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A machine learning approach for estimating compressive strength of concrete structures using an artificial electric field algorithm-based neuro-fuzzy predictor 19

A machine learning approach for estimating compressive stren...

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19th Orissa Information technology Society International Conference on Information technology, OCIT 2021

作者： Nayak, Sarat Chandra Dash, Ch. Sanjeev Kumar Behera, Ajit Kumar Mishra, Bijan Bihari CMR College of Engineering & Technology Department of Artificial Intelligence Engineering & Machine Learning Hyderabad501401 India Silicon Institute of Technology Department of Computer Science & Engineering Silicon Hills Patia Bhubaneswar751024 India

ISBN: (纸本)9781665416641

machine learning methods are found better to manual and statistical methods in estimating compressive strength of concrete structures. However, there is need of exploring an effective and accurate predictor for this domain. This article proposes an artificial electric field algorithm-based neuro-fuzzy network (AEFA+NFN) for prediction of compressive strength of concrete structures. A single hidden layer neural network (SHNN) is used as the base model and its inputs are fuzzified using Gaussian triangular membership function with a degree of membership to different classes. The model parameters are decided by AEFA. The model is evaluated on samples from a publicly available dataset with curing ages at 3, 7, 14, and 28 days. Considering four sample series, the AEFA+NFN produced an average MAPE of 0.092073 and ARV of 0.139731 which are better compared to others. The experimental outcomes are in favour of AEFA+NFN, hence it can be suggested as a promising forecasting tool. © 2021 IEEE.

关键词： Electric fields

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artificial intelligence powered chatbots for the speech disabled and hearing impaired using Feedforward Neural Network

Artificial Intelligence powered chatbots for the speech disa...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Subhayan Mukherjee Anusuya Chatterjee Khushbu Chandrakar Sandeep Saxena Artificial Intelligence and Machine Learning Asansol Engineering College Asansol India Electronics and Communication Engineering Asansol Engineering College Asansol India Computer Science and Engineering Asansol Engineering College Asansol India Computer Science and Engineering Greater Noida Institute of Technology Delhi India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Speech has been the catalyst for natural human communication, and any disability in this field presents a significant barrier to normal communication. Sign language is the primary method of communication, but its education is not available in all parts of the country. This research paper titled ’artificial intelligence Powered Chatbots for the Speech Disabled and Hearing Impaired using Feedforward Neural Network’ provides a novel integration of artificial intelligence to guide and empower the daily routines of people with speech disabilities. By introducing a specialized chatbot tailored to the unique needs of speech-disabled individuals, this paper addresses the pressing need for advanced assistive technologies in the daily lives of speech-disabled individuals. This paper underscores the transformative impact of AI in fostering inclusivity and empowerment in the lives of people with disabilities.

关键词： Training Auditory system Speech recognition User interfaces Chatbots Software Real-time systems Feedforward neural networks artificial intelligence Speech processing

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Estimating fish weight growth in aquaponic farming through machine learning techniques

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

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Intelligent Technologies (CONIT), International Conference on

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

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

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Ultrasound-based Kidney Disease Detection: Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Network 8

Ultrasound-based Kidney Disease Detection: Resampling Cubatu...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Padmanayana Kurumeti, N.S.L. Kumar Veena, S. Raja, Ch. Upadhye, Vijay Jagdish Maranan, Ramya Department of Artificial Intelligence and Data Science Engineering AJ Institute of Engineering and Technology Karnataka Mangaluru 575006 India Department of Artificial Intelligence and Machine Learning Aditya University Andhra Pradesh Surampalem 533437 India Department of Electronics and Communication Engineering SJC Institute of Technology Karnataka Chikkaballapur 562101 India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad 500075 India Department of Microbiology Research and Development Cell (RDC) Dept of Microbiology Parul Institute of Applied Sciences (PIAS) Parul University Post Limda Gujarat Waghodia 391760 India Department of Research and Innovation Saveetha School of Engineering SIMATS Tamil Nadu Chennai 602105 India

ISBN: (纸本)9798331512248

Limitations of ultrasound-based identification of kidney disease include poor image resolution, operator reliance, and lack of detection of small tissue anomalies. Diagnostic accuracy may be affected by artefacts or noise, or changes in patient anatomy. It may also have difficulty detecting microscopic structural changes or renal disease in its incipiency. The limitations are overcome by the analysis of the Resampling Cubature Kalman Filter and Physics-Guided VGG Cross-Contextual Networks (RCKF-PG-VGG-CCNet) for kidney disease detection from ultrasound images based on the Kidney Ultrasound Image Dataset. The initial use of the Resampling Cubature Kalman Filter and the first use of Physics-Guided VGG Cross-Contextual Networks is to enhance image quality. Through identifying regions through deviations, the Scale-Aware Meet Transformer (SAMT) performs segmentation. While the Synergistic Fibroblast Optimization (SFO) enhances classification performance through optimization of the search for diseased regions, the Physics-Guided VGG Cross-Contextual Networks (PG-VGG-CCNet) classify kidney disease and identify features. Experimental output from a Python-based experiment demonstrates that RCKF-PG-VGG-CCNet is more efficient than other methods with 99.9% efficiency and 99.8% sensitivity and may enhance the accuracy of kidney disease detection. © 2025 IEEE.

关键词： Kidney disease Kidney Ultrasound Image Dataset Resampling Cubature Kalman Filter Scale-Aware Meet Transformer Synergistic Fibroblast Optimization

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An Optimized Elliptical Curve Cryptography based Crypto-Watermarking Model in Smart Healthcare Application

An Optimized Elliptical Curve Cryptography based Crypto-Wate...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： G N Keshava Murthy Manjula G Harshitha P Achyutha Prasad N Department of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru Karnataka India Department of CSE BGS College of Engineering and Technology Bangalore India Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Telemedicine uses current communication and information technology to provide clinical treatment remotely. Given the ubiquitous usage of DWT and DCT are used for watermarking, ECC-FFA is used for encryption. The only cryptographic algorithms used in the current approaches for watermarking are AES, DES, and RSA, and the DCT and DWT are typically used separately. But because we integrated the ECC-FFA cryptographic method with the DWT and DCT watermarking technologies, the recommended solution is more effective than earlier ones.. A strong and lossless patient medical information exchange system employing crypto-watermarking is suggested in order to secure medical data. For the watermarking approximate, the discrete wavelet alter (DWT) and discrete cosine alter (DCT) are combined, and the elliptical curve cryptography (ECC) algorithm is utilized. At the same time, metaheuristic algorithms may be used to solve the optimum key creation issue in cryptography, which is viewed as an optimisation challenge. As a stem, the master nucleus of this masterpiece is on the development of cryptographic encryption for picture security based on metaheuristic optimization algorithms. According to this perspective, this study creates the ECC-FFA model, an effective produce fruit fly optimisation (FFO) technique with ECC for picture protection. The suggested model uses the ECC technique to carry out the encryption and decryption procedure. Various medical photos were used in the experiment, and the peak signal to noise ratio was used to assess how well the suggested method worked.

关键词： Telemedicine Wireless networks Metaheuristics Watermarking Medical services Elliptic curve cryptography Discrete wavelet transforms

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Discussion and Application of Virtual Garment Design technology 2

Discussion and Application of Virtual Garment Design Technol...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Venkatasubramanian, R. Vidhya, V. Sankar Ganesh, S. Khadjibekov, S. Alawady, Ahmad Hussein Uma Reddy, N.V. New Prince Shri Bhavani College Of Engineering And Technology India M.E.T Engineering College Nagercoil India Medchal India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan College of technical engineering The Islamic university Najaf Iraq Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Ring Road Bellandur Post Bengaluru560103 India

ISBN: (纸本)9798350305418

As science and technology have advanced, more people have begun to use computer technology. Virtual reality technology has received attention and extensive references from the apparel design industry because of its superiority in accuracy and authenticity, its ease of preservation and modification, and its capacity to inspire designers' creative inspiration. This is due to the fact that it is the new technology that has been experienced the most intensely. As a result, research will be conducted on conceptualizing and implementing technologies for virtual clothing design. As a result, research will be conducted on conceptualizing and implementing technologies for virtual clothing design. First, the gene coding of an interactive genetic algorithm for product concept design was shown, and then the picture evaluation method of a virtual reality environment was presented. Both of these were presented in the same order. In conclusion, the findings of optimization experiments on synthetic lines were acquired by the use of experiments. Experiments have demonstrated that the optimized line is more conducive to the visual identification of images and graphics. © 2023 IEEE.

关键词： Costume designing Genetic algorithm Virtual reality

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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Thatikonda, Ramya Kempanna, M. Tatikonda, Reshmi Bhuvanesh, A. University of the Cumberlands Masters in Computer Science United States Jawaharlal Technological University Bachelors in Ece Hyderbad India University of the Cumberlands PhD in Information Technology Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Central Missouri Masters in Computer Information Systems Warrensburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350306088

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process Automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration's success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace. © 2023 IEEE.

关键词： Integration

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Advanced Data Extraction and Predictive Analytics for Hemodialyzers Using AI and OCR

Advanced Data Extraction and Predictive Analytics for Hemodi...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Espin Shalo S P Jeeshan R J S Gunasekar Pius Agbulu G Sairam V A Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Tamilnadu Chennai Department of Computer Science and Design Rajalakshmi Engineering College Thandalam Tamilnadu Chennai Department of Mechatronics Engineering Chinhoyi University of Technology Zimbabwe Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

The emphasis on medical technology has catalyzed increasing technology adoption in healthcare automation to manage patient data. Hemodialysis is a life-saving procedure in kidney disease requiring constant observation of such parameters as displayed on a hemodialysis machine. Paperbased data recording in these environments is tedious, timeconsuming, and susceptible to human errors mainly due to stress. This work proposes a method for extracting data from displays of a hemodialysis machine based on images from a mobile camera. With image analysis and Optical Character Recognition (OCR) technologies, the data, including temperature and conductivity, extracts and translates the values into JavaScript Object Notation (JSON). The algorithm has been developed to focus on plural machine models and not be limited to a specific format and screen sizes frequently used in clinical practice. In addition, the system uses artificial intelligence in predictive modelling to identify patterns and possible health issues and ensure professionals address them at the right time. The outcomes of the comparative analysis show that the mentioned scheme is more effective than the conventional paper-to-computer data managenent approach.

关键词： Temperature distribution Telemedicine Optical character recognition Medical services Conductivity Recording Data mining Predictive analytics artificial intelligence Stress

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Remote Sensing–Based Food Processing for Changing Climatic Conditions

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 484-495页

作者： Raj, Roop Gondi, Dedeepya Sai Nitnaware, Swati R. Banerjee, Sudipta Athithan, Senthil Arpita D, Gopinath Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Artificial Intelligence and Machine Learning The University of Texas at Dallas Richardson TX United States Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science Kristu Jayanti College (Autonomous) Karnataka Bengaluru India

A product must pass quality control inspections before it can be shipped to customers. In terms of quality assurance, this is crucial. Having a high-quality product is more significant for customer happiness than supplying a larger number of the same thing. As the climatic conditions change, the shelf time also varies. Quality can also be understood as the sum of all the factors that go into making something that the target audience values highly. There has been a dramatic uptick in recent years in the usage of the application of remote sensing and advanced image processing to enhance the appearance of fruits and further commodities. This is because these tools are especially helpful in overcoming the places where people’s eyesight misses the mark. This points out that the use of AI and imaging technology can replace overdue and unreliable techniques of quality assurance in manufacturing with quicker and more objective procedures. This article uses remote sensing and picture segmentation to describe how to analyze and rate food for different climatic conditions. It can tell the difference between several fruit varieties and also tell if a specific piece of fruit has gone bad. Gaussian elimination is first used to clean up the images by removing any visible noise. The images are then subjected to histogram equalization, which improves their overall quality. To carry out the segmentation process, the K-means clustering method is used. Then, photographs of fruits are classified using a number of strategies for machines to learn, including KNN, SVM, and C4.5 formulas to analyze the condition of a fruit and report back on its wholesomeness. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Agriculture artificial intelligence Food Processing Remote sensing technology

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Enhancing Insect Image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

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International Conference on Advances in Information technology (ICAIT)

作者： C A Yogaraja C Priyanka Priyadharshini. SP J.Jagan Babu S. Rajagopal S. Poorani Computer Science and Engineering Ramco Institute of Technology Rajapalayam School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus Department of Artificial Intelligence and Machine Learning (AML) St. Joseph's College of Engineering OMR Chennai Department of ECE R.M.D Engineering college Kavaraipettai Department of IT National Engineering College Kovilppati Computer Technology-UG Kongu Engineering College Erode

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect Image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species.

关键词： Visualization Image recognition Insects Biological system modeling Noise Feature extraction Image sequences

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