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Advancing Brain Tumor Analysis through Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis

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computers, Materials & Continua 2023年第12期77卷 3835-3851页

作者： S.Kannan S.Anusuya Department of Computer Science and Engineering Malla Reddy College of EngineeringSecunderabadTelunganaIndia Department of Information Technology Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSIMATSChennaiTamil Nadu602117India

Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk *** this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis(DHA-ISSP)*** DHA-ISSP model combines a three-band 3D convolutional neural network(CNN)U-Net architecture with dynamic hierarchical attention mechanisms,enabling precise tumor segmentation and survival *** DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels,enhancing segmentation *** achieving remarkable results,our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation *** a Dice similarity coefficient of 0.89 and a Hausdorff distance of 4.8 mm,the DHA-ISSP model demonstrates its effectiveness in accurately segmenting brain *** also extract radio mic characteristics from the segmented tumor areas using the DHA-ISSP *** applying cross-validation of decision trees to the selected features,we identify crucial predictors for glioma survival,enabling personalized treatment *** the DHA-ISSP model and the desired features,we assess patients’overall survival and categorize survivors into short,mid,in addition to long *** proposed work achieved impressive performance metrics,including the highest accuracy of 0.91,precision of 0.84,recall of 0.92,F1 score of 0.88,specificity of 0.94,sensitivity of 0.92,area under the curve(AUC)value of 0.96,and the lowest mean absolute error value of 0.09 and mean squared error value of *** results clearly demonstrate the superiority of the proposed system in accurately segmenting brain tumors and predicting survival outcomes,highlighting its significant merit and potential for clinical applications.

关键词： Survival prediction 3D multimodal MRI brain tumors segmentation CNN U-Net deep learning

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Online Vehicle Routing with Pickups and Deliveries under Time-Dependent Travel-Time Constraints

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arXiv 2024年

作者： Kontogiannis, Spyros Paraskevopoulos, Andreas Zaroliagis, Christos Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press "Diophantus" Greece Computer Technology Institute and Press "Diophantus" Greece

The Vehicle Routing Problem with pickups, deliveries and spatiotemporal service constraints (VRPPDSTC) is a quite challenging algorithmic problem that can be dealt with in either an offline or an online fashion. In this work, we focus on a generalization, called VRPPDSTCtd, in which the travel-time metric is time-dependent: the traversal-time per road segment (represented as a directed arc) is determined by some function of the departure-time from its tail towards its head. Time-dependence makes things much more complicated, even for the simpler problem of computing earliest-arrival-time paths which is a crucial subroutine to be solved (numerous times) by VRPPDSTCtd schedulers. We propose two online schedulers of requests to workers, one which is a time-dependent variant of the classical Plain-Insertion heuristic, and an extension of it trying to digest some sort of forecasts for future demands for service. We enrich these two online schedulers with two additional heuristics, one targeting for distance-balanced assignments of work loads to the workers and another that makes local-search-improvements to the produced solutions. We conduct a careful experimental evaluation of the proposed algorithms on a real-world instance, with or without these heuristics, and compare their quality with human-curated assignments provided by professional experts (human operators at actual pickup-and-delivery control centers), and also with feasible solutions constructed from a relaxed MILP formulation of VRPPDSTCtd, which is also introduced in this paper. Our findings are quite encouraging, demonstrating that the proposed algorithms produce solutions which (i) are significant improvements over the human-curated assignments, and (ii) have overall quality pretty close to that of the (extremely time-consuming) solutions provided by an exact solver for the MILP formulation. © 2024, CC BY.

关键词： Travel time

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EEG evoked automated emotion recognition using deep convolutional neural network 5

EEG evoked automated emotion recognition using deep convolut...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Abgeena, Abgeena Garg, Shruti Birla Institute of Technology Mesra Department of Computer Science and Engineering Ranchi India

ISBN: (纸本)9781665493604

As life continues to change in the digital era, it is crucial to perceive a person's emotional state. Affective computing is receiving more attention with the increase in the human-computer interface (HCI). Human emotion recognition employing electroen-cephalogram (EEG) signals has been studied to obtain a person's emotional status for different stimuli. However, it is difficult to identify clear patterns in EEG signals because they have low electrical impulses and are highly sensitive to noise. A deep convolutional neural network (DCNN) was employed in the present study to recognize emotions in EEG signals. For this purpose, a publicly available dataset, DREAMER, was utilized in this study to assess the applicability of the model for emotion classification. The dataset consisted of three-dimensional emotions, that is, valence, arousal, and dominance (VAD). 2D emotions arousal and valence were the most-recognized emotions in existing research. The present study identified the 3D emotions present in the above-mentioned dataset. In this study, raw EEG signals from the DREAMER dataset were pre-processed. Subsequently, three EEG rhythms, theta, alpha, and beta, were extracted using a bandpass filter. The power spectral density (PSD) was computed using fast Fourier transform (FFT) in the feature extraction. Finally, a 1D CNN model is applied to the classification of emotions. In addition, the performance of the proposed model was compared with two machine learning (ML) classifiers: random forest (RF) and extreme Gradient Boosting (XGBoost) classifiers. The highest accuracy (ACC) of 97.6% was obtained using the proposed model in the dominance dimension. The working principles were compared and discussed to determine the suitability of the model for emotion recognition applications. © 2023 IEEE.

关键词： Emotion Recognition

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Bias Mitigation in News Recommender Systems Using Preference Correction 7

Bias Mitigation in News Recommender Systems Using Preference...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Shinde, Shilpa Gaikwad, Mrunalini Ansari, Hamza Kale, Purvaja Lunawat, Smital Ramrao Adik Institute of Technology University of Mumbai Department of Computer Engineering India

ISBN: (纸本)9798350304268

This paper presents a novel approach for mitigating biases in news recommendation systems while also incorporating a user's personal preferences to provide a personalised recommendations. The proposed hybrid content and collaborative-based filtering system ranks news categories according to weights based on user history, resulting in a recommendation system that recommends news articles from a purely unbiased perspective. The method of 'preference correction' is incorporated to further recommend articles that the user will also prefer to read, improving the overall user experience. This approach can be applied to any news dataset that collects user impressions and has the potential to enhance awareness among readers and burst filter bubbles. © 2023 IEEE.

关键词： collaborative based recommendation content-based recommendation cosine similarity hybrid ensemble voting

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Quick Response Codes for Bone Fracture Diagnosis Based on Deep Learning

Quick Response Codes for Bone Fracture Diagnosis Based on De...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Arikaran, N. Sathishkumar, R. Balaji, S. Ashwin, G. Mohamed Salman, M. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350315127

Orthopaedic treatment and a precise diagnosis of bone fractures are essential components of contemporary healthcare. This study introduces a novel method for streamlining and improving the analyses of bone fractures through the use of QR codes and object detection methods. In this study, we present a novel technique to increase the speed and precision of bone fracture diagnosis by integrating computer vision skills with QR code technology. The workflow of the system starts with the gathering of digital medical pictures, such as X-rays or CT scans, which are afterwards tagged with QR codes that are patient specific. To locate fractures in these photos and to identify them, a deep learning-based on fracture detection model is used. The method is also well suited for telemedicine applications, enabling distant specialists to offer quick assessments. Additionally, the gathered information can be used for orthopaedic care research and initiatives to enhance quality of care. We demonstrate the system's efficacy and emphasize its potential to revolutionize orthopaedics through thorough examination and case studies. We think that the combination of object detection with QR codes can greatly improve the speed and precision of bone fracture diagnosis, leading to better patient outcomes overall. With a direct bearing on patient care and the medical community's capacity to offer prompt and accurate diagnoses in orthopaedic patients, this research represents a significant advancement in the merging of medical imaging technology with artificial intelligence. © 2023 IEEE.

关键词： Orthopedics

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Segmentation of Brain Tumors using traditional Multiscale bilateral Convolutional Neural Networks 2

Segmentation of Brain Tumors using traditional Multiscale bi...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Asha, P. Ankayarkanni, B. Joshila Grace, L.K. Kaarthik, K.M. Reddy, M. Hemanth Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665491990

Non-automated segmentation of brain tumors from the MRI images is a tedious task, which may sometimes produce inaccurate results. Therefore, the accuracy and reliability of brain tumor segmentation is also key to decision-making, treatment design, and analysis of treatment results. The automated brain tumor segmentation strategy uses manually developed parameters. As with convolutional neural networks, training requires excessive amount of annotated data that is generally challenging to retrieve in the medical field. The prior demand is the design of a task adapted along with robust feature selection and representation. This seems mandatory for effective and efficient segmentation of complicated brain tumors, which in turn is possible with two-channel Convolutional Neural Networks (CNN). Hence, two-channel CNN is adapted for the segmentation as it provided promising results for similar research. The two-channel CNN model contributes much in lessening jitter and over fitting parameters. Conclusively, a two-way cascade design to CNN is introduced, where the base CNN output is processed as an auxiliary source and combined at the final level. When testing the model in the BRATS2013 and BRATS2020 knowledge sets, the CNN integrated package in the 2-channel design was not hidden. Compared to the progressive method described above, the overall performance has been improved and the process quality remains attractive. © 2023 IEEE.

关键词： Decision making

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Wind Energy Production Prediction in Smart Industries

Wind Energy Production Prediction in Smart Industries

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Isravel, Deva Priya Viswamohan, R. Raj, Mejul Lawrence Arakal, Ashley John Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333244

The generation of wind power is dependent on wind speed, which can be challenging to control and predict due to its variable nature. By analysing the link between wind speed and power output, regression models for wind power curve modelling can help anticipate and optimise wind power generation. For the purpose of forecasting wind power, the authors of this study experimented with a number of regression models, including KNeighborsRegressor, Decision TreeRegressor, and ExtraTreesRegressor. They discovered that wind speed significantly affected power output and that regression modelling was a useful strategy for forecasting and maximising wind power generation. Compared to other models, the ExtraTreesRegressor model performed better. © 2023 IEEE.

关键词： Wind power

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Image-Based Bird Species Identification Using Machine Learning 9

Image-Based Bird Species Identification Using Machine Learni...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Persia Abishal, B. Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering TamilNadu India

ISBN: (纸本)9798350397376

Along with humans, birds lead lovely lives and are amazing creatures. One of the signs of climatic change is birds. From mid-level consumers to top predators, birds play a significant role in all trophic levels. Currently, extinction threatens many of these bird species. Naturally, each bird is unique in terms of its traits as well as its physical attributes, including shape, size, beak, feathers, silhouette, and others. As opposed to audio-based classification, bird photos are particularly useful in identifying species. By far, the most comfortable method for humans to identify birds is by visual classification. One of the crucial components of image categorization is the dataset of birds, which has been gathered. From the input image, the features are retrieved, and then classification is performed. Both regression and classification are performed using the Random Forest method. To create autographs using tensor flow, the input image is first transformed to grayscale and then to matrix format. As a consequence, the characteristics of the provided bird image are extracted, the bird's name is determined, and the bird's origin is also shown. © 2023 IEEE.

关键词： Birds

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Securing Cloud-based Data Sharing with Accountable Proxy Re-Encryption 4

Securing Cloud-based Data Sharing with Accountable Proxy Re-...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Mahalakshmi, E. Vaidhegi, A. Jayakumar, D. Ramkumar, M.O. Rajmohan, R. Department of Computer Science and Engineering Villupuram India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350349900

Cloud computing has been a considerable way to store and share data among different communities in the past few decades. However, it has always been a challenge to ensure that the data is secured during cloud data transfer. The proposed paper resolves the major challenges being faced in cloud data sharing with the new approach of Accountable Proxy-ReEncryption (APRE). The proposed paper deals with protocols and anticipates cryptography measures, namely homomorphic encryption, by ensuring two things: confidentiality and integrity. Both work on the shared data of the cloud. The major aspect of homomorphic encryption is that it has an inbuilt feature of function execution over the encrypted data without decryption. So, it communicates the data confidentially by maintenance. Thus, re-encryption of the proxy used in this research would also allow for safe delegation to the proxy servers. The APRE protocol brings about different accountability mechanisms. In this case, it deals with the aspect of transparency and data supervision in communication. The homomorphic signatures in the paper enable the data owner to monitor and audit the proxy functions. It strictly enforces the decryption policies and minimizes the rate of mishandling of shared information. In so doing, this layer boosts the trust of cloud data sharing, especially in terms of provision of confidentiality, preservation of privacy, and the allowance of the robustness of behaviour in verifying and tracking data access. Finally, this proposed system uses a comprehensive assessment framework to check the effectiveness and efficiency of the APRE protocol. It can, therefore, be inferred that from the outcome obtained from the results, this proposed system outperforms the other existing systems in security, performance, and effectiveness. © 2024 IEEE.

关键词： Cloud platforms

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