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检索条件"机构=Computer Engineering and Applications"

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PreScienceMED: Leveraging Text Classification for Precise Disease Prediction 8

PreScienceMED: Leveraging Text Classification for Precise Di...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Prathwini Prathyakshini Nitte Department of Master of Computer Applications Karkala India Nitte Department of Information Science and Engineering Karkala India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350593

Emergent of new and modern technologies provide personalized solutions to health issues faced by the individuals. A chatbot using Natural language processing (NLP) is proposed which can detect relevant disease based on the user input. Different classification techniques such as Support vector Machine (SVM), Random Forest, Gaussian Naive Bayes and Decision Tree are used in detecting the relevant diseases with each disease containing 17 symptoms. Based on the symptoms the all the models can detect the diseases out of which Random Forest yields better accuracy score of 99.59%. Hence the proposed model can detect the diseases based on user queries. The study utilizes machine learning algorithms in order to automatically classify medical data based on the symptoms, reducing the reliance on manual analysis, and increasing efficiency. © 2024 IEEE.

关键词： Random forests

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AI Enabled Convolutional Neural Networks to Detect Brain Tumors 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Rao, Ankur School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

In the early stages of a person’s existence, it is common practice to screen for the presence of brain tumors. Nevertheless, in the current day, it has been improved using various AI computations. Along these same lines, in order to diagnose a patient with a brain tumor, we take into consideration the data of patients, such as MRI scans of the patient’s brain. Our main concern at this point is determining whether or not the tumor is present in the patient’s brain. For a patient to have a healthy lifespan, it is essential for the tumor to be identified at the earliest possible stage. During this phase of the project, we use a calculation called a convolutional neural network to determine the level of severity, which provides accurate results. The division of tumors is an essential and challenging task that falls within the purview of clinical image production. This is due to the fact that human-aided manual characterization might result in erroneous anticipations and discoveries. We proposed the use of a convolutional neural network as a means of differentiating cancers from 2D appealing resonance brain imaging (MRI). The exploratory study was carried out on a dataset that included tumors with a wide range of dimensions, localizations, configurations, and picture forces. Convolutional neural networks, which are built using Keras and Tensorflow, were chosen for usage by our team because, in comparison to regular neural networks, they provide a much-improved presentation. During the course of our investigation, CNN demonstrated an accuracy of 98.73%. Because of its great speculation capacity and quickness, the CNN design that was only recently developed has the potential to be employed as an effective decision-aid device for radiologists working in clinical diagnostics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Segmentation of Water Bodies from Satellite Images Using Spatial-Channel Attention UNet

Segmentation of Water Bodies from Satellite Images Using Spa...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Anitha, M. Chitra, P. Meenaragavi, P. Thiagarajar College of Engineering Department of Computer Applications Madurai625 015 India Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350378177

Precise division of aquatic areas is essential for efficient environmental supervision and administration. It enables the precise delineation and tracking of aquatic ecosystems, facilitating the assessment of water quality and the identification of environmental hazards such as pollution and habitat degradation. By offering high-resolution data and enabling thorough coverage of large areas, advanced technologies like satellite images play a crucial role in improving water body segmentation and expanding the capacity to monitor and manage water resources effectively and sustainably. In this study, the focus is on leveraging Sentinel-2 satellite imagery for accurate water body segmentation. The dataset is meticulously curated and comprises Sentinel-2 satellite images. Masks are then created to isolate water bodies from the original images using advanced deep-learning architectures, including UNet, Deeplabs, and SegNet. Furthermore, the performance of UNet is enhanced by incorporating attention mechanisms such as channel attention, spatial attention, and combined spatial and channel attention. These algorithms are used to generate output masks, which are then compared with the existing masks from the dataset. Subsequently, the evaluation was performed using metrics such as accuracy, binary cross- entropy loss, and mean intersection over union, providing comprehensive insights into their efficacy. Notably, the findings highlight the remarkable accuracy achieved by the UNet model enhanced with spatial and channel attention mechanisms, reaching an impressive 96.7%. This notable progress emphasizes the possibility of achieving better accuracy in segmenting water, which in turn aids real-time monitoring and informed decision-making in environmental management. © 2024 IEEE.

关键词： Decision making

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Cumulative Summary List Driven Lightweight Frequent Closed High Utility Itemset Mining 2

Cumulative Summary List Driven Lightweight Frequent Closed H...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Siva, S. Chaudhari, Shilpa REVA University School of Computer Science and Applications Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350320923

Pattern mining has always been a dominant approach in data mining to identify the object(s) of interest from a humongous large data space to make real-time decisions. In sync with diverse business intelligence or other enterprise demands, pattern mining methods have gained wide-spread attention to serve purposes like high-utility itemset identification, segmentation and top-(itemsets) recommendation. In this paper, a robust cumulative summary list-structure driven lightweight frequent closed high-utility itemsets mining model (CSLFC-HUIM) is proposed that uses an improved cumulative summary list structure (CULS) to ensure optimal set of candidate itemsets in search space. Unlike classical support value driven strict-thresholding, CSLFCHUIM applied lift-score as minimum threshold called cumulative support/utility threshold to prune the search space. The strategic amalgamation of cumulative support value, utility threshold with CULS and cumulative summary list structure (CSLS) concept guarantee optimal set of itemsets in nested-list structure. It reduces unwanted database-scanning, execution-time and memory exhaustion, which makes it a potential solution for different pattern mining and business intelligence tasks. © 2023 IEEE.

关键词： Data mining

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SDN-based Mobile Edge Computing with Vacation of Virtual Machines 3

SDN-based Mobile Edge Computing with Vacation of Virtual Mac...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Rout, Suchismita Jaiswal, Ankita Bagade, Monika Keshao Patra, Sudhansu Shekhar Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Shri Ramdeobaba College of Engineering and Management Dept. of Computer Sc. & Engineering Nagpur India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350381931

A vast array of IoT devices have been implemented worldwide as cloud computing and virtualization have become more and more commonplace. Because of this, processing of computationally demanding tasks is moved from the edge to the centralised cloud, increasing energy consumption in cloud data centres and causing a noticeable delay for vital applications. The paradigm for cloud computing has shifted in recent years from centralised to mobile edge computing (MEC), where computational workloads are delegated to edge servers close to user equipment (UE). This epitome results in minimal latency for UE, effective resource utilisation at the edge, and decreased energy consumption in cloud data centres. Given this, developing optimal energy-saving solutions for resource allocation and computing job offloading in MEC networks has become a challenging challenge due to the rapidly growing size and complexity of these networks. The vacation policy is a suggested power-saving traffic management strategy for edge servers. Software-defined network (SDN) architecture allows virtual machines (VMs) to take vacations. The goal of QoS optimisation is to decrease the active servers serving the tasks while maintaining QoS criteria. Numerous performance metrics of the system are calculated, including average system size, sojourn duration, percentage of client requests fulfilled, and frequency of desertion. Lastly, many numerical results are given to demonstrate the influence of certain particular factors on the MEC performance metrics. © 2024 IEEE.

关键词： Mobile edge computing

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Exploring Human Gesture Recognition: Bridging Communication Gaps Through Dynamic Signal Analysis 3rd

Exploring Human Gesture Recognition: Bridging Communication ...

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (纸本)9789819751457

The ability of humans to communicate more effectively via the use of gestures is widely recognised as one of the most important aspects of human interaction. Because of the inextricable link that exists between body language and successful communication, there has been a significant amount of study conducted in the domains of computer vision and human–computer interaction (HCI). Signal recognition is one of the most important aspects of this study. Signal recognition attempts to analyse and decode the information that is transmitted by human motions. These movements include gestures such as hand and facial expressions. Signal recognition is a key bridge that enables people with varying skills to successfully communicate and connect with others. This is particularly true for those who have trouble understanding speech. There is a possibility that algorithms developed for signal detection can help bridge the communication gap and make interactions more welcoming to all parties involved. These attempts constitute a big step forward in the direction of making interactions with computers seem more natural and more analogous to communication between humans. Static signals and dynamic signals are the two basic types that may be distinguished within the field of signal recognition. These critical frames act as turning points, summing together the most important aspects of the meaning sent by the signal. The purpose of this study is to investigate the area of dynamic signal identification and key frame extraction in order to shed light on the advantages and disadvantages associated with both of these processes. By investigating these facets, our ultimate goal is to get a more profound comprehension of the complexities involved in interpreting dynamic gestures and the possible uses of these gestures. This understanding will add to the continuing attempts to make interactions between humans and computers more natural, inclusive, and human-like, which will eventually improve th

关键词： Gesture recognition

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Skin disease detection using Decision Tree and Support Vector Machine 1

Skin disease detection using Decision Tree and Support Vecto...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Verma, Sarvachan Kumar, Ajitesh Kumar, Manoj Gla University Computer Engineering and Applications Mathura India Guru Ghasidas Vishwavidyalaya Information Technology Bilaspur India

ISBN: (纸本)9798350367720

Skin-related conditions are the most common health problems worldwide. Infections carry concealed risks that may lead to a spectrum of health challenges, encompassing both physical ailments and mental health disorders such as depression. In extreme instances, it has the potential to induce skin cancer. Manually diagnosing skin conditions by medical professionals is a laborious and subjective process. Thus, it is clear to patients and physicians that automatic skin disease prediction is necessary to enable quick treatment plans. The primary aim of this study is to predict whether a skin disease is benign or malignant. To reduce image noise, our approach utilizes Gaussian filtering. Subsequently, for each image contrast correlation energy and homogeneity was computed using GLCM- Gray Level Co-occurrence Matrix. Machine learning algorithms were applied for classification: Support Vector Machine (SVM) and Decision Tree (DT). We found that Decision Tree (DT) attained a classification accuracy of 91%, which is higher related to SVM. © 2024 IEEE.

关键词： Support vector machines

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Application of Artificial Intelligence in Soil Contamination Analysis: Green Sustainable Practices 2

Application of Artificial Intelligence in Soil Contamination...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Jabin, Shagufta Abbas, Sadiqa Chadha, Anupama Chawla, Jyoti School of Engineering & Technology MRIIRS Haryana Faridabad India School of Computer Applications MRIIRS Haryana Faridabad India

ISBN: (纸本)9798331541217

Soil nutrients have a significant contribution in the health of soil and in productivity of crops. However, traditional methods of analysis of different parameters of soil such as electrical conductivity (EC), pH, organic carbon, moisture content, nitrogen, C/N ratio, phosphorus and potassium (NPK) and soil temperature are time consuming and exhaustive methods. However, with advancement in artificial intelligence, various advanced and digital based techniques can be explored for handling the large number of soil parameters data for better and efficient results. A set of AI techniques including Deep Learning (DL) and Machine Learning (ML) have great potential in advanced technology based agriculture systems. Farmers and soil scientists can identify the amount of nutrients present in agricultural soil by exploiting AI methods particularly ML and DL techniques. These techniques are accurate and adaptable techniques. Accuracy of results can be achieved by ML technique. Generative AI, a subset of AI is playing an important role to enhance the productivity in agriculture domain. In this paper, the authors have shown the application of GAN, a machine learning framework, to generate data that imitates real soil contamination data. This synthetic data can help to augment training datasets for machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Blockchain - A Deployment Mechanism for IoT Based Security 1

Blockchain - A Deployment Mechanism for IoT Based Security

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Kant, Ravi Sharma, Shashank Vikas, Vikas Chaudhary, Sachin Jain, Animesh Kumar Sharma, Kewal Krishan Iimt Engineering College Computer Science and Engineering Department Meerut India Iimt University School of Computer Science & Applications Meerut India

ISBN: (纸本)9798350338027

Implementation of IoT domain invites tremendous attacking opportunities which demands end to - end security mechanism. applications related to the domain of IoT varies from critical applications to normal business-oriented applications like Intelligent Transportation Systems, Smart Grid, Video Surveillance, Banking, Logistics, Insurance etc. A special support in terms of security must be required for critical applications as well as normal business applications. Large amount of security mechanisms implemented time to time. Blockchain technology proves to be useful in providing security to several applications based on IoT by following defense - in - depth or castle approach. Blockchain can be defined as a database for storing processed data in a sequential manner. Such data has been shared among participating users. Information can be stored on a public ledger which can't be updated. Every device in the corresponding network must retain the same ledger. Through the medium of this paper, we try to highlight importance of Blockchain Mechanism in IoT environment and also point out that the concept of Blockchain mechanism is only concerned with security. © 2023 IEEE.

关键词： Blockchain

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