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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Choudhary, Shilpa Kumar, Sandeep Kumar, Munish Gulhane, Monali Kaliraman, Bhawna Verma, Rashmi Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram India Department of Cse Nagpur India Pune India Dpg Institute of Technology and Management Department of Computer Science and Engineering Gurugram India

ISBN: (纸本)9798350342338

Nowadays, both the corporate and public research sectors are interested in autonomous vehicles. Levels of unpredictability are the reasons self-driving cars have not yet made it to the consumer market. Multiple sensors are frequently used to address this, which helps the vehicle's system become more robust. The most often used sensors are radars, lidars, and cameras, but their costs can increase quickly, making them unaffordable in some markets. Utilizing fewer but stronger sensors for visualization could help with this. This resolves the issue of the decreased view range caused by rainy weather, a specific failure mode for camera sensors. The state-of-the-art object identification with distance estimation technique, You Only Look Once (YOLOv3), is tested with the Kalman filter and discrete wavelet transform with bilateral filtering as rain, haze, and fog removal strategies. With YOLOv3, filtered movies in daytime and dusk conditions were tested, and the results reveal that the accuracy has not increased sufficiently to be valid for use in autonomous vehicles. The study field has potential and thus indicates that more object identification and distance estimate techniques be considered as future work. © 2023 IEEE.

关键词： Fog Haze OpenCV Radars Sensors YOLOv3

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Diverse Geographical Regions Based Biodiversity Conservation by LiDAR Image with Deep Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 738-749页

作者： Babu C. N, Gireesh Reddy, Chilukala Mahender Kumar, Mungara Kiran Nagaraju, Komatigunta Maheswari, G. Uma Kiran, Ajmeera Department of Computer Science and Engineering BMS Institute of Technology & amp Management Karnataka Bengaluru India Department of Artificial Intelligence and Data Science GITAM (Deemed to be University) Hyderabad India Department of CSE School of Technology GITAM (Deemed to Be University) Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P India Department of Computer Science and Engineering RMK College of Engineering and Technology Pudhuvoyal Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Reliable data on the composition and structure of forests at various spatial scales is necessary for the conservation and monitoring of forest biodiversity. However, because field sampling techniques can be challenging, comprehensive data regarding the features of forest habitat over wide areas are frequently lacking. In order to get over this restriction, we created variables that describe the structure of the forest landscape over a wide environmental gradient using a nationally accessible light detection and ranging (LiDAR) remote sensing dataset. This research proposes a novel technique in forest management based on biodiversity analysis using LiDAR image processing by deep learning techniques. Here, the input LiDAR image has been collected as temperature pattern dataset in major deforestation regions and processed for noise removal and normalization. This image feature has been extracted using recurrent graph learning-based SegNet component analysis. Then, this extracted feature has been classified using Gaussian deep belief region-based adversarial neural networks. The experimental analysis has been carried out for various temperature pattern datasets in terms of random accuracy, average precision, recall, NSE, and AUC. The results of this study will help determine policy objectives for the restoration of mountain areas and enhance the effectiveness of monitoring damaged mountain forests. With the help of the suggested method, 96% random accuracy, 94% average precision, 49% NSE, 93% recall, and 95% AUC were achieved. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial neural networks Deep learning techniques Deforestation rate LiDAR image processing Temperature rise analysis

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Sustainable Agriculture and Climate Change: A Deep Learning Approach to Remote Sensing for Food Security Monitoring

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 709-721页

作者： Maguluri, Lakshmana Phaneendra Geetha, B. Banerjee, Sudipta Srivastava, Shambhu Sharan Nageswaran, A. Mudalkar, Pralhad K. Raj, G. Bhupal Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Chennai India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science School of Management Sciences Varanasi India Department of CSE Tagore Engineering College Chennai India Institute of Management Bharati Vidyapeeth (Deemed to Be University Kolhapur India School of Agriculture SR University Warangal India

Remote sensing technology has revolutionized agricultural monitoring, providing crucial data for managing crops and predicting yields. Traditional models, while effective, often fail to capture the complex spatial and temporal dynamics inherent in agricultural landscapes. This study introduces a sophisticated Hybrid CNN-Transformer model tailored for agricultural monitoring via remote sensing data. Unlike conventional models, this advanced approach seamlessly integrates spatial features extracted by CNNs with the broader contextual insights offered by Transformers. The model is meticulously evaluated against nine existing methodologies including standard CNNs, RNNs, LSTMs, GANs, SVMs, Decision Trees (DT), Random Forests (RF), Gradient Boosting Machines (GBM), and Logistic Regression (LR). The proposed model demonstrates superior performance across multiple metrics, achieving an unprecedented accuracy of 98.88%. Precision, recall, F1-score, and ROC-AUC scores also surpass existing models, highlighting its effectiveness in classifying and predicting diverse agricultural conditions from remote sensing data. This paper provides comprehensive comparisons across these dimensions, underscoring the advantages of integrating advanced neural network architectures for enhanced agricultural insights and decision-making. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Agricultural monitoring Convolutional neural networks (CNNs) Crop health monitoring Machine learning Precision agriculture Remote sensing Spatial feature extraction Temporal analysis Transformer models

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Adaptive Learning in Multiagent Systems Using Brain-computer Interfaces 2

Adaptive Learning in Multiagent Systems Using Brain-Computer...

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2nd International Conference on Disruptive technologies, ICDT 2024

作者： Dhatterwal, Jagjit Singh Kaswan, Kuldeep Singh Thapliyal, Nitin Malik, Kiran Koneru Lakshmaiah Education Foundation Artificial Intelligence and Data Science AP Guntur India Galgotias University Department of Computer Science and Engineering UP Greater Noida India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Matu Ram Institute of Engineering and Management Department of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350371055

Adaptive learning in multiagent systems has emerged as a promising approach to enhance agents' capabilities to adapt to dynamic environments and optimize their performance. In this research paper, we investigate the integration of brain-computer interfaces (BCIs) as a novel means to facilitate adaptive learning in multiagent systems. BCIs establish direct communication channels between the human brain and external devices, enabling real-time monitoring and interpretation of neural activity. By leveraging BCIs, agents can capture valuable cognitive signals from human operators and use this information to adapt their decision-making processes and behaviors. We present a theoretical framework that outlines the incorporation of BCIs into multiagent systems and the mechanisms for adaptive learning. Additionally, we propose a comprehensive methodology for evaluating the effectiveness of this approach. The results of our experiments demonstrate the potential of adaptive learningin multiagent systems using BCIs, paving the way for new applications in various domains, including human-machine col-laboration, assistive technologies, and interactive gaming systems. © 2024 IEEE.

关键词： Adaptive Learning Brain-computer Interfaces Human-Agent Interaction Human-Machine Collaboration Multiagent Systems

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Retraction Note: Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network (Wireless Personal Communications, (2022), 127, 1, (749-765), 10.1007/s11277-021-08403-5)

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Wireless Personal Communications 2025年第1期140卷 781-782页

作者： Setiawan, Roy Ganga, Ramakoteswara Rao Velayutham, Priya Thangavel, Kumaravel Sharma, Dilip Kumar Rajan, Regin Krishnamoorthy, Sujatha Sengan, Sudhakar Department Management Universitas Kristen Petra Surabaya Indonesia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Information Technology Adhiyamaan College of Engineering Tamil Nadu Hosur635109 India Department of Computer Science Wenzhou-Kean University Zhejiang Wenzhou325060 China Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

The Editor-in-Chief has retracted this article because of concerns regarding the substance and overall soundness of this work. An investigation conducted after its publication discovered instances of nonsensical statements, raising questions on both the authorship and validity of the content presented. Sujatha Krishnamoorthy disagrees with this retraction. Dilip Kumar Sharma and Sudhakar Sengan have not stated whether they agree or disagree with this retraction. The remaining authors have not replied to correspondence from the Publisher. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

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SFARDE: A Knowledge-Centric Semantic Strategic Framework for Heritage Artifact Recommendation Integrating Generative AI and Differential Enrichment of Ontologies 6th

SFARDE: A Knowledge-Centric Semantic Strategic Framework for...

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6th International Conference on Knowledge Graphs and Semantic Web, KGSWC 2024

作者： Chadalawada, Archit Deepak, Gerard Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9783031812200

Strategic models for heritage artifact recommendation that integrates generative AI and ontologies is the need of the hour. This paper proposes a knowledge-centric semantic model for heritage artifact recommendation which outgrows knowledge from the user perspective and the perspective of the dataset. The user query is enhanced with generated metadata from the Web 3.0 and classified using a strong Bi-LSTM model, and the artifacts are outgrown through generative AI using Gemini Pro as well as ontologies through standard knowledge repositories. This framework computes enhancing differential semantics through Second Order Co-occurrence Pointwise Mutual Information and CoSimRank used at several stages in the pipeline. The Dolphins Echolocation Algorithm is encompassed for optimization of the initial solution set to transform it into a more optimal solution set, using CoSimRank as an objective function. The overlap of generation of tags and the populated knowledge graphs in the dataset perspective to yield a matching instance reprioritization vector is the underlying strategy of the model, which uses both knowledge which is generated from the user perspective through preprocessed user queries, as well as the dataset perspective. An overall precision of 96.07%, recall of 97.07%, and F-measure of 96.57%, with a low False Discovery Rate of 0.04 makes the proposed SFARDE framework a best-in-class model for heritage artifact recommendation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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A Comprehensive Approach to Groundwater Quality Prediction and management in the State of Telangana, India, Using Stacking Ensemble Approach 4

A Comprehensive Approach to Groundwater Quality Prediction a...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Senapati, Chinmayee Patra, Kumar Janardan Swain, Satyaprakash Jayasingh, Suvendra Kumar Senapati, Swagatika Ray, Niranjan Kumar Siksha 'o' Anusandhan Deemed to Be University Institute of Technical Education and Research Department of Civil Engineering Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Bhubaneswar India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

Groundwater, a vital resource for various purposes, faces increasing threats to its quality due to factors like overuse, pollution and climate change. In the semi-arid climate of the state of Telangana, India, exacerbated by intensive agricultural practices, groundwater quality is a significant concern. This study presents a machine learning-based approach for predicting and managing groundwater quality in Telangana, utilizing a stacking ensemble method. Our proposed approach combines decision trees (DT) with meta-models such as logistic regression, K-nearest neighbors (K-NN), Bayesian ridge regression (BRR) and random forest (RF). Trained on a comprehensive dataset from the Telangana Open Data portal (2018-2021), the stacking ensemble demonstrates superior performance, achieving an accuracy of 96.07%, surpassing the best individual model (DT) at 92.68%. It not only enhances predictive capabilities but also provides insights into the spatial distribution of groundwater quality, identifying areas vulnerable to contamination. These findings offer valuable information for policymakers, environmental scientists and water resource managers, guiding informed decision-making for sustainable water resource management in Telangana. This research contributes to the ongoing discourse on environmental conservation and the imperative for effective groundwater management. © 2024 IEEE.

关键词： Decision trees

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An extended COPRAS method based on complex q-rung orthopair fuzzy 2-tuple linguistic Maclaurin symmetric mean aggregation operators

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2119-2142页

作者： Naz, Sumera Mehreen, Rida Abbas, Tahir Piñeres-Espitia, Gabriel Butt, Shariq Aziz Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan School of Mathematics Minhaj University Lahore Pakistan Department of Mathematics Abbotabad University Science and Technology Abbottabad Pakistan Universidad de la Costa Barranquilla Colombia School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan

The complex q-rung orthopair fuzzy 2-tuple linguistic set (Cq-ROFTLS), which merges the concepts of complex q-rung orthopair fuzzy sets (Cq-ROFS) and 2-tuple linguistic terms, offers significant advantages in dealing with uncertain and imprecise information during decision-making by effectively representing two-dimensional information within a single set. Notably, the Cq-ROFTLS introduces phase terms that empower experts to express their perspectives flexibly, particularly enhancing its capacity to address periodic elements. To address uncertainty, this approach employs complex values to quantify both membership and non-membership degrees within 2-tuple linguistic environment. Additionally, this research introduces the generalized Maclaurin symmetric mean (MSM) aggregation operator, specifically designed for Cq-ROFTL information. This introduces the Cq-ROFTLMSM and its dual form, the Cq-ROFTL Dual MSM (Cq-ROFTLDMSM), each carrying valuable properties. In cases where the importance of input factors varies, the study proposes the Cq-ROFTL weighted MSM (Cq-ROFTLWMSM) and its dual form, the Cq-ROFTL weighted dual MSM (Cq-ROFTLWDMSM). These operators not only make their debut but also showcase their properties and applications. They flexibly adjust to the significance of inputs, leading to a more refined decision-making process. The methodology extends to address multi-attribute group decision-making (MAGDM) within the Cq-ROFTL framework using the Complex Proportional Assessment (COPRAS) method. The introduction of new aggregation techniques further enhances this approach. A practical illustration involving the selection of the optimal bio-energy production technology (BPT) highlights the real-world effectiveness of the methodology. Through thorough comparisons and a focused exploration of advantages, the study effectively validates the merits of this approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Decision making

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An Improved Fuzzy Grounded Dynamic Package Priority Resolve and Line management System in Wireless Sensor Network

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SN computer science 2025年第2期6卷 1-12页

作者： Songare, Lokendra Singh Kumhar, Bhupchand Rajput, Ajeet Singh Shukla, Prashant Kumar Nigam, Ankita Gautam, Prakhar Ahluwalia, Ritu Department of Computer Science and Engineering Medicaps University MP Indore India Department of Computer Science & Engineering IES Institute of Technology and Management IES University MP Bhopal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L Deamed to be University Vaddeswaram Andhra Pradesh Guntur 522302 India Princeton Institute of Engineering and Technology for Women Telangana Hyderabad India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Department of CSBS Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

In Wireless Sensor Networks, it is crucial to schedule packets efficiently while taking priorities into account. It helps congestion avoidance algorithms decide on rate adjustments and packet discarding and mitigates the performance hit from WSN’s resource constraints. The current PPI method works well for use cases where the threshold values are already known. FDMP (Fuzzy Based Dynamic Packet Priority Determination and Line management) is presented in this article. FDMP is a congestion-aware packet scheduling algorithm that offers robust and universal mechanisms for dynamically determining packet priority and efficient line management. Therefore, it achieves minimum end-to-end delay for high priority packets while providing adequate fairness for low priority packets by scheduling packets within each line according to their execution time. From our simulations, we can deduce that the FDMP decreases end-to-end transmission delay and jitter while increasing packet delivery ratio, average residual energy, and throughput. Congestion in the network can be avoided through the optimal scheduling of packets to prevent packet retransmission and the removal of packets with expired deadlines from the transmission medium. When compared to the Dynamic Multilevel Priority (DMP) line scheduling approach, which is also a resource control algorithm, FDMP demonstrated superior performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DMP FDMP PPI QOS WSN

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