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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Umamaheswari, T.S. Dhaygude, Amol Dattatray Dewangan, Omprakash Krishnan, T. Yerpude, Poonam Swarnkar, Suman Kumar Jayagovind Harigopal Agarwal Agarsen College Department of Computer Applications Chennai India University of Washington Department of Data Science Seattle United States Kalinga University Department of CS & IT Chhattisgarh Raipur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Indian Institute of Technology Department of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350304480

Predictive modeling of disease progression in chronic conditions is a crucial task in healthcare, as it enables early identification and personalized intervention for patients at risk of adverse outcomes. In recent times, there has been a notable advancement in the field of machine learning techniques, which has demonstrated considerable potential in accurately forecasting the evolution of diseases. This development has proven to be highly beneficial for healthcare professionals, as it equips them with vital insights that can significantly enhance the management of patients. This research paper provides a thorough examination and comparative evaluation of different machine learning algorithms in the context of forecasting the course of diseases in chronic situations. We explore the use of diverse datasets containing longitudinal patient records, clinical variables, and disease-specific markers to train and evaluate predictive models. The study also investigates the impact of feature selection and data preprocessing techniques on the model's performance. Moreover, we assess the generalizability and robustness of the models across different chronic diseases to determine their potential for broader clinical applications. The results demonstrate that machine learning models can effectively predict disease progression, with certain algorithms outperforming others in specific scenarios. The research findings presented in this study offer significant contributions to the understanding and implementation of machine learning techniques for predicting disease development. The aforementioned realizations have the potential to be of substantial assistance to medical professionals in terms of making well-informed decisions, which will ultimately lead to better outcomes for patients. © 2023 IEEE.

关键词： Chronic conditions Clinical applications Clinical variables data preprocessing Disease progression Feature selection Generalizability Healthcare Longitudinal patient records Machine learning Patient outcomes Predictive modeling

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An automated seamless mosaicing system of multi-charge coupled devices of panchromatic data

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Journal of the Indian Society of Remote Sensing 2004年第1期32卷 103-111页

作者： Ramakrishnan, R. Manthira Moorthi, S. Padmanabhan, N. Gupta, P. Data Products Software Division Signal and Image Processing Group Space Applications Centre (ISRO) Ahmedabad-380 015 India Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur 208 016 India

Panchromatic data of pixel resolution 5.8 m obtained from IRS-IC and IRS-ID satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a combination of 3 CCD line sensors, each with 4096 pixels. Each CCD-line sensor with different imaging times causes geometric problems of mosaicing three strips data together. In this paper, we propose the details of the design elements of system that caters to the need for accurate and automatic multi strip image registration without any second resampling of the data. The systematic geometric correction grid mapping is improved to facilitate accurate mosaicing by automatic image registration task that makes use of the overlap data within image strips and image registration is achieved up to sub-pixel level.

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Disguise Face Classification Using EfficientNet Deep Learning 1st

Disguise Face Classification Using EfficientNet Deep Learnin...

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Padmashree, G. Wagle, Shruti G. Karunakar, A.K. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789811954023

With the rise in popularity of social media and intelligent gadgets, one essential biometrics for identifying people is their face. The efficiency of existing automatic face recognition systems has decreased due to factors such as face ageing, conceals, and pose variations. Face recognition algorithms must be more accurate for recognizing faces hidden behind masks and makeup, as security and surveillance requirements become more stringent. Disguise face classification acts as a standalone early warning system in such scenarios. The major goal of this research was to see how the EfficientNet family of models compares to the current state-of-the-art architecture in terms of disguise face classification. For disguise face classification, the EfficientNet deep learning architecture was proposed in this paper. The models were trained and tested using the Disguise Faces in Wild (DFW) 2018 data set. The transfer learning method was used to train EfficientNet and other deep learning models. Results obtained proved that EfficientNet-B3 has outperformed other EfficientNet architectures with 92.2% precision and 93.9% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Maximum likelihood based estimation with quasi oppositional chemical reaction optimization algorithm for speech signal enhancement

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3265-3275页

作者： Kaur, Inderjeet Nassa, Vinay Kumar Kavitha, T. Mohan, Prakash Velmurugan, S. Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India Department of Artificial Intelligence & Data Science Sharad Institute of Technology College of Engineering Maharashtra Yadrav-Kohiapur India Department Computer Applications Kongu Engineering College Perundurai Tamil Nadu Erode India Data Science and Analytics Center Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Vel Tech Multi Tech Dr. RR & Dr.SR Engineering College Tamil Nadu Chennai India

In recent times, speech enhancement (SE) becomes a significant process in the field of speech signal processing. Since the speech signal in real time gets affected by background noise, the efficacy of the speech-based applications gets severely affected. The SE techniques have gained considerable attention in the usage of speaker recognition, video conferences, speech broadcast, speech-enabled biometric systems, smartphones, hearing aid, and so on. Numerous conventional SE and machine learning (ML) models have been employed for processing and removing the additive noise from the speech signal. This paper presents a novel Maximum Likelihood Based Estimation with Quasi Oppositional Chemical Reaction Optimization (MLE-QOCRO) algorithm for speech signal enhancement. The presented MLE-QOCRO algorithm follows three-stage processes namely preprocessing, mask generation, and spectral filtering. Besides, the mask generation process involves QOCRO algorithm to classify the speech signal into noise or speech frames. In addition, MLE based spectral filtering technique is employed at the final stage to get enhanced speech signals. A set of experiments were performed to highlight the improved performance of the proposed MLE-QOCRO algorithm, and the results are investigated in terms of several performance measures. Speech signal augmentation using the unique MLE-QOCRO algorithm, which combines maximum likelihood estimation with quasi-opposing chemical reaction optimization, is the subject of this paper. The QOCRO algorithm is used in the mask generation process in the model to distinguish between noise and speech frames in the input speech signal. A spectral filtering method based on MLE is also utilized to improve the voice signal at the very end. The resultant experimental values pointed out the enhanced outcome of the proposed MLE-QOCRO algorithm over the other compared techniques. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Appl

关键词： Chemical reaction optimization Maximum likelihood Spectral filtering Speech signal enhancement

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ReFuSeg: Regularized Multi-modal Fusion for Precise Brain Tumour Segmentation 9th

ReFuSeg: Regularized Multi-modal Fusion for Precise Brain T...

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9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image Computing and computer Assisted Intervention, MICCAI 2023

作者： Kasliwal, Aditya Sagaram, Sankarshanaa Srivastava, Laven Seth, Pratinav Khan, Adil Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031761591

Semantic segmentation of brain tumours is a fundamental task in medical image analysis that can help clinicians in diagnosing the patient and tracking the progression of any malignant entities. Accurate segmentation of brain lesions is essential for medical diagnosis and treatment planning. However, failure to acquire specific MRI imaging modalities can prevent applications from operating in critical situations, raising concerns about their reliability and overall trustworthiness. This paper presents a novel multi-modal approach for brain lesion segmentation that leverages information from four distinct imaging modalities while being robust to real-world scenarios of missing modalities, such as T1, T1c, T2, and FLAIR MRI of brains. Our proposed method can help address the challenges posed by artifacts in medical imagery due to data acquisition errors (such as patient motion) or a reconstruction algorithm’s inability to represent the anatomy while ensuring a trade-off in accuracy. Our proposed regularization module makes it robust to these scenarios and ensures the reliability of lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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QUATRE Algorithm with Sort Strategy for Global Optimization in Comparison with DE and PSO Variants 4th

QUATRE Algorithm with Sort Strategy for Global Optimization ...

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4th Euro-China Conference on Intelligent data Analysis and applications, ECC 2017

作者： Pan, Jeng-Shyang Meng, Zhenyu Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319685267

Optimization algorithm in swarm intelligence is getting more and more prevalent both in theoretical field and in real-world applications. Many nature-inspired algorithms in this domain have been proposed and employed in different applications. In this paper, a new QUATRE algorithm with sort strategy is proposed for global optimization. QUATRE algorithm is a simple but powerful stochastic optimization algorithm proposed in 2016 and it tackles the representational/positional bias existing in DE structure. Here a sort strategy is used for the enhancement of the canonical QUATRE algorithm. This advancement is verified on CEC2013 test suite for real-parameter optimization and also is contrasted with several state-of-the-art algorithms including Particle Swarm Optimization (PSO) variants, Differential Evolution (DE) variants on COCO framework under BBOB2009 benchmarks. Experiment results show that the proposed QUATRE algorithm with sort strategy is competitive with the contrasted algorithms. © 2018, Springer International Publishing AG.

关键词： Swarm intelligence

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Evaluating the Effectiveness of Object Detection Algorithms for Autonomous Parking

Evaluating the Effectiveness of Object Detection Algorithms ...

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2024 International Conference on computer, Electronics, Electrical Engineering and their applications, IC2E3 2024

作者： Sharma, Utkarsh Subramonian, Krishnan Dsouza, Aaron Sughosh, P. Savitha, G. Girisha, S. Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Civil Engineering Manipal576104 India

ISBN: (纸本)9798350388534

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the 'PKLot' dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development. © 2024 IEEE.

关键词： Traffic congestion

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Ensemble of Machine Learning Classifiers for Detecting Deepfake Videos using Deep Feature

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第4期50卷 1279页

作者： Padmashree, G. Karunkar, A.K. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Rapid progress in artificial intelligence, machine learning, and deep learning over the last few decades has resulted in new methodologies and tools for altering multimedia. Deepfakes represent a technology for seamlessly swapping faces in videos, yielding remarkably lifelike outcomes. While this technology offers numerous applications, its malicious use, such as disseminating false information or participating in cyber harassment, can exert a significant impact on society. This makes the identification of deepfakes a critical issue. In this paper, we propose a hybrid strategy for deepfake identification in videos that combines deep learning and machine learning. Faces are identified in the videos using YOLO-V3 face detectors and using the efficientNet deep learning model, features are extracted from the faces. Deepfakes are identified using an ensemble of machine learning classifiers such as support vector machine (SVM), decision trees(DT), k-nearest neighbor (KNN), and naive bayes(NB) based on the max voting approach, which provides better results for datasets of varying sizes and resolutions. Experiments are carried out by integrating the Celeb-DF(v2) and FaceForensics++ (FF++) datasets and the suggested technique achieves 99.64% accuracy and proves that the suggested method is more effective than state-of-the-art methods. © 2023, International Association of Engineers. All rights reserved.

关键词： Decision trees

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE International Conference on Computing and data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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