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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Rahman, Mohammed Ziaur Mishra, Awakash Ojha, Ananta Goswami, Shubhashish School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Using a realistic dataset based on the United Kingdom clinical exercise studies Datalink, various architectures are evolved and evaluated to decide the simplest for predicting the risk of cardiac arrest. The architectures examined include several modified lengthy short-term memory networks, multi-input/multi-output architectures, Convolutional Neural Networks (CNNs), and Residual Neural Networks. Moreover, the consequences of the architectures on the predictions are compared while the statistics are supplemented with external capabilities. Furthermore, the results recommend increasing the facts with external functions, enhancing prediction accuracy for several architectures. Via this observation, conclusions may be drawn about which architectures are satisfactory-desirable for predicting cardiac arrest chance, as well as the consequences of external capabilities on the effectiveness of the architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Exploring the Benefits of Deep Learning for Data Science Practices 15

Exploring the Benefits of Deep Learning for Data Science Pra...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Mohit Kumar Kumar, Ritesh Sinha, Deepak Kumar Senthilkumar, K.K. Dhabliya, Dharmesh Ahluwalia, Gunveen Vivekananda Global University Department of Electrical Engineering Jaipur India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

With the growing occurrence of records technology and analytics, Deep studying has become an effective device within the records technology field. Deep mastering is a subset of artificial Intelligence that uses algorithms to construct complex fashions for huge-scale records analysis. The models created through Deep studying are often extra correct than traditional analytic techniques, as they use a wide style of capabilities and labels to discover records. Deep studying models can remedy many facts and technological know-how problems, from herbal language processing to photo class to regression modeling. Deep mastering models are compelling in conditions in which a high degree of accuracy is needed and where the inputs are complicated and varied. In brief, the blessings of Deep studying for data technological know-how consist of improved accuracy, faster processing, and automated feature extraction. © 2024 IEEE.

关键词： Economic and social effects

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Combining CNN and SVM for High-Precision Wheat Disease Classification using Leaf Image Analysis 2

Combining CNN and SVM for High-Precision Wheat Disease Class...

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2nd IEEE Global Conference on Communications and information Technologies, GCCIT 2024

作者： Jain, Anuj Kumar Jain, Sonam Kaur, Urvinder Dahiya, Atisha Bhandari, Rahul Goel, Sandeep Chandigarh University Department of Cse Punjab Mohali India Panipat Institute of Engineering and Technology Department of Computer Applications Haryana Samalkha India Panipat Institute of Engineering and Technology Department of Information Technology Haryana Samalkha India

ISBN: (纸本)9798350388916

This study presents a novel classification algorithm designed to accurately identify plant diseases using leaf image analysis. The model employs a hybrid architecture containing a CNN (Convolutional Neural Network) and an SVM (Support Vector Machine) classifier, featuring 2 convolutional layers and 2 max-pooling layers. A variety of datasets encompassing five distinct plant diseases have been utilized for assessment: Stripe Rust, Powdery Mildew, Leaf Rust, Septoria Leaf Blotch, and Fusarium Head Blight. For each disease category, the model can correctly classify a positive case with precision values ranging from 92.45% to 93.75%. Similarly, the recall values are consistent with a range from 91.84% to 94.12%, which were very good too and would be shown that no matter what was taken into account either ring or original brain scans researchers could obtain positive instances well enough using this model resources The model showcases a significant performance, with F1-Score (a balanced metric concerning precision and recall) having high values constant between 92.78% and 93.75%. Support Proportions indicate how much of the overall dataset can be attributed to a given disease class and Support Values show where there are occurrences in each proportion. Its great Weighted Average Accuracy of 93.25% shows that the model is trained on a wide range of plant diseases so it does not only work for one or two datasets but across all datasets. This endto-end macro-average and micro-average evaluation, again only shows all around effectiveness of the model. Results & conclusions: This study provides a highly accurate automated plant disease diagnostic model, which is of great significance for crop management and precision agriculture. If validated through follow-up studies, the findings could improve crop yields and sustainability as well as help develop remote sensing technology that rapidly identifies plant disease. © 2024 IEEE.

关键词： Precision agriculture

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Applying Natural Language Processing for Detecting Cybersecurity Threats Using Sentimental Analysis Techniques 5th

Applying Natural Language Processing for Detecting Cybersecu...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Mishra, Awakash Ganesh, D. Sharma, Apurva Vignesh, R. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper examines the ability to use herbal Language Processing (NLP) and Sentiment evaluation strategies to detect cybersecurity threats. We describe the traditional tactics and present-day tendencies in cybersecurity chance detection and discuss the demanding situations the technique confronts. We then present how NLP and Sentiment analysis can resource inside the detection technique, provide examples of ways NLP tools can assist in chance detection, and discuss how NLP can offer beneficial data in phrases of danger detection. We quickly review the strategies for classifying and verifying sentiment from textual statistics. Our findings propose that NLP and mawkish analysis can successfully discover cybersecurity threats and provide treasured insights for additional studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Investigating the Use of Multi-Sourced Input Data for Time Series Algorithms Applied to Hyper Spectral Imagery 5th

Investigating the Use of Multi-Sourced Input Data for Time S...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Raj, Shanthi Pichandi Ananda Mishra, Awakash D, Preethi Sivaraman, Himani School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

This paper examines the efficacy of merging multi-sourced enter records into time series algorithms for analyzing hyperspectral imagery. Combining entered information from different resources (e.g., from Radar, Landsat, and LiDAR) with usually used time collection algorithms (e.g., SVM, Random Forests, and Gradient Boosting) is investigated in this study. First, Landsat, LiDAR, and Radar data are purified, and the derived pixels are stacked and rasterized to generate input datasets as collections of vectors. Secondly, the entered datasets are transformed into temporal capabilities with the same old Deviation technique (SDM) and minimum Spanning Tree (MST) to visualize the records from a temporal angle. The temporal features enter the various time series algorithms for hyper hyperspectral type. The paper concludes by showing the capacity improvements because of merging more than one asset within the analyzed algorithms and further discussing feasible explorative programs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： analyzed explorative hyperspectral

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Fuzzy-NMS: Improving 3D Object Detection With Fuzzy Classification in NMS

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Li Zhang, Xinyu Zhao, Fachuan Wu, Chuze Wang, Yichen Song, Ziying Yang, Lei Xu, Bin Li, Jun Ge, Shuzhi Sam School of Machanical Engineering Beijing Institute of Technology Beijing China School of Vehicle and Mobility Tsinghua University Beijing China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering Interactive Digital Media Institute Social Robotics Laboratory National University of Singapore Singapore

Non-maximum suppression (NMS) is an essential post-processing module in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments use the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time. IEEE

关键词： Object detection

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Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

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Journal of Safety Science and Resilience 2024年第1期5卷 91-101页

作者： Sachin Dhawan Hemanta Kumar Bhuyan Subhendu Kumar Pani Vinayakumar Ravi Rashmi Gupta Arun Rana Alanoud Al Mazroa Dorset College King George BlvdSurreyBritish ColumbiaCanada Department of Information Technology Vignan’s Foundation for ScienceTechnology and Research(Deemed to be University)Andhra PradeshIndia Krupajal Computer Academy Biju Patnaik University of Technology(BPUT)Rourkela 751002India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia NSUT East Campus(Formerly AIACTRDelhi 110031India(Affiliated to Guru Gobind Singh Inderaprastha University))Dwarka 110078India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida 201310Uttar PradeshIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia

The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as ***,this progress has also led to security concerns related to the transmission of confidential ***,safeguarding these data during communication through insecure channels is crucial for obvious *** emergence of steganography offers a robust approach to concealing confidential information,such as images,audio tracks,text files,and video files,in suitable media carriers.A novel technique is envisioned based on back-propagation *** to the proposed method,a hybrid fuzzy neural network(HFNN)is applied to the output obtained from the least significant bit substitution of secret data using pixel value dif-ferences and exploiting the modification *** simulation and test results,it has been observed that the proposed methodology achieves secure steganography and superior visual *** the experiments,we observed that for the secret image of the cameraman,the PSNR&MSE values of the proposed technique are 61.963895 and 0.041361,respectively.

关键词： Image enhancement Image processing Steganography Deep learning Fuzzy neural combination

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Exploring Data as Quantum Bits: Analysis of Quantum Image Encoding Patterns for NISQ era

Exploring Data as Quantum Bits: Analysis of Quantum Image En...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Gopakumar, Parvathy Rubell Marion Lincy, G. Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kerala Pala Kottayam India

ISBN: (纸本)9798350314946

The generall efficiency of a computational system, be it simple or complex, presentation of the data plays a crucial role in getting the most out of it. Classical information theory, quantum mechanics, and computer science collectively influence Quantum information in comprehending how information can be encoded, transmitted, and modified using quantum systems. Quantum mechanics allows quantum information to incorporate a variety of properties, such as quantum encoding of classical information, storing coherent details in quantum superpositions, quantum computations and entanglement, which occurs when two or more qubits become highly correlated and share an unbreakable quantum state. There are numerous barriers and constraints in the broad field of quantum computation, ranging from dealing with Decoherence to ensuring error correction. Moreover, the qubit, a fundamental computational unit, is challenging to create, manipulate, and store. To ensure the quality of the Quantum Algorithms, the area of quantum data encoding has been thoroughly researched and explored as relevant results can be obtained by adequate encoding. In this paper, we reviewed the terminology and physical modeling of qubits, as well as the encoding approaches used to transform data from classical to quantum models. Furthermore, different encoding schemes for Quantum Image Processing were explored to determine the efficiency of quantum pixel representation(QPIXL) framework for effective application in NISQ era. © 2023 IEEE.

关键词： Qubits

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Traceability and Prevention System Construction of Network Attacks Driven by Big Data

Traceability and Prevention System Construction of Network A...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Liu, Benfa School of Computer and Information Engineering Wuhan Railway Vocational College of Technology Wuhan Hubei430205 China

ISBN: (纸本)9798350329773

The traditional mode of defending against DDoS attacks forms a distributed and strongly coupled system by integrating data processing and control logic into network devices. This system structure can improve the reliability of network operation, but it also has some problems, such as overburdened managers, unable to achieve global unified scheduling and consuming huge resources. In this paper, typical network attacks such as SQL(Structured Query Language) and DDOS (Distributed Denial of Service) are taken as research objects, and the characteristics of these two types of attacks in the context of big data are modeled. The packet is tested by NS2 simulation software, and the results show that BP(Backpropagation) algorithm is used to improve the probabilistic packet marking method, which can effectively improve the convergence speed of the tracking algorithm and improve the tracking accuracy. In view of the large number and high complexity of attack sources in big data environment, it is difficult to solve them by traditional methods based on experience or theory. This paper intends to improve the existing traceability methods by using the adaptability and self-learning ability of BP network. When the number of attack sources is 60, the false alarm rate of the traditional packet marking algorithm is 0.34, and the false alarm rate of the improved probabilistic packet marking algorithm is 0.20. © 2023 IEEE.

关键词： Big data

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Application of Waning Immunity Index Model using Spiking Neural Networks for COVID-19 Pandemic in the geographic context of India 2

Application of Waning Immunity Index Model using Spiking Neu...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Senthil Velan, S. Rubini, P. Sivaranjani, S. Cmr Institute of Technology Information Science and Engineering Bengaluru India Cmr University Computer Science and Engineering Soet Bengaluru India Computer Science and Engineering Mvj College of Engineering Bengaluru India

ISBN: (纸本)9798350315905

Spiking Neural Networks (SNN) are biologically inspired networks working on the principle of communication triggered while crossing of threshold potentials. During the COVID-19 pandemic, immunity has been acquired by the population in a geographical location by infections and immunizations. The Waning Immunity Model (WII) has been used to apply the method of SNNs so that the results of the model provide a better way of understanding its effects. The dataset considered in this research is for a time period of six months during the years of 2021 and 2022 focusing on the geographical location of India. Based on the proposed new model, the spike in the WII index is clearly evident in the first half of the time period under consideration, This model will help the healthcare and governments officials to plan for the booster doses to be administered to the human population for reinvigorating the antibodies effectively fighting the COVID-19 virus. © 2023 IEEE.

关键词： COVID-19

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