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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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Quantum Inspired Multiobjective Optimization in Clustered Homogeneous Wireless Sensor Networks for Improving Network Lifetime and Coverage

Quantum Inspired Multiobjective Optimization in Clustered Ho...

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International Conference on Emerging Wireless Communication Technologies and Information Security, EWCIS 2020

作者： Kanchan, Pradeep Pushparaj, D. Shetty Attea, Baraá A. Department of Computer Science and Engineering NMAMIT Nitte Nitte India Department of Mathematical and Computational Sciences NITK Surathkal India Department of Computer Science University of Baghdad Baghdad Iraq

ISBN: (纸本)9789813363922

The optimization technique in which many objectives are simultaneously optimized is called multiobjective optimization. A wireless sensor network (WSN) consists of many sensors forming a network. These sensor nodes mainly run on battery which deteriorates with time. Our aim is to optimize coverage and lifetime of the network. One of the most effective methods for minimizing energy and increasing lifetime of nodes is clustering. In this paper, we integrate the two objectives of improving network lifetime and increasing coverage. We use quantum bits or qubits in our representation instead of bits. A qubit can be in 0 state, 1 state or a super position of these two states at the same time. This is what makes quantum computing-based algorithms more powerful as we can have more diversity. The proposed algorithm, quantum inspired multiobjective evolutionary algorithm based on decomposition (QMOEAD) is compared with LEACH, SEP, NSGAII and MOEA/D on the basis of coverage and network lifetime. The results show that QMOEAD outperforms the other algorithms mentioned above. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Matching individual Ladoga ringed seals across short-term image sequences (vol 102, pg 957, 2022)

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MAMMALIAN BIOLOGY 2022年第3期102卷 1045-1045页

作者： Nepovinnykh, E. Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering School of Engineering Science Lappeenranta-Lahti University of Technology LUT P.O.Box 20 53851 Lappeenranta Finland Department of Artificial Intelligence Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University Polytechnicheskaya 29 Saint Petersburg Russian Federation 195251 Department of Computer Science and Computational Experiment Southern Federal University Rostov-on-Don Russian Federation 344006 Interregional Charitable Public Organization “Biologists for Nature Conservation” (BFNC) 24 line 3-7 Saint Petersburg Russian Federation 199106

Automated wildlife reidentification has attracted increasing attention in recent years as it provides a non-invasive tool to identify and to track individual wild animals over time. In this paper, the first steps are taken towards the automatic photo-identification of the Ladoga ringed seals (Pusa hispida ladogensis). A method is proposed that takes a sequence of images, each containing multiple individuals as the input, and produces cropped images of seals grouped based on one certain individual per group. The method starts by detecting each seal from the images and proceeds to matching the individual seals between the images. It is shown that high grouping accuracy can be obtained with a general-purpose image retrieval method on an image sequence taken from the same location within a relatively short period of time. Each resulting group contains multiple images of one individual with slightly different variations, for example, in pose and illumination. Utilizing these images simultaneously provides more information for the individual re-identification compared to the traditional approach, i.e., which utilizes just one image at a time. It is further demonstrated that a convolutional neural network based method can be used to extract the unique pelage patterns of the seals despite the low contrast. Finally, a method is proposed and experiments with the novel Ladoga ringed seals data are carried out to provide a proof-of-concept for the individual re-identification.

关键词： Animal re-identification Convolutional neural networks Instance segmentation Ladoga ringed seal Photo-identification

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A survey of load balancing and implementation of clustering-based approach for clouds

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Recent Advances in computer science and Communications 2021年第3期14卷 669-677页

作者： Sharma, Anju Pandey, Rohit Singh, Simar P. Kumar, Rajesh Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Bathinda Punjab India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala Punjab India

Background: Generally, it is observed that there is not a single algorithm that classifies the task using Quality of Service (QoS) parameters requested by the task but instead focuses on clas-sifying resources and balancing the task's using the availability of resources. In past literature, authors divided the load balancing solutions in three main parts workload estimation, decision making, task transferring. Workload estimation deals with identifying requirements for the incoming tasks on the system. Decision making is done to analyze that whether or not load balancing should be performed for the given node. If the decision for load balancing has been made then third step deals with transferring task to appropriate node to reach a saturation point where the system will be in the stable state. Objective: To address this issue, our approach is more focused upon on workload estimation and its main objective is to cluster the incoming heterogeneous task into generic groups. Another issue for this approach is that the client demand varies for the number of tasks. Thus, some attributes may be much more critical to a user then the others and this demand changes from user to user. Methods: This paper classify the tasks using QoS parameters and focused on work-load estimation. The main objective is to cluster the incoming heterogeneous task into generic groups. For this, K-Medoid based clustering approach for cloud computing is devised and implemented. This approach is then compared with its different iterations to analyses the workload execution more deeply. Results: The analysis of our approach is computed using cloudsim simulator. Results and computa-tions shows that the data is very uneven in initial times, as some clusters have only four elements and others are having much more elements. Whereas after the 20th iteration data observed is more normally balanced, so the clusters formed after 20th iteration were more stable than clusters formed initially i.e. 1st iteratio

关键词： Surveys

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May the force be with you: unified force-centric pre-training for 3D molecular conformations 23

May the force be with you: unified force-centric pre-trainin...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Rui Feng Qi Zhu Huan Tran Binghong Chen Aubrey Toland Rampi Ramprasad Chao Zhang Computational Science and Engineering Georgia Institute of Technology Computer Science Department University of Illinois Urbana-Champaign Materials Science and Engineering Georgia Institute of Technology

Recent works have shown the promise of learning pre-trained models for 3D molecular representation. However, existing pre-training models focus predominantly on equilibrium data and largely overlook off-equilibrium conformations. It is challenging to extend these methods to off-equilibrium data because their training objective relies on assumptions of conformations being the local energy minima. We address this gap by proposing a force-centric pretraining model for 3D molecular conformations covering both equilibrium and off-equilibrium data. For off-equilibrium data, our model learns directly from their atomic forces. For equilibrium data, we introduce zero-force regularization and forced-based denoising techniques to approximate near-equilibrium forces. We obtain a unified pre-trained model for 3D molecular representation with over 15 million diverse conformations. Experiments show that, with our pre-training objective, we increase forces accuracy by around 3 times compared to the un-pre-trained Equivariant Transformer model. By incorporating regularizations on equilibrium data, we solved the problem of unstable MD simulations in vanilla Equivariant Transformers, achieving state-of-the-art simulation performance with 2.45 times faster inference time than NequIP. As a powerful molecular encoder, our pre-trained model achieves on-par performance with state-of-the-art property prediction tasks.

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Revolutionizing Connectivity and Data Processing in IoT Ecosystems through Edge Computing

Revolutionizing Connectivity and Data Processing in IoT Ecos...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Kumari, V. Sheeja Pillai, N. Muthuvairavan Jayarin, P.Jesu David, D. Beulah Jenin, J.S. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computational Intelligence Thandalam Tamil Nadu Chennai India R.M.D Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Information Technology Thandalam Tamil Nadu Chennai India Care College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350388602

This work, which explores the performance of the existing five IoT sensor classification algorithms and develops five more algorithms, intends to later compare the obtained classification accuracies. In this evaluation, the algorithms included are Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Naive Bayes, while the proposed ones are called EdgeNet, IoTBoost, EdgeSVM, DeepEdge, and EdgeFusionNet. The performances are evaluated using the key measures of precision and processing time. Results show that the algorithms under study are usually more accurate than well-known ones, with EdgeFusionNet reaching 94% accuracy which is the highest accuracy. In the selection of algorithms, existing ones show different performance but the Decision Tree and Naive Bayes demonstrate relatively faster processing times compared to the Random Forest and the Support Vector Machine. Yet, the proposed EdgeSVM algorithm shows almost the fastest processing time which exceeds Zokfl2 and other algorithms onference. On top of that, extra measures comprise memory usage, energy efficiency, scalability, model size, and robustness are evaluated for the suggested algorithms. Thus, the results may be inferred that the suggested algorithms provide the advanced solutions for the IoT sensor data classification, that have higher accuracy and comparable or better time complexity in comparison with the existing ones. These outcomes give useful information for researchers as well as technicians that using IoT techniques for the sake of various applications purposes. © 2024 IEEE.

关键词： Support vector machines

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Building a portable parallel asynchronous PDE solver using Kokkos

Building a portable parallel asynchronous PDE solver using K...

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Ranjan Bhat Konduri Aditya Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem.

关键词： Scalability Partial differential equations High performance computing Conferences Buildings computer architecture Benchmark testing Libraries Performance analysis Finite difference methods

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Exploring Cutting-Edge Machine Learning and Data Mining Techniques for Enhancing Big Data Management with Advanced Algorithmic Strategies for Optimal Data Processing and Analysis 2

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anitha, M. Kumari, V. Sheeja Pillai, N. Muthuvairavan Jayarin, P. Jesu David, D. Beulah Department of Artificial Intelligence & Data Science CARE College of Engineering Tamil Nadu Trichy India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India Department of Computer Science and O. Business Systems R.M.D Engineering College Tamil Nadu Thiruvallur India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Thandalam Chennai India

ISBN: (纸本)9798350383867

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting. © 2024 IEEE.

关键词： Adversarial machine learning

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Trust-aware Routing Protocol using Modified Sand Cat Swarm Optimization in Iot-Based WSN

Trust-aware Routing Protocol using Modified Sand Cat Swarm O...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Yamsani, Nagendar Anitha, D. Mahaboob John, Y.M. Jamuna Rani, M. Sowmya, M. Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Mahendra College of Engineering Ece Department Salem India Sona College of Technology Department of Ece Salem India Nitte Meenakshi Institute of Technology Department of Ai and Ds Bengaluru India

ISBN: (纸本)9798350313413

The trust-aware secure route selection leverages trust-based metrics for evaluating the credibility of nodes and routes which enables the selection of the most reliable and secure paths for data transmission. Secure routing is of major significance for preventing wireless devices from other threats and effective characteristics in WSN attain an efficient secure routing selection. This research proposed the trust secure route selection utilizing Modified Sand Cat Swarm Optimization (TSRS-MSCSO) for efficient route selection. Initially, the secure nodes are identified by using energy and delay of the fitness function. After identifying secure nodes, the route selection is performed through the Modified Sand Cat Swarm Optimization algorithm. The performance of the proposed model is estimated by metrics like packet delivery ratio, throughput, delay and energy consumption with various nodes of 50, 100, 150, and 200. The proposed model attained a high packet delivery ratio of 98.8%, less delay of 3s, high throughput of 96.27%, and less energy consumption of 2.14J for 50 nodes which ensures better performance than other existing methods like Dynamic Deep Neural Network-based Particle Swarm Optimization (DDNN-PSO), Chaotic Bumble Bees Mating Optimization- Trust Sensing Model (CBBMOR-TSM) and fuzzy TOPSIS based hybrid Jarratt Butterfly Optimization (FTOPSIS-HJBO) models. © 2023 IEEE.

关键词： Energy Efficiency Internet of Things Route Selection Sand Cat Swarm Optimization Secure nodes Throughput Wireless Sensor Networks

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NLP-IISERB@Simpletext2022: To Explore the Performance of BM25 and Transformer Based Frameworks for Automatic Simplification of Scientific Texts

NLP-IISERB@Simpletext2022: To Explore the Performance of BM2...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Saha, Sourav Roy, Dwaipayan Goud, B. Yuvaraj Reddy, Chethan S. Basu, Tanmay Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Computational and Data Sciences Indian Institute of Science Education and Research Kolkata India Department of Data Science and Engineering Indian Institute of Science Education and Research Bhopal India

CLEF SimpleText 2022 lab focuses on developing effective systems to identify relevant passages from a given set of scientific articles. The lab has organized three tasks this year. Task 1 is focused on passage retrieval from the given data for a query text. These passages can be complex and hence require further simplification to be carried out in tasks 2 and 3. The BioNLP research group at the Indian institute of science Education and Research Bhopal (IISERB) in collaboration with two different information retrieval research groups at IISER Kolkata and ISI Kolkata participated only in Task 1 of this challenge and submitted three runs using three different retrieval models. The paper explores the performance of these retrieval models for the given task. We used a standard BM25 model as our first run to identify 1000 relevant passages for each query. Moreover, the passages for each query were ranked based on their similarity scores generated by the BM25 model. For our second run, we used a BERT (Bidirectional Encoder Representations from Transformers) based re-ranking method, called as Mono-BERT to further rank the 1000 passages retrieved by our first run for each query. A pre-trained sequence to sequence model based re-ranking method, called MonoT5 was used as our third run to reorder the 1000 passages retrieved by the Mono-BERT model for each query. As the official results of this task are not yet announced, we cannot explore the performance of our submissions. However, we have manually checked the retrieved results of many queries for each run, which indicate that the performance improved from run 1 to run 2 and further to run 3. © 2022 Copyright for this paper by its authors.

关键词： Information retrieval

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