Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Macherla, Harshini; Kotapati, Ghamya; Sunitha, Manepalli Tulas; Chittipireddy, Koteswara Rao; Attuluri, Balaji; Vatambeti, Ramesh

doi:10.18280/isi.280219

Cited by 8 publications

(8 citation statements)

References 22 publications

(26 reference statements)

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“…Healthcare monitoring systems, often known as e-Health systems, rely on wireless sensor networks (WSNs) [5]. Many of today's available smart watches boast of being able to track your health in minute detail.…”

Section: Introductionmentioning

confidence: 99%

Designing a Smart IoT Environment by Predicting Chronic Kidney Disease Using Kernel Based Xception Deep Learning Model

Joteppa,

Balraj,

Cheruku

et al. 2024

RIA

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Chronic Kidney Disease (CKD) is often asymptomatic in its early stages, and patients may not experience noticeable symptoms until the disease has significantly progressed. This challenge in early detection results in patients seeking medical attention only when complications arise. Symptoms, when present, are nonspecific and vary widely among individuals, including fatigue, swelling, and changes in urination patterns, which may be mistakenly attributed to other conditions, leading to delayed diagnosis. In contemporary healthcare applications, the integration of Cloud Computing (CC) and the Internet of Things (IoT) has become commonplace. The cloud, with its superior processing capability compared to mobile devices, is particularly advantageous in analyzing the vast volumes of patient data generated by IoT devices. Machine Learning (ML) and Deep Learning (DL) models have gained interest in medical diagnostics due to their excellent prediction accuracy. This research introduces a novel method for diagnosing CKD using IoT and Cloud Computing. The selection of appropriate features and algorithms is crucial for optimizing the final model's performance. To address missing values and enhance results, a unique sequential approach is employed. Furthermore, the classification step utilizes m-Xception, employing a distinct architecture and breaking down the convolution layer into depth-based sub-layers linked by linear residuals. Effective model training results from a well-defined learning strategy. For selecting model kernel values, especially in large-scale examples, a Squeaky Wheel Optimization (SWO) metaheuristic is recommended. The projected model undergoes simulation testing on the canonical CKD dataset and is statistically evaluated. The findings suggest the feasibility of developing an automated method for estimating CKD severity. In conclusion, recent advances in predictive modeling and deep learning offer a fresh perspective on problem-solving, with potential applications in the field of renal illness and beyond.

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Section: Introductionmentioning

confidence: 99%

Designing a Smart IoT Environment by Predicting Chronic Kidney Disease Using Kernel Based Xception Deep Learning Model

Joteppa,

Balraj,

Cheruku

et al. 2024

RIA

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“…New evidence, however, suggests that traffic congestion may soon continue its rising trend over the past few years. Over $53 billion was lost in 2021 due to traffic congestion in the United States, up 41% from the previous year [8]. In this situation, effective measures to lessen traffic are of the utmost importance.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based RNN Model in a Traffic Control System for 5G-Envisioned Internet of Vehicles

Macherla,

Muvva,

Lella

et al. 2024

MMEP

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In metropolitan areas, traffic jams on city streets are a major source of annoyance and financial losses. Recent advancements in data processing algorithms and the widespread availability of traffic detectors have made it possible to implement data-driven strategies for reducing traffic congestion. In order to benefit from intersection cooperation in this setting, this paper presents a distributed control strategy based on RL. In this scenario, traffic prediction software's embedding that takes into account the state of nearby junctions is used to synthesize an RL controller that controls the traffic lights. Loop detector characteristics are insufficient for precise data imputed in sophisticated traffic control systems. Most current imputation methods only use these extracted characteristics, which leads to the creation of data replicas that lack the necessary precision. The clean data are first given a statistical multi-class label, with classes ranging from C1 to Cn. Then, using a deep recurrent neural network (RNN) model, the best data model is created from the labelled spotless data and applied to the class of models in the missed-volume data. Results from simulations using TRANSYT demonstrate that the suggested strategy outperforms conventional methods in terms of waiting times and other important presentation indices.

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“…A precise diagnostic tool capable of distinguishing benign from malignant tumors is therefore indispensable for early detection. Traditionally, mammography has been the gold standard for early detection and diagnosis of breast cancer [2]. However, its applicability is circumscribed, especially in detecting cancer in young women with dense breast tissue.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Speckle Noise Reduction in Breast Cancer Ultrasound Imagery Using a Hybrid Deep Learning Model

Reddy,

Chitteti,

Yesupadam

et al. 2023

ISI

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Ultrasonic imaging serves as a pivotal tool in mitigating overdiagnosis of breast cancer in women, owing to its high sensitivity, low false-positive rate, and ability to reduce unnecessary biopsies. Nevertheless, these images are impaired by speckle noise, which appears as granular interference obscuring tissue boundaries and diminishing image contrast. This noise impedes subsequent image processing tasks such as edge detection, segmentation, feature extraction, and classification. Existing strategies for speckle noise reduction in ultrasonic images either compromise on effectiveness or demand substantial processing time, presenting challenges in preserving fine edge details. Addressing these issues, we propose an innovative hybrid deep learning model, FCNN-IDOA, which synergizes a Fundamental Convolutional Neural Network (FCNN) with an optimization algorithm. Our FCNN model is built upon the framework of GoogLeNet, enhanced with fifteen additional layers to augment its expressiveness. Subsequently, an Improved Dragonfly Optimization Algorithm (IDOA) is deployed to optimize FCNN's parameters, thereby improving the computational efficiency of the model. The suggested model has demonstrated superior performance, outstripping previous models in terms of accuracy. During experimental validation, the model achieved an average t(s) value of 84.764421, a PSNR value of 66, an MSE value of 54.9143, an RMSE value of 0.491631, and a final t(s) value of 83.759067. The results indicate that this novel model significantly outperforms the BC models, rendering it a promising solution for speckle noise reduction in breast cancer ultrasound images.

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Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Cited by 8 publications

References 22 publications

Designing a Smart IoT Environment by Predicting Chronic Kidney Disease Using Kernel Based Xception Deep Learning Model

Designing a Smart IoT Environment by Predicting Chronic Kidney Disease Using Kernel Based Xception Deep Learning Model

A Deep Reinforcement Learning-Based RNN Model in a Traffic Control System for 5G-Envisioned Internet of Vehicles

Enhanced Speckle Noise Reduction in Breast Cancer Ultrasound Imagery Using a Hybrid Deep Learning Model

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