenting by separate tasks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Application of Artificial Intelligence to Knowledge Discovery for the Maintenance and Conservation of Botanical Gardens 7

Application of Artificial Intelligence to Knowledge Discover...

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7th Doctoral Symposium on Natural Language Processing, NLP-DS 2024

作者： Aguilar-Rajme, Claudia GPLSI research group University of Alicante Spain Department of Computer Science Technical Sciences Faculty Agricultural University of Havana Cuba

Botanical gardens play a key role in the conservation of biodiversity and generate large amounts of data related to the management and maintenance of plants on a daily basis. In this sense, the Network of Botanical Gardens in Cuba stands out, made up of 12 institutions that, among other things, have in common that they manage these data by dividing them into three main registers: Introduction Register, Living Plant Register and Herbarium. The diversity of formats and the heterogeneity of the information make it difficult to manage by the Center’s specialists, as well as to make it accessible to researchers and students in search of references. In this context, there is an opportunity to use artificial intelligence and natural language processing techniques to facilitate access to the implicit and explicit information contained in these records. To this end, it is proposed to evaluate the results of their use in order to finally obtain a product capable of using the extracted knowledge to provide recommendations and guidelines for the management and conservation of plants. © 2024 Copyright for this paper by its authors.

关键词： Plant management

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