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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Evaluating Mobility Models for IH-VANETs: A Simulation-Based Analysis

Evaluating Mobility Models for IH-VANETs: A Simulation-Based...

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International Conference on computer and communications Technologies (ICCCT)

作者： Raghu Ramamoorthy Anitha Velu C Valarmathi M. Ananthi Department of Computer Science and Engineering The Oxford College of Engineering Bengaluru India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Intelligent Heterogeneous Vehicular ad hoc networks (IH-VANETs) are more dynamic due to the mobility of vehicles. Routing packets along efficient paths improves the efficiency of the routing schemes. Relying on efficient mobility models for traffic generation for real-time road scenarios is conducive to efficient routing. This work presents and evaluates the performance of various mobility models including the Random Waypoint Mobility Model (RWP), Intelligent Driver Model (IDM), Intelligent Driver Model-Intersection Management (IDM-IM), and Manhattan Mobility Model (MMM). The reactive protocol such as Dynamic Source-Routing (DSR) is used with mobility models to evaluate the efficiency in the form of Packet Loss Ratio (PLR), End-to-End Delay (EED), Throughput (T), Packet Delivery Rate (PDR), and Overhead of routing (OR). DSR and mobility models are implemented in Network Simulator (NS 2.35). Simulations show that utilizing real-time traffic with mobility models affects DSR performance in all aspects.

关键词： Protocols Mobility models Roads Vehicular ad hoc networks Packet loss Routing Throughput Real-time systems Vehicle dynamics Vehicles

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Pretrained Model with CNN for Lung Cancer Identification and Classification 7

Pretrained Model with CNN for Lung Cancer Identification and...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Kanwal, Shaista Ali, Sajid Alahmadi, Tahani Jaser Assam, Muhammad Ansari, Ghulam Jillani Bhatti, Uzair Aslam University of Education Department of Information Sciences Lahore Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Department of Information Systems P.O. Box 84428 Riyadh Saudi Arabia University of Science and Technology Department of Software Engineering KPK Bannu Pakistan School of Information and Communication Engineering Hainan University China

ISBN: (纸本)9798350350890

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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State-aware Real-time Tracking and Remote Reconstruction of a Markov Source

arXiv

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arXiv 2023年

作者： Salimnejad, Mehrdad Kountouris, Marios Pappas, Nikolaos The Department of Computer and Information Science Linköping University Sweden The Communication Systems Department EURECOM Sophia-Antipolis France

The problem of real-time remote tracking and reconstruction of a two-state Markov process is considered here. A transmitter sends samples from an observed information source to a remote monitor over an unreliable wireless channel. The receiver, in turn, performs an action according to the state of the reconstructed source. We propose a state-aware randomized stationary sampling and transmission policy which accounts for the importance of different states of the information source, and their impact on the goal of the communication process. We then analyze the performance of the proposed policy, and compare it with existing goal-oriented joint sampling and transmission policies, with respect to a set of performance metrics. Specifically, we study the real-time reconstruction error, the cost of actuation error, the consecutive error, and a new metric, coined importance-aware consecutive error. In addition, we formulate and solve a constrained optimization problem that aims to obtain the optimal sampling probabilities that minimize the average cost of actuation error. Our results show that in the scenario of constrained sampling generation, the optimal state-aware randomized stationary policy outperforms all other sampling policies for fast evolving sources, and, under certain conditions, for slowly varying sources. Otherwise, a semantics-aware policy performs better only when the source is slowly varying. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

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Importance-aware Sampling of a Two-State Markov Source for Real-time Tracking

Importance-aware Sampling of a Two-State Markov Source for R...

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Asilomar Conference on Signals, systems & computers

作者： Mehrdad Salimnejad Marios Kountouris Nikolaos Pappas Department of Computer and Information Science Linkoping University Linkoping Sweden Communication Systems Department EURECOM Sophia-Antipolis France

This work considers the problem of real-time remote tracking and reconstruction of a two-state Markov process for actuation. The transmitter sends samples from an observed information source to a remote monitor over an unreliable wireless channel. We propose a state-aware randomized stationary sampling and transmission policy, which considers the importance of different states and their impact on the communication objective. We then analyze the performance of the proposed policy and compare it with existing goal-oriented joint sampling and transmission policies using relevant metrics. Specifically, we assess the real-time reconstruction error, the cost of actuation error, and the consecutive error metrics. Furthermore, a constrained optimization problem is formulated and solved so as to minimize the average cost of actuation error by determining optimal sampling probabilities. Our results show that the optimal state-aware randomized stationary policy outperforms other policies in scenarios with constrained sampling for fast-evolving sources. In addition, when the source changes slowly, although the semantics-aware policy tends to be more effective, the optimal state-aware randomized stationary policy excels under certain conditions.

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Real-time Reconstruction of Markov Sources and Remote Actuation over Wireless Channels

arXiv

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arXiv 2023年

作者： Salimnejad, Mehrdad Kountouris, Marios Pappas, Nikolaos The Department of Computer Information Science Linköping University Sweden The Communication Systems Department EURECOM Sophia-Antipolis France

In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely an N-state Markov chain and an N-state Birth-Death Markov process. We investigate several joint sampling and transmission policies, including a semantics-aware one, and we study their performance with respect to a set of metrics. Specifically, we investigate the real-time reconstruction error and its variance, the cost of actuation error, the consecutive error, and the cost of memory error. These metrics capture different characteristics of the system performance, such as the impact of erroneous actions and the timing of errors. In addition, we propose a randomized stationary sampling and transmission policy and we derive closed-form expressions for the aforementioned metrics. We then formulate two optimization problems. The first optimization problem aims to minimize the time-averaged reconstruction error subject to time-averaged sampling cost constraint. Then, we compare the optimal randomized stationary policy with uniform, change-aware, and semantics-aware sampling policies. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best. The objective of the second optimization problem is to obtain an optimal sampling policy that minimizes the average consecutive error with a constraint on the time-averaged sampling cost. Based on this, we propose a wait-then-generate sampling policy which is simple to implement. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Semantic Filtering and Source Coding in distributed Wireless Monitoring systems

arXiv

引用

arXiv 2023年

作者： Agheli, Pouya Pappas, Nikolaos Kountouris, Marios Communication Systems Department EURECOM Sophia-Antipolis France Department of Computer and Information Science Linköping University Sweden

The problem of goal-oriented semantic filtering and timely source coding in multiuser communication systems is considered here. We study a distributed monitoring system in which multiple information sources, each observing a physical process, provide status update packets to multiple monitors having heterogeneous goals. Two semantic filtering schemes are first proposed as a means to admit or drop arrival packets based on their goal-dependent importance, which is a function of the intrinsic and extrinsic attributes of information and the probability of occurrence of each realization. Admitted packets at each sensor are then encoded and transmitted over block-fading wireless channels so that served monitors can timely fulfill their goals. A truncated error control scheme is derived, which allows transmitters to drop or retransmit undelivered packets based on their significance. Then, we formulate the timely source encoding optimization problem and analytically derive the optimal codeword lengths assigned to the admitted packets which maximize a weighted sum of semantic utility functions for all pairs of communicating sensors and monitors. Our analytical and numerical results provide the optimal design parameters for different arrival rates and highlight the improvement in timely status update delivery using the proposed semantic filtering, source coding, and error control schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Drops

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract)

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

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International Conference on Data Engineering

作者： Wei Fang Haipeng Jiang Hengyang Lu Jun Sun Xiaojun Wu Jerry Chun-Wei Lin Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Distributed Systems and IT Devices Silesian University of Technology Poland

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

关键词： Runtime Itemsets Heuristic algorithms Sociology Approximation algorithms Search problems Topology

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Smart Pregnancy Monitoring and Data Analyzing System for Rural Woman

Smart Pregnancy Monitoring and Data Analyzing System for Rur...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Saravanan, R. Palanikumar, S. Kumar, M. Lenin Department of Computer Science & Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Department of Computer Science & Business Systems Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai India Department of Electronics & Communication Engineering Vignan Institute of Technology and Sciences Telangana India

ISBN: (纸本)9798350305708

Monitoring pregnancy woman health is continuos and vital process. Approximately 830 women die a day from pregnancy-related causes in India. In developing countries, still more number of maternal death is happening and the medical information is not centralized for sharing. In rural areas, there is a lack of adequate facilities to the pregnant women for their routine checkup and this ends up with a higher infant and maternal death rate. The resulting health issue is a challenge to rural community people. With the recent developments in the IoT based technology, pregnant women are continuously monitored through suitable sensors. The sensor data are streamed to the centralized server through wireless technology for monitoring and performing data analytics. The proposed work utilizes an acceleration wireless sensor to observe the live movement of the fetus, along with scanned ultrasound images. Other vital parameters including movement sign, pulse, pressure, SPO2, sleep, count the amount of kicks of the unborn baby, and measuring the ECG pattern of the pregnant women. The goal of the proposed work is to effectively monitor the health condition of pregnant woman through sensors and if any abnormality is being observed, it will send an early warning message to the medical professional. In the proposed architecture, a massive amount of unstructured sensor data is collected through microcontrollers and the data are streamed to Apache Kafka and stored in MongoDB. Results shows that, a complete pregnant woman monitoring system is developed and data analytics can be performed, which enable the medical professional and other recipient to monitor pregnant women's activities from anywhere at any time. In the case of emergency, an email/SMS notifications send to the doctor and other recipients. © 2023 IEEE.

关键词： Apache Kafka Apache Storm ECG MongoDB SoC

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