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machine learning model for predicting pancreatic fistula after pancreatoduodenectomy 1

Machine learning model for predicting pancreatic fistula aft...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Saleh, Hager El-Rashidy, Nora Mohamed, Eman Sultan, Ahmad M El Nakeeb, Ayman El-Sappagh, Shaker South Valley University Faculty of Computers and Artificial Intelligence Hurghada Egypt Kafrelsheiksh University Faculty of Artificial Intelligence Machine Learning and Information Retrieval Department Kafrelsheiksh13518 Egypt Damnhour University Faculty of Computers and Information Damnhour Egypt Mansoura University Gastroenterology Surgical Center Faculty of Medicine Mansoura Egypt Galala University Faculty of Computer Science and Engineering Suez435611 Egypt

ISBN: (纸本)9798350335569

Pancreaticoduodenectomy (PD) is a complex surgery used mainly to treat tumors and other pancreas disorders. PD is considered one of the most challenging surgeries because it may have several complications, including bleeding and infections in the surgical area, temporary or permanent diabetes, and pancreatic leakage (PL), which may lead to morbidity and mortality. In this study, we build an accurate and medically oriented machine learning model that predicts PL after PD based on patient markers collected only before the PD operation. The study is made using a real-world dataset for 397 Egyptian patients. The proposed machine learning pipeline starts with a data preprocessing step that handles the missing data values by the median values. In the next step, diverse interpretable classifiers, including logistic regression, random forest, decision tree, support vector machine, XGBoost, and AdaBoost, are utilized to predict the PL. Hyperparameter optimization is done using grid search with k-fold cross-validation. The results indicate that XGBoost achieves the highest marks, outperforming the state-of-the-art techniques in several evaluation metrics (i.e., accuracy= 91%, precision= 90.96%, recall= 91.0%, F1-score= 90.97%, and AUC=89.78%. The resulting model is accurate enough to be medically relevant for PL prediction in real healthcare settings. © 2023 IEEE.

关键词： Surgery

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Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Indian Food Segmentation and Calorie Estimation Using CatBoo...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Acharya, Nabin Singh, Ayush Kumar Dwivedi, Anshuman Kumar Jayanthi, M.G. Kannadaguli, Prashant Cambridge Institute of Technology Dept. of Information Science and Engineering Bengaluru India Cambridge Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Cambridge Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

ISBN: (纸本)9798350300826

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%. © 2023 IEEE.

关键词： Convolutional neural networks

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Solar Tracker with Obstacle Detection Vehicle

Solar Tracker with Obstacle Detection Vehicle

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Electrical, Electronics, Communication and computers (ELEXCOM), International Conference on

作者： Yashdeep Singh Umesh Patidar Vivek Gaur Shivendra Singh Thakur Harikant Bilthare Kritika Bansal Artificial Intelligence and Machine Learning Madhav Institute of Technology and Science Gwalior India Centre of Artificial Intelligence Madhav Institute of Technology and Science Gwalior India

The aim of this paper is to develop a system that combines solar tracking technology with obstacle detection. This improves the safety and energy efficiency of a solar-powered vehicle. Solar powered vehicles face several challenges such as limited range and the need for efficient energy harvesting. This is done by adjusting the solar panel position based on the location of the sun to maximize energy harvesting. This ensures that the vehicle can operate for longer distances and remain charged. The system is designed to be reliable and efficient, and it was able to perform well in various testing environments.

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Innovative Horizons in Agricultural Technology with TSA Based StrawberrySqueezeNet Classification Model 2

Innovative Horizons in Agricultural Technology with TSA Base...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Vindhya, G.B. Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Tumkur Gubbi India

ISBN: (纸本)9798350373110

A fruit plant with sufficient value is the strawberry plant. High concentrations of fibre, antioxidants, potassium, vitamin C, and folic acid can be found in strawberry fruit. Because bacteria, pests, and microbes frequently impact strawberry plants, cultivating strawberries is a simple operation. Digital image processing will be used to introduce strawberry disease initially and limit its spread in strawberry plants. To improve strawberry disease diagnosis, a multi-pronged strategy was used, starting with the application of a carefully selected dataset. The dataset was then enriched and diversified using data augmentation techniques, which made it easier to build a reliable and complex model. For classification, the StrawberrySqueezeNet architecture was used, utilising its inherent capacity to identify complex patterns in the data. For hyperparameter tuning, a Tangent Search technique was used to maximise the model's performance. In order to get higher classification accuracy, the iterative optimisation procedure dynamically adjusted parameters as it explored the hyperparameter space. The result of these efforts was an astounding accuracy of 98.3%, which beat the industry standards set by other models. © 2024 IEEE.

关键词： Fruits

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PFCM Based Segmentation and TFA Based DCNN Model for Skin Cancer Classification Using Dermoscopic Images 2

PFCM Based Segmentation and TFA Based DCNN Model for Skin Ca...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Bandeppa, Lalitha Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology Cumberlands United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Worldwide, skin lesions have emerged as a serious health concern;however, the survival rate can be improved by identifying skin lesions sooner with dermoscopy pictures. It will be a painstaking process to classify the skin lesion from those dermoscopy photographs. Using deep learning models improves the accuracy of skin lesion categorization. There has been a recent influx of convolutional neural networks (CNN) into this field;CNNs have well-established methods for feature extraction, which improves classification. This project aims to create AI-based solutions, namely deep learning (DL) procedures, to differentiate among benign and malignant skin lesions in dermoscopy pictures. Seven types of skin lesions were used in the Skin Cancer MNIST: HAM10000 dataset. Visual illness diagnosis using deep learning models is becoming increasingly common. For the purpose of skin cancer classification, this study suggests using a deep convolutional neural network (DCNN). In order to enhance the accuracy of the proposed DCNN classifier, the Tangent Flight Algorithm (TFA) is recommended as the optimum approach for optimising the weights, and possibilistic fuzzy clustering (PFCM) is employed for segmentation. Results from this study demonstrate that, compared to convolutional approaches, the suggested technology provides superior accuracy. The proposed TFA based DCNN model outperformed state-of-the-art prediction methods with training accuracy of 0.962, testing accuracy of 0.947, sensitivity of 0.824, and F1-score of 0.822. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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SINN Based Federated learning Model for Intrusion Detection with Blockchain Technology in Digital Forensic 2

SINN Based Federated Learning Model for Intrusion Detection ...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Pramod Pawar, Priyanka Kumar, Deepak Krupa, R. Pareek, Piyush Kumar Manoj, H.M. Deepika, K.S. University of the Cumberlands Information Technology Cumberlands United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Today, smart devices permeate every part of our lives, ushering in a new technological era known as Internet of Things (IoT). There ration of cybersecurity risks due to the proliferation of smart devices in IoT contexts and the massive volumes of sensitive data they store. In order to determine the time and place of these attacks and to collect evidence that can lead to the identification of the perpetrators, digital forensics is essential. Research into digital forensics in an IoT setting is fraught with difficulties owing to factors such as the dispersed nature of data storage, the need to be able to track and verify the provenance of evidence, the difficulty in obtaining data from many sources, and the lack of visibility into the evidence collection procedure. This is why the research suggested merging two potential technologies to offer a complete answer. The research employed federated learning to train representations locally using data kept by IoT devices, with the help of a dataset that was created to mimic attacks on the IoT ecosystem. The study utilises the BoT-IoT dataset and employs a classification method called Shepard Interpolation Neural Network (SINN) with the Shuffled Frog-Leaping algorithm (SFLA). After that, in order to make the blockchain lightweight, the study aggregated data using blockchain by gathering parameters from the IoT gateway © 2024 IEEE.

关键词： Cybersecurity

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A Hybrid Remora Optimization Algorithm with Variable Neighborhood Search for the Flexible Job Shop Scheduling Problem 7

A Hybrid Remora Optimization Algorithm with Variable Neighbo...

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Yang, Zheng Liang, Xiaodan Li, Yajing Wang, Haoyue School Of Computer Science And Technology Tiangong University Tianjin China Engineering Research Center Of Integration And Application Of Digital Learning Technology Ministry Of Education The Open University Of China Beijing China College Of Artificial Intelligence Tianjin University Of Science And Technology Beijing China

ISBN: (纸本)9798350361445

Reducing the production makespan of modern small-batch, multi-variety jobs can be abstracted into a flexible job shop scheduling problem (FJSP). The organization can greatly improve production efficiency by scheduling according to the optimal solution of FJSP. However, due to the high computational complexity of FJSP, it is often difficult to achieve higher performance in actual production. To optimize the makespan of the FJSP, a hybrid remora optimization algorithm with variable neighborhood search (HROA-VNS) is proposed in this work. A hybrid initialization method based on machine load balancing is utilized to improve the quality of the initial population. To solve discrete FJSP, a conversion method for individual positions and scheduling solutions is proposed, and a host switching mechanism (HSM) is designed to enhance the development and exploration capabilities of the algorithm. Furthermore, for enhancing the accuracy and effectiveness of the optimal solution, a variable neighborhood search (VNS) strategy which contains multiple structures is designed for local search. According to the experimental data, compared with the basic algorithm, the HROA-VNS reduced the makespan by an average of 33.02% and 14.40% on the two benchmark test sets respectively, and it shows better performance than related algorithms. © 2024 IEEE.

关键词： Evolutionary algorithms

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New Approach of artificial intelligence in Digital Forensic Investigation: A Literature Review 1

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International Conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Verma, Raghav Garg, Sonia Kumar, Karan Gupta, Gaurav Salehi, Waleed Pareek, Piyush Kumar Kniežova, Jaroslava Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

A Digital Forensic is a subfield of forensic science known as "digital forensic science" that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. computer forensics was the original meaning of the word "digital forensics." it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： artificial intelligence

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Life Expectancy Prediction using machine learning Technique – KNN

Life Expectancy Prediction using Machine Learning Technique ...

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Applied intelligence and Computing (AIC), IEEE World Conference on

作者： Preeti Narooka Aarushi Vashistha Priyanka Mitra Gulisha Derashri Department of Artificial Intelligence and Machine Learning School of Computer Science Engineering Manipal University Jaipur Rajasthan India Department of Computer Science and Engineering Jaipur Engineering College and Research Centre Jaipur India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In the modern world, life expectancy turns out to be the most valuable factor in determining population’s health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory.

关键词： Training Support vector machines Logistic regression machine learning algorithms Pandemics Biological system modeling Medical services Nearest neighbor methods Socioeconomics Random forests

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