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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Gupta, Aryan Bhadauria, Devansh Singh Atray, Manav Kaur, Inderjeet Department of Computer Science & Engineering Galgotias College of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032911571

Creating natural language descriptions or captions for images is a formidable task that requires a combination of computer vision techniques to understand image content and natural language processing models to express this understanding in coherent sentences. This paper delves into the development of a web-accessible Image Caption Generator, aiming to improve user experience by automatically generating captions for uploaded images. The versatility of this generator extends its applicability to various uses, such as image indexing, assisting visually impaired individuals, and enhancing interactions on social media platforms. This paper results in caption generator without the need for complex data pre-processing or a specialized model pipeline. Notably, a singular end-to-end model seamlessly predicts captions, streamlining the entire process. The paper integrates social media platforms with the proposed Image Caption Generator giving users automated and contextually relevant captions, fostering a more immersive and inclusive digital experience. © 2025 the Author(s).

关键词： Long short-term memory

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Gender Classification from Fingerprint Using Hybrid CNN-SVM

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Journal of Artificial Intelligence and technology 2024年第1期4卷 82-87页

作者： J.Serin Keren T.Vidhya I.S.Mary Ivy Deepa V.Ebenezer A.Jenefa Department of Information Technology Women’s Christian CollegeChennaiIndia Department of Computer Science and Engineering Women’s Christian CollegeChennaiIndia Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia

Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business *** biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable *** research employs fingerprints to classify gender,with the intention of being relevant for future *** methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based *** these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running *** this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main *** main goal of this study is to use CNN to extract fingerprint *** features are then sent to an SVM classifier to determine *** hybrid model’s performance measures are examined and compared to those of the conventional CNN *** a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.

关键词： digital image processing fingerprint gender classification hybrid CNN-SVM hybrid model pattern recognition

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FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

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Chinese Physics B 2022年第2期31卷 109-118页

作者： Fei Yu Zinan Zhang Hui Shen Yuanyuan Huang Shuo Cai Sichun Du School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation *** MHNN is simulated and dynamically analyzed,and implemented on ***,a new pseudo-random number generator(PRNG)based on MHNN is *** post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of *** experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming *** random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high ***,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).

关键词： memristive Hopfield neural network(MHNN) pseudo-random number generator(PRNG) FPGA image encryption decryption system

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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A Sensitive Wavebands Identification System for Smart Farming

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computer Systems science & engineering 2022年第10期43卷 245-257页

作者： M.Kavitha M.Sujaritha Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641025India

Sensing the content of macronutrients in the agricultural soil is an essential task in precision *** helps the farmers in the optimal use of *** reduces the cost of food production and also the negative environmentalimpacts on atmosphere and water bodies due to indiscriminate dosageof *** traditional chemical-based laboratory soil analysis methodsdo not serve the purpose as they are hardly suitable for site specific soil ***,the spectral range used in the chemical-based laboratory soil analysismay be of 350-2500 nm,which leads to redundancy and *** sensors based on the discovery of spectral wavebands that respondto soil macronutrient concentrations,on the other hand,is an innovative and successfultechnology since the results are dependable and *** goal of thisarticle is to use a supervised neuro-fuzzy based dimensionality reduction approachin the sensor development process to determine sensitive wavebands of ***,the spectral signatures of the soil are collected inan outdoor environment and mapped with its macronutrient *** thisspectral analysis,the spectral reflectance of 424 wavelengths has been obtainedand these wavelengths are evaluated through combined and individual modesas *** wavelengths are selected in each case by minimizing the fuzzy reflectance assessment *** effectiveness of these selected wavelengthsin each mode is validated by modeling the relation between the reduced reflectancespace and each macronutrient concentration using Partial Least Squares Multi Variable Regression(PLS-MVR)*** of optimal wavebands areidentified and the results are compared with the existing systems.

关键词： Sensitive waveband determination macronutrients feuro-fuzzy based dimensionality reduction partial least squares multi variable regression reflectance

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Application and Effectiveness Evaluation of BP Neural Network Model Based on Rafflesia Optimization Algorithm Optimization in Daily Average Temperature Prediction 15th

Application and Effectiveness Evaluation of BP Neural Netw...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zhang, Zhen Chu, Shu-Chuan Pan, Jeng-Shyang Li, Wei Yang, Hongmei College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer Science and Technology Harbin Engineering University Harbin China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

With economic and social development, the impact of climate change on human social life is constantly deepening. The ability to accurately predict trends in meteorological data has become an important area of research at present. The main focus of this research is to explore the application and evaluation of the effectiveness of a BP neural network model based on the optimization of the Rafflesia Optimization Algorithm(ROA) for daily average temperature prediction. The data for this study uses a large amount of historical meteorological data as the training set, including date, time and the corresponding daily mean temperature. The prediction model was constructed using a BP neural network-based temperature prediction model, and the ROA was used to optimise the weights and biases of the BP neural network to improve the accuracy and reliability of the prediction model. The experimental results show that the optimized BP neural network model has higher accuracy and stability in predicting the daily average temperature compared with the traditional BP neural network model. The optimised model is also able to better capture the non-linear relationships and time-series features in the meteorological data, thus improving the accuracy and reliability of the prediction. This research can provide a reliable tool for meteorological prediction and climate research, which helps to accurately predict future climate change trends and provide scientific basis and decision support for future economic and social development and human life progress. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Climate change

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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IoT based Leakage Detection and Emergency Shutdown 6

IoT based Leakage Detection and Emergency Shutdown

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Abarna, P.S. Priya, S. Padma Pavalarajan, S. Sivatharani, S. Manshi, M. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

The risk of gas leaks has grown significantly as a life threatening issue in industrial activities, cooking, and heating. This system integrates automatic reaction mechanisms, real-time monitoring capabilities, and advanced detection technologies in a multifaceted manner to address various dangers. The comprehensive gas leak detection and automatic cut off system is intended to improve safety in a variety of situations, including industrial and residential ones. To precisely identify the existence of dangerous gases like methane and propane, the proposed system combines infrared, electrochemical, and semiconductor types of gas sensors integrated with a mobile application, users may remotely administer and monitor the system in real-time. The system initiates a multi-tiered response upon detecting a gas leak;it sounds an alarm, notifies user-specified The mobile devices about the leak, and turns on an exhaust fan to release concentrated gas. A solenoid valve is used by the system to quickly shut off the gas supply and electricity from any location. The need of user-friendly controls and prompt notifications in emergency situations is highlighted in this paper's discussion of software architecture, sensor integration, and user interface design. Experimental results show that the system is reliable and capable of responding quickly under different circumstances. In future the application of machine learning algorithms for predictive maintenance and the possibility of integrating IoT to further strengthen security protocols. The results emphasize importance to deploy these kinds of extensive safety systems widely in order to reduce the risks associated with gas exposure. © 2025 IEEE.

关键词： Solenoid valves

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Blockchain-Assisted Fine-Grained Deduplication and Integrity Auditing for Outsourced Large-Scale Data in Cloud Storage

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IEEE Internet of Things Journal 2025年第12期12卷 21662-21678页

作者： Liu, Bingyun Zhang, Xiaojun Yang, Xingchun Zhang, Yuan Xue, Jingting Zhou, Rang Southwest Petroleum University School of Computer Science and Software Engineering Engineering Research Center for Intelligent Oil and Gas Exploration Chengdu610500 China Sichuan Police College Intelligent Policing Key Laboratory of SichuanProvince Luzhou646000 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China Chengdu University of Technology School of Computer Science and Cyber Security Chengdu610059 China

Cloud computing has emerged as a promising mode for storaging vast quantities of big data, which is vulnerable to potential security threats, making it urgent to ensure data confidentiality and integrity auditing. In addition, as a large number of duplicate data files exist in cloud storage, data deduplication is significant to improve storage efficiency. In this paper, we propose a blockchain-assisted fine-grained deduplication and integrity auditing scheme for outsourced large-scale data in cloud storage, achieving internal deduplication and cross-user external deduplication for ciphertexts and authentication tags. By constructing sparse summation ciphertext tree, the scheme implements proofs of ownership protocol through vector commitment, and guarantees the retrieval of distributed deduplication data blocks by designing the reconstruction matrix and bloom filter. The scheme exploits blockchain and smart contracts to ensure transparent integrity auditing without a third-party auditor (TPA), thereby avoiding malicious auditing biases. Security analysis and performance evaluation demonstrate the feasibility of the scheme for deploying in cloud storage systems. © 2014 IEEE.

关键词： Data integrity

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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