Intelligent Deep Learning Enabled Oral Squamous Cell Carcinoma Detection and Classification Using Biomedical Images

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 11

Опубликована: Июнь 30, 2022

Oral cancer is one of the lethal diseases among available malignant tumors globally, and it has become a challenging health issue in developing low-to-middle income countries. The prognosis oral remains poor because over 50% patients are recognized at advanced stages. Earlier detection screening models for mainly based on experts' knowledge, necessitates an automated tool detection. recent developments computational intelligence (CI) computer vision-based approaches help to accomplish enhanced performance medical-image-related tasks. This article develops intelligent deep learning enabled squamous cell carcinoma classification (IDL-OSCDC) technique using biomedical images. presented IDL-OSCDC model involves recognition proposed employs Gabor filtering (GF) as preprocessing step eliminate noise content. In addition, NasNet exploited generation high-level features from input Moreover, grasshopper optimization algorithm (EGOA)-based belief network (DBN) employed classification. hyperparameter tuning DBN performed EGOA which turn boosts outcomes. experimentation outcomes benchmark imaging dataset highlighted its promising other methods with maximum accu y , prec n reca l F score 95%, 96.15%, 93.75%, 94.67% correspondingly.

Язык: Английский

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MLP-Like Model With Convolution Complex Transformation for Auxiliary Diagnosis Through Medical Images

IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 27(9), С. 4385 - 4396

Опубликована: Июль 19, 2023

Medical images such as facial and tongue have been widely used for intelligence-assisted diagnosis, which can be regarded the multi-label classification task disease location (DL) nature (DN) of biomedical images. Compared with complicated convolutional neural networks Transformers this task, recent MLP-like architectures are not only simple less computationally expensive, but also stronger generalization capabilities. However, models require better input features from image. Thus, study proposes a novel convolution complex transformation (CCT-MLP) model DL DN recognition Notably, Tokenizer multiple layers first to extract shallow make up loss spatial information obtained by MLP structure. Subsequently, Channel-MLP architecture transformations is deep-level contextual features. In way, multi-channel extracted mixed perform Experimental results on our constructed image datasets demonstrate that method outperforms existing methods in terms both accuracy (Acc) mean average precision (mAP).

Язык: Английский

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Energy Efficient Path Planning Scheme for Unmanned Aerial Vehicle Using Hybrid Generic Algorithm-Based Q-Learning Optimization

IEEE Access, Год журнала: 2023, Номер 12, С. 13400 - 13417

Опубликована: Дек. 19, 2023

Efficient path planning optimization strategies are required to maximize flying time while consuming the least energy. This research offers a novel approach for energy-efficient Unmanned Aerial Vehicles (UAVs) that combines hybrid evolutionary algorithm and Q-learning accounting UAV's velocity distance from obstacles. To overcome constraints of traditional approaches, methodology genetic algorithms Q-learning. The suggested optimizes path-planning decisions based on real-time information by considering Genetic Algorithm (GA) creates wide collection candidate pathways. In contrast, uses reinforcement learning make educated selections present proximity static integration allows UAV modify its dynamically energy requirements environmental constraints. main goal is develop scheme capable dealing with obstacle-filled environments improve efficiency collision avoidance during flight missions. Our experimental results show technique outperforms classical GA method in terms significantly reducing consumption maintaining suitable rate best cost desired locations. analysis performance GA/QL more than 57.14% compared GA.

Язык: Английский

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Task Reverse Offloading with Deep Reinforcement Learning in Multi-Access Edge Computing

Опубликована: Авг. 15, 2023

The Multi-access Edge Computing (MEC) technology's quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine data and produce tasks, crowd-sensing will be transferred to MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for decide appropriately based on from devices infrastructure. This study builds a framework reverse offloading that carefully balances relationship between task completion time user mobile energy consumption. Moreover, decrease system use generally, an adaptive optimal method Deep Q-Network is created (DQN). results of simulations demonstrate suggested approach may successfully minimize consumption work latency when compared full local fixed techniques.

Язык: Английский

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Intelligent deep learning supports biomedical image detection and classification of oral cancer

Technology and Health Care, Год журнала: 2024, Номер 32, С. 465 - 475

Опубликована: Май 14, 2024

Oral cancer is a malignant tumor that usually occurs within the tissues of mouth. This type mainly includes tumors in lining mouth, tongue, lips, buccal mucosa and gums. on rise globally, especially some specific risk groups. The early stage oral asymptomatic, while late may present with ulcers, lumps, bleeding, etc.

Язык: Английский

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Green Machine Learning Approach for QoS Improvement in Cellular Communications

2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Год журнала: 2022, Номер unknown, С. 523 - 528

Опубликована: Май 23, 2022

Green cellular communications are becoming an important approach due to large-scale and complex radio networks. Due the dynamic network behaviors related interference distribution, traffic bottlenecks, congestion points, hotspots, there is a need evaluate processes in systems addition ensuring spectrum availability. The delay, loss rate, SNR most issues that may affect communication performance. Artificial intelligent algorithms such as machine learning (ML) enable detection of dynamics networks by analyzing evaluating links qualities. It enables extraction knowledge from autonomously. extracted information helps know about every change wireless parameters, frequency, modulation, route selection, etc. This paper provides details use ML green efficiently upgrade enhances different approaches including quality services (QoS), signal load, energy efficiency, which critical paradigms. also presents technical concept solve significant problems communications, future aspects considerations for consumption minimization using communications.

Язык: Английский

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Enhancing Medical Services Through Machine Learning and UAV Technology

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 307 - 343

Опубликована: Янв. 17, 2024

This chapter focuses on the enhancement of medical services through integration unmanned aerial vehicle (UAV) technology and machine learning algorithms. It explores broad spectrum applications benefits that arise from combining these two technologies. By employing UAVs for automated delivery, supplies can be efficiently transported to remote or inaccessible regions, thereby improving access vital items. Remote patient monitoring, facilitated learning, enables real-time data collection analysis, enabling early identification health issues. equipped with equipment capabilities enhance emergency response by providing immediate assistance during critical situations. Disease surveillance outbreak management benefit use machine-learning algorithms identify disease hotspots predict spread illnesses.

Язык: Английский

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Deep Learning Approaches for Object Detection in Autonomous Driving: Smart Cities Perspective

Опубликована: Янв. 1, 2025

Язык: Английский

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Attacks Detection in 6G Wireless Networks using Machine Learning

Опубликована: Авг. 15, 2023

Unlike the fifth generation (5G), which is well recognized for network cloudification with micro-service-oriented design, sixth (6G) of networks directly tied to intelligent orchestration and management. The Attacks Detection in 6G (AD6Gs) wireless created by this research uses a Machine Learning (ML) algorithm. pre-processing stage ML-AD6Gs process initial step. second involves feature selection approach. Correlation Feature Selection algorithm (CFS) used implement suggested hybrid strategy. It selects best subset reduces dimensionality each independent analyses dataset CICDDOS2019. voting average method as an aggregation step, two classifiers—Random Forest (RF) Support Vector (SVM)—are modified be ML Algorithms. proposed shown outperformed existing classification method. accuracy was 99.9%% CICDDOS2019 false alarm rate 0.00102

Язык: Английский

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Chaotic Sea Horse Optimization with Deep Learning Model for lung disease pneumonia detection and classification on chest X-ray images

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(27), С. 69825 - 69847

Опубликована: Фев. 2, 2024

Язык: Английский

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