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Astrometric Observations of NEA 1998 HH49 Using the Daocheng 50 cm Telescope

Research in Astronomy and Astrophysics 2024年第7期24卷 71-78页

作者： Huan Xu Xiang-Ming Cheng Yi-Gong Zhang Teng-Fei Song Zhen-Jun Zhang Qing-Yu Peng Yunnan Observatories Chinese Academy of SciencesKunming 650216China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of the Structure and Evolution of Celestial Objects Chinese Academy of SciencesKunming 650216China Engineering Laboratory on Applied Astronomical Technology of Yunnan Kunming 650216China School of Information Engineering Kunming UniversityKunming 650214China School of Computer Science and Information Engineering Anyang Institute of TechnologyAnyang 455000China Department of Computer Science Jinan UniversityGuangzhou 510632China Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan UniversityGuangzhou 510632China

This study details an astrometric observation campaign of the Near-Earth Asteroid 1998 HH49,conducted with the aim of refining our understanding of its physical *** the 50 cm telescope located at the Wumingshan Mountain in Daocheng,Sichuan,images were obtained over four nights,from 2023 October 19 to October *** observations were processed using Astrometrica software,facilitating the precise determination of the asteroid's *** observational results were compared with the ephemerides from three distinct sources to verify accuracy:the Jet Propulsion Laboratory(JPL)Horizons System,the Institut de Mécanique Céleste et de Calcul deséphémérides(IMCCE)Miriade,and the Near-Earth Objects Dynamic Site(NEODyS-2).When compared with the JPL ephemeris,a mean observed-minus-calculated(O-C)result of 0.″07 in the *** and-0.″35 in the *** was ***,the comparison with the IMCCE ephemeris yielded mean O-C results of 0.″08 in the *** and-0.″06 in the *** comparison with the NEODyS-2 ephemeris yielded the mean O-C results of 0.″06 in ***-0.″49 in *** study's findings demonstrate a general consistency between the observed data and the ephemeris predictions,with minor discrepancies observed across the data ***,both the JPL and NEODyS-2 ephemerides show that the residuals in the *** exceed those in the *** disparities may result from atmospheric differential color refraction,ephemeris discrepancies,observational errors,and other ***,it is worth noting that further investigation is required due to the potential influence of additional ***,the Daocheng 50 cm Telescope exhibits the ability to conduct high-precision positional measurements.

关键词： astrometry ephemerides time

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A novel ontological-based trust aware hybrid key management scheme (OTAHKMS) to enhance network lifetime and energy usage in wireless sensor networks (WSNs)

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International Journal of Information technology (Singapore) 2024年第3期16卷 1429-1435页

作者： Barve, Amit Pallavi, R. Deepak, Shashikant Murugan, R. Yadav, Dhananjay Singh, Ashutosh Kr. Sharma, Meenakshi Shalini, S. Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Karnataka Bangalore India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology UP Noida India Department of Education Sanskriti University UP Mathura India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India

Security, heterogeneity, and interoperability are the prominent concerns in wireless sensor networks (WSNs) with restricted amounts of resources such as energy, transmission range, and processing of packets. Moreover, secured and trusted communication among WSNs is required to enhance network lifetime and security, thereby reducing energy consumption in the network. Thus, the given paper presents an ontological-based trust aware hybrid key management scheme (OTAHKMS) for WSNs. The proposed approach is a two-fold approach, where the first phase involves the encryption process for maintaining data confidentiality and integrity, while the second phase involves the use of ontology for capturing trust relationships and representing contextual information among several nodes in the network. The performance of the proposed trusted approach is validated and compared with existing cited works based on evaluation metrics such as communication trust value (CTV), network throughput (NT), packet delivery ratio (PDR), and energy consumption (EC). The results show that the proposed ontological-based trusted approach efficiently outperforms existing protocols, thereby enhancing network lifetime and energy usage in WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Encryption Packets transmission Security Trust validation Wireless sensor networks (WSNs)

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Improved top-down attention phasmatodea population spiking neural network based multi crop recommendation framework

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Multimedia Tools and Applications 2025年第10期84卷 8071-8105页

作者： R, Chetan Ashoka, D.V. B, Ajay Prakash Department of Computer Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Information Science and Engineering JSS Academy of Technical Education Visvesvaraya Technological University Karnataka India Department of Artificial Intelligence and Machine Learning Dr. Ambedkar Institute of Technology Visvesvaraya Technological University Karnataka India

Modern agriculture refers to the contemporary practices, technologies and methods used in food production to meet the growing global demand for agricultural products. With increasing concerns about environmental degradation, soil health and resource depletion, there is an insistent need for agricultural practices that are both productive and sustainable. Multicropping mitigate the negative impacts of monoculture and reduce the reliance on synthetic inputs. This research introduces an innovative approach for multi-crop recommendation through the utilization of an integrated framework that combines advanced optimization techniques and neural networks like Enhanced top-down attention Spiking Neural Network (ETSNN). The proposed framework involves two main phases: the first phase focuses on classifying soils suitable for specific agro-ecological zones using an enhanced top-down attention spiking neural network. The second phase predicts multi-crop yields across different districts based on various environmental parameters using an Improved Phasmatodea population Evolutionary Algorithm (IPEA). Before classifying the crops feature selection takes place for the dataset by Enhanced Sea horse Optimization (ESO) method. The outputs from both phases are harmonized using a stacking-based fusion method. The developed user interface accepts real-time input for location-specific soil properties and provides crop recommendations that take into account both the input data and climate parameters. The proposed method attains 99.7% accuracy, 99.2% precision, 99% recall and 99.9% F1-score respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Titanic Survivors Prediction Analysis Using Machine Learning Algorithms 1

Titanic Survivors Prediction Analysis Using Machine Learning...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Devamane, Shridhar B. Abhijith, B.H. Purushotham, Bhushan Ravindranath, R.C. Lakshmikantha, G.C. Udayaprasad, P.K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Global Academy of Technology Department of Ai and Ml Bangalore India

ISBN: (纸本)9798350365405

Machine learning algorithms are computational models that allow computers to acquire knowledge and improve performance on a task by automatically learning patterns and rules from input data provided by the user. In this work we are predicting the Titanic disaster survivors based on a particular dataset. Based on characteristics such as age, sex, ticket class, etc., we applied the Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest algorithm to identify which passenger would survive. © 2024 IEEE.

关键词： Adversarial machine learning

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Exploring CNN Architectures for Seizure Detection from MRI during Pregnancy 3

Exploring CNN Architectures for Seizure Detection from MRI d...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Nayak, Geetanjali Padhy, Neela Madhab Mishra, Tusar Kanti Giet Unviersity Dept. of Computer Science and Engineering Odisha Gunupur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798350375190

Deep Learning (DL) represents a subset that has revolutionized traditional machine learning. Unlike earlier methods relying on handcrafted feature extraction, DL automates both feature extraction and classification. This innovation has significantly advanced applications in various medical domains, including the diagnosis of epileptic seizures. This research provides a comparative overview of studies concentrating on automated epileptic seizure detection using leading convolutional neural networks (CNN) architectures. It explores diverse architectures proposed for the automatic diagnosis of epileptic seizures through MRI, encompassing an analysis of rehabilitation systems utilizing DL. The proposed work investigates the application of four primitive CNN architectures on the identification of onset of seizure. The proposed architectures are validated through benchmark MRI samples. Experimental evaluation is made on samples of pregnant females. Overall rate of accuracy stands at 85%, 80%, 90%, and 95% respectively. © 2024 IEEE.

关键词： Network architecture

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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Using Machine Learning to Forecast the Risk of Suicide Attempts over Time 1

Using Machine Learning to Forecast the Risk of Suicide Attem...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Shetty M, Swara Praveen, Varsha Chinmayi, H.S. Bhavitha, K.S. Kamal, Shoaib Rao, Trupthi Global Academy of Technology Department of Computer Science Engineering Bengaluru India Global Academy of Technology Department of Electonics and Communication Engineering Bengaluru India Global Academy of Technology Department of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350339727

Suicide is recognized as a grave public health concern affecting individuals of all ages, from children and adolescents to young adults and beyond. It represents a significant cause of mortality within this population, influenced by various factors. Conventional methods for predicting suicide attempts have restricted the accuracy and scope of detecting these perilous behaviors. To enhance the accuracy and scope of detecting suicide attempts, we utilized machine learning on electronic health records from a vast medical database. Our study involved 469 adults exhibiting suicidal tendencies, among whom 85 patients made suicide attempts (cases). Additionally, 384 participants who did not attempt suicide but had depression showed promising signs of suicidal behavior, as confirmed by expert record review. This approach surpasses the limitations of traditional methods in predicting suicide attempts. Through the development of machine learning algorithms, we achieved accurate predictions of future suicide attempts (AUC = 0.80, precision= 0.30, recall = 0.54, Fl-score = 0.32, support=13). Our findings are a significant stride towards precise and scalable detection of suicide risk. Additionally, they offer valuable insights into how the risk of suicide attempts fluctuates over time. © 2023 IEEE.

关键词： Machine learning

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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Rice Grain Analysis using Image processing

Rice Grain Analysis using Image processing

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Gad, Abhay S Bhamidipati, Kishore Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798350361537

Rice is a staple cereal grain for more than half of the world's population, segregating rice is a crucial for food security and economic reasons. Traditional methods of rice segregation are time consuming, labor intensive and subjected to human error. By implementing this methodology we can ensure that the cost paid by the consumer is justified and accurately reflects the quality of the rice. This research presents a comprehensive methodology for the automated segregation of rice grain based on detection of broken rice using Watershed algorithm. Detection of number of rice grains. For detection of broken rice we implement using Threshold algorithm. These all methodology (RAQ) (Rice Grain Segregation and Quality Assessment) make it a valuable tool for enhancing food security and quality control in the rice industry. Using the proposed (RAQ) method, we have achieved promising results in the detection of broken rice and segregation of rice. This method (RAQ) offers a powerful method for segregation of rice it can be used in rice industry for enhancing food security and quality control processes. Its efficiency, accuracy and scalability make it a valuable asset. This method can also be implemented on other grains too. © 2024 IEEE.

关键词： Grain (agricultural product)

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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