Optimized Nature-Inspired Computing Algorithms for Lung Disorder Detection

Narayana, V. Lakshman; Patibandla, R. S. M. Lakshmi; Pavani, V.; Radhika, P.

doi:10.1007/978-981-19-6379-7_6

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…Through a comprehensive exploration of optimization-based approaches conducted by various researchers, their relevance for enhancing the AMISE approach becomes evident. This review encompasses a broad spectrum of optimization strategies, including traditional optimization algorithms, metaheuristic approaches, and machine-learning-based optimization [58][59][60], all of which resonate with the innovative and advanced nature of our proposed method. These techniques aim to address the intricate challenges of medical image segmentation and to ultimately optimize algorithm parameters to improve the segmentation outcomes, and, by extension [24,33], to advance the core concept of image enhancement that underpins our research.…”

Section: Optimization-based Approachesmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-score/Dice, specificity, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially for chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach for advancing the accuracy and reliability of medical image segmentation and pave the way for further research and method integration to enhance this critical healthcare application.

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Section: Optimization-based Approachesmentioning

confidence: 99%

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Saifullah,

Dreżewski

2024

Applied Sciences

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“…Because they are unable to employ complex security measures, these low-power devices are particularly susceptible to infestation. An anomaly detection solution for IoT networks that utilizes edge computing to uncover hidden threats is proposed by Lakshman Narayana et al [16] and called ADRIoT. A traffic preprocessor, a collection of anomaly detectors tailored to individual devices, and a traffic capturer are all components of an edge's detection module.…”

Section: Mishra Et Al [10] 2020mentioning

confidence: 99%

“…Worms, backdoors, malware, rootkits, adware, and so on are all examples of malware that can manifest in different ways and cause different types of damage [15]. Because most modern malware uses many polymorphic layers to avoid detection or uses side processes to automatically upgrade to a newer version at relatively short intervals, traditional signature-based malware detection methods are becoming more and more useless [16].…”

Section: Introductionmentioning

confidence: 99%

Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review

Rao,

Jain

2024

IJSSE

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Cloud computing, integral for data storage and online services, presents significant advantages over traditional data storage and distribution methods, including enhanced convenience, on-demand storage, scalability, and cost efficiency. Its growing adoption in securing Internet of Things (IoT) and cyber-physical systems (CPS) against various cyber threats offers numerous opportunities. Despite the continuous evolution of malware and the lack of a universally effective detection method, cloud environments provide a promising approach for malware detection. Cloud computing, recognized for its efficiency, scalability, flexibility, and reliability on elastic resources, is widely utilized in the IT industry to support IT infrastructure and services. However, one of the foremost security challenges faced is malware attacks. Certain antivirus scanners struggle to detect metamorphic or encrypted malware in cloud environments due to complexity and scale, allowing such threats to evade detection. High detection rates with precision in reducing false positives are essential. Machine learning (ML) classifiers, a vital component in Artificial Intelligence (AI) systems, require training on extensive data volumes to develop credible models with high detection rates. Traditional detection methods face challenges in identifying complex malware, as modern malware employs contemporary packaging and obfuscation techniques to circumvent security measures. This paper provides a detailed discussion on detecting malware in cloud environments and the advantages of cloud computing in safeguarding IoT and CPS from cyber attacks. It presents a survey on malware analysis and detection models, aiding researchers in identifying limitations of traditional malware detection models in cloud environments and inspiring the design of innovative models with enhanced quality of service levels.

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“…Unfortunately, memory size, energy capacity, and computational capabilities are typically limited on low-cost sensor nodes [14]. It is usually not feasible to use centralized administration for many encryption and authentication schemes in routing protocols [15]. Adversaries may use physical ways to compromise sensor nodes put in an unsupervised region [16].…”

Section: Introductionmentioning

confidence: 99%

Network Master Node Assessed Trust Factor with Arbitrary Neighbor Assessment for Secure Route Detection in 6G Enabled Wireless Sensor Networks

Movva,

Chintala

2024

IJSSE

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The wireless research community has been concentrating on sixth-generation (6G) wireless technology. One of the new paradigms brought forward by 6G is its extensive global coverage, enormous spectrum consumption, sophisticated new applications, and tight security. Nevertheless, current classical computers may lack the computational capability necessary to fully realize such features. Already, major IT firms are investigating quantum computers, which might be used as 6G enablers. A growing number of people are opting for 6G enabled wireless sensor networks (WSNs) due to its potential low cost and broad use. Malicious or self-serving nodes, in addition to broken nodes, can significantly reduce a network's performance. Most trust management schemes come with a powerful tool that can detect unusual node behavior. This research goal is to secure WSNs against malicious attacks that take advantage of the replay of routing information, hence a strong trust based routing model has been devised and built to provide safe routing options for WSNs. This research proposes a Network Master Node assessed Trust Factor with Arbitrary Neighbor Assessment (NMN-TF-ANA) for Secure Route Detection in WSN. The proposed model considers nodes in routing based on trust factor and random neighbor evaluation to achieve secure data transmission. The proposed model when contrasted with existing model achieves better performance in route selection.

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Optimized Nature-Inspired Computing Algorithms for Lung Disorder Detection

Cited by 12 publications

References 27 publications

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach

Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review

Network Master Node Assessed Trust Factor with Arbitrary Neighbor Assessment for Secure Route Detection in 6G Enabled Wireless Sensor Networks

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