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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Akshya, J. Neelamegam, G. Sureshkumar, C. Nithya, V. Kadry, Seifedine SRM Institute of Science and Technology Department of Computational Intelligence Chennai603203 India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore641032 India KGiSL Institute of Technology Department of Information Technology Coimbatore641032 India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore641032 India Noroff University College Department of Applied Data Science Kristiansand4612 Norway

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications. © 2024 Elsevier B.V.. All rights reserved.

关键词： Trajectories

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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), Information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Dynamic Coloring on Restricted Graph Classes 1

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13th International Symposium on Algorithms and Complexity, CIAC 2023

作者： Bhyravarapu, Sriram Kumari, Swati Reddy, I. Vinod The Institute of Mathematical Sciences HBNI Chennai India Department of Electrical Engineering and Computer Science IIT Bhilai Raipur India

ISBN: (数字)9783031304484

ISBN: (纸本)9783031304477

A proper k-coloring of a graph is an assignment of colors from the set to the vertices of the graph such that no two adjacent vertices receive the same color. Given a graph, the Dynamic Coloring problem asks to find a proper k-coloring of G such that for every vertex of degree at least two, there exists at least two distinct colors appearing in the neighborhood of v. The minimum integer k such that there is a dynamic coloring of G using k colors is called the dynamic chromatic number of G and is denoted by. The problem is NP-complete in general, but solvable in polynomial time on several restricted families of graphs. In this paper, we study the problem on restricted classes of graphs. We show that the problem can be solved in polynomial time on chordal graphs and biconvex bipartite graphs. On the other hand, we show that it is NP-complete on star-convex bipartite graphs, comb-convex bipartite graphs and perfect elimination bipartite graphs. Next, we initiate the study on Dynamic Coloring from the parameterized complexity perspective. First, we show that the problem is fixed-parameter tractable when parameterized by neighborhood diversity or twin-cover. Then, we show that the problem is fixed-parameter tractable when parameterized by the combined parameters clique-width and the number of colors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coloring

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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SMT-Solving Induction Proofs of Inequalities 7

SMT-Solving Induction Proofs of Inequalities

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7th International Workshop on Satisfiability Checking and Symbolic Computation Square Workshop, SC-Square 2022

作者： Uncu, Ali K. Davenport, James H. England, Matthew Austrian Academy of Science Johann Radon Institute for Computational and Applied Mathematics Linz Austria University of Bath Faculty of Science Department of Computer Science Bath United Kingdom Coventry University Research Centre for Computational Science and Mathematical Modelling Coventry United Kingdom

This paper accompanies a new dataset of non-linear real arithmetic problems for the SMT-LIB benchmark collection. The problems come from an automated proof procedure of Gerhold-Kauers, which is well suited for solution by SMT. The problems of this type have not been tackled by SMT-solvers before. We describe the proof technique and give one new such proof to illustrate it. We then describe the dataset and the results of benchmarking. The benchmarks on the new dataset are quite different to the existing ones. The benchmarking also brings forward some interesting debate on the use/inclusion of rational functions and algebraic numbers in the SMT-LIB. © 2023 CEUR-WS. All rights reserved.

关键词： Rational functions

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Efficient Net-based Expert System for Personalized Facial Skincare Recommendations 7

Efficient Net-based Expert System for Personalized Facial Sk...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Akshya, J. Mehra, Vinit Sundarrajan, M. Sri, Pattapu Teja Choudhry, Mani Deepak SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India KGiSL Institute of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350397253

In recent years, recommender systems have gained a lot of traction in both the commercial world and the academic research community. The term 'recommendation system for facial skin care' refers to an automated system that gives consumers advice on which facial skin care products to use based on their specific skin type, skin issues, and preferences. Product recommendation systems analyze user data using a variety of methods, including collaborative filtering, content-based filtering, and machine learning algorithms. Better skincare results may be achieved through the usage of a suggestion system in which consumers receive advice related to their own skin issues. The problem with current models is that they don't adequately address users' issues and concerns while being based on collaborative or content-based filtering. In order to create an effective face skincare model, the authors of this study suggest a hybrid approach that integrates several existing methods (including KNN, CNN, Transfer Learning of Efficient Net BO, and a content-based filtering recommendation engine). The user's skin tone, skin type, and acne severity are all inputs into the algorithm, which then outputs a recommendation for the most suitable product. The results of a comparison between the suggested model and other models prove its superiority. Using a hybrid model that takes into account a number of different methodologies and user inputs, the article aspires to deliver a more all-encompassing and individualized solution for facial skincare recommendations. The proposed model has the potential to enhance the precision and efficiency of current skincare recommendation systems. The implementation of EfficientNet B0 improves the model's accuracy, which now has a validation accuracy of 80% and a training accuracy of 87.10%. © 2023 IEEE.

关键词： Recommender systems

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Methodical Tamil Character Recognition Using Fabricated CNN Model

Methodical Tamil Character Recognition Using Fabricated CNN ...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Bhavani, M. Ravikumar, S. Prithi, S. Arockia Raj, Y. Rajendiran, Babu Rajalakshmi Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Chennai India PSNA College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350348217

Recognizing text is one of the most challenging problems in computer vision. To put it simply, handwritten character recognition is the process of recognising text in photographs, papers, and other media and converting it into a form that a computer can read. You can solve this issue by employing the image classification method. We will be able to recognise the user's typed Tamil characters in this paper. We designed a brand new model, MVGG16, to handle character recognition and classification. Its basic structure is analogous to that of VGG16, but we added a few more layers to improve accuracy. So far, 156 Tamil characters have been learnt, with 350 images each class. In experimental settings, this model achieves an accuracy of 91.8%. As an input method, this work makes use of a whiteboard where the user can scribble the Tamil character to be recognised. The character will be sent for verification after the user has written it and the image has been exported. Once the model identifies the character, it will show it to the user. © 2023 IEEE.

关键词： Character recognition

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Securing unmanned aerial vehicle—heterogeneous networks using functional encryption scheme

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Multimedia Tools and Applications 2025年 1-19页

作者： Gupta, Sachin Kumar Gupta, Parul Singh, Pawan Department of Electronics and Communication Engineering Central University of Jammu UT of Jammu and Kashmir Jammu181143 India Computer Science of Engineering J.B. Institute of Technology Uttarakhand Dehradun248197 India Department of Computer Science School of Mathematics Statistics and Computational Sciences Central University of Rajasthan Ajmer- 305817 Rajasthan India

In today's scenario, as the internet expands at a faster pace, the number of data consumers also increases at an exponential rate. As the range and number of users increased, a change from traditional homogeneous networks to heterogeneous networks is in demand. However, heterogeneous networks' primary focus is to give a higher quality of service criteria to users. Furthermore, in a heterogeneous network enabled by unmanned aerial vehicles, a large number of users and devices are active. In such a diverse environment, intruder nodes may efficiently operate and transmit numerous harmful actions throughout the network. As a result, the network's entire communication is open to many types of security threats. Therefore, to secure the communication, we used the functional encryption scheme, which allows us to protect the data against intrusion attacks. The whole procedure of establishing a functional encryption scheme in unmanned aerial vehicles integrated heterogeneous network is proposed in this study in two phases: the first phase between user equipment and micro base station, and the second phase between micro base station and unmanned aerial vehicles. The proposed approach is validated using the simulation tool namely Automated Validation of Internet Security Protocols and Applications. The outcome of the security validation ensures that the proposed methodology provides the desired security and robustness to the network. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Network intrusion

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Diabetic Retinopathy Using Deep Learning

Diabetic Retinopathy Using Deep Learning

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2023 International Conference on computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Joshi, Shivani Kumar, Rajiv Rai, Praveen Kumar Garg, Sanskar G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering India

ISBN: (纸本)9798350323917

According to a recent survey by WHO, every fourth person is diabetic. It can be due to lifestyle, pollution, heredity or many other reasons. A diabetic person with the time has some threats in life. One of these threats is Retinopathy which is a well-known microvascular consequence of diabetes mellitus that poses a risk to vision is diabetic retinopathy. Worldwide, 93 million people have diabetic retinopathy at this time. Studies on the prevalence of diabetic retinopathy and risk factors are lacking. People who have diabetes may have more chances to develop diabetic retinopathy, an eye disorder that can lead to blindness. The retina's blood vessels are impacted (the light-sensitive layer of tissue in the back of your eye). It's crucial to undergo a thorough dilated eye exam at least once a year if you have diabetes. In this paper, we have used the deep learning method by taking a single snapshot of the human fundus for detecting the stage of diabetic retinopathy. We have also suggested the multistage transfer learning method, which uses comparable datasets with various labelling. With a sensitivity and specificity of 0.99, the proposed method can be used as early diagnosis of diabetic retinopathy on APTOS 2019 dataset for Detecting Blindness (13000 images). We employed a deep learning model Shapley Additive exPlanations (SHAP) for game theoretic strategies to research the diagnosis of diabetic retinopathy. In order to associate optimal credit allocation with local explanations, it makes use of the conventional Shapley values from game theory and their related extensions. To understand the predictions of our model and how to further improve its performance, in this study we will train a base ResNet50 model, evaluate it, and use the SHAP model's explain ability technique. © 2023 IEEE.

关键词： Blood vessels

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