Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection

Jul 30, 2025

Journal Applied Sciences

publisher MDPI

DOI https://doi.org/10.3390/app15158448

Issue 15

Volume 15

Breast cancer continues to be one of the leading causes of women’s deaths around the world, and this has emphasized the necessity to have novel and interpretable diagnostic models. This work offers a clear learning deep learning model that integrates the mobility of MobileNet and two bio-driven optimization operators, the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA), in an effort to boost classification appreciation and the convergence of the model. The suggested model demonstrated excellent findings as the DOA-optimized MobileNet acquired the highest performance of 98.96 percent accuracy on the fusion test, and the FLA-optimized MobileNet scaled up to 98.06 percent and 95.44 percent accuracies on mammographic and ultrasound tests, respectively. Further to good quantitative results, Grad-CAM visualizations indeed showed clinically consistent localization of the lesions, which strengthened the interpretability and model diagnostic reliability of Grad-CAM. These results show that lightweight, compact CNNs can be used to do high-performance, multimodal breast cancer diagnosis.

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Use of chest x-ray images and artificial intelligence methods for early diagnosis of covid-19

Jul 17, 2025

Journal Politeknik Dergisi

publisher Gazi University

DOI https://doi.org/10.2339/politeknik.1654887

Issue Year 2025 EARLY VIEW

The worldwide epidemic brought on by COVID-19 has substantially hurt people’s health. To discover and treat ill people, given the significant usage of efficient screening and diagnostic methods, as well as a crucial way to this deadly illness. One strategy that might be used to help with COVID-19 early diagnosis is to make use of X-ray pictures of individuals’ chests. Different Computer Aided Diagnosis (CAD) methods have been created to aid doctors in doing this work by providing them more extra information and suggestions. This investigation uses pictures of chest X-rays taken to create a CAD method for COVID-19 illness. Convolutional Neural Network (CNN), Resnet50, Xception, Densnet, Mobilenet, VGG16, Resnet152v2, and Inceptionv3 will use in the investigation to examine the pictures and remark on automatic detection and categorization of COVID-19 cases. The effectiveness of each method will be examined on a big collection of chest X-ray pictures to identify its accuracy and reliability in detecting COVID-19 cases. The result of this investigation could be used to design an effective and reliable tool for COVID-19 diagnosis and evaluation.

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Decentralized security and data integrity of blockchain using deep learning techniques

Sep 3, 2020

Journal Periodicals of Engineering and Natural Sciences

DOI 10.21533/pen

Issue 3

Volume 8

Since the introduction of blockchain, cryptocurrencies have become very attractive as an alternative digital payment method and a highly speculative investment. With the rise in computational power and the growth of available data, the artificial intelligence concept of deep neural networks had a surge of popularity over the last years as well. With the introduction of the long short-term memory (LSTM) architecture, neural networks became more efficient in understanding long-term dependencies in data such as time series. In this research paper, we combine these two topics, by using LSTM networks to make a prognosis of decentralized blockchain security. In particular, we test if LSTM based neural networks can produce profitable trading signals for different blockchains. We experiment with different preprocessing techniques and different targets, both for security regression and trading signal classification. We evaluate LSTM based networks. As data for training we use historical security data in one-minute intervals from August 2019 to August 2020. We measure the performance of the models via back testing, where we simulate trading on historic data not used for training based on the model’s predictions. We analyze that performance and compare it with the buy and hold strategy. The simulation is carried out on bullish, bearish and stagnating time periods. In the evaluation, we find the best performing target and pinpoint two preprocessing combinations that are most suitable for this task. We conclude that the CNN LSTM hybrid is capable of profitably forecasting trading signals for securing blockchain, outperforming the buy and hold strategy by roughly 30%, while the performance was better. The LTSM method used by current system for encrypting passwords is efficient enough to mitigate modern attacks like man in the middle attack (MITM) and DDOS attack with 95.85% accuracy

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Low energy consumption in manet network

Jun 2, 2020

Journal Periodicals of Engineering and Natural Sciences

DOI 10.21533/pen

Issue 2

Volume 8

The aim of this paper is design and develop energy efficient MANET network in wireless networks. One of the most significant and effective protocol based on low energy consumption and number of Ad-hoc is MANET as remote directing convention source nodes forward in network simulator. Less number of nodes in the network would give low energy usage or consumption as the nodes in the network exceeds or increases that will also increase the energy consumption in the network. The designed MANET system is tried with 9, 12, 15 and 18 number of nodes in a system using network simulation-2 (NS-2). Henceforth source node needs to restart over and over which brings about low energy consumption use and use, ectiveness is less and packet space is additionally less and throughput is likewise less and more start to finish delay. Arrangement of this issue in MANET convention which is advanced as the node doesn't advance when demand arrived at their first it checked there is low energy consumption (battery lifetime) and until the node energy consumption is more noteworthy than the limit. Designed MANET examinations of the energy consumption and node energy consumption by maintaining a strategic distance from the low number of nodes in a network. By contrasting energy consumption and node it demonstrates that MANET is far superior to existing framework 802.11 protocol convention based on battery lifetime, energy consumption, throughput, and power transmission. We have performed a comparison between EEM and AODV routing protocol considering different measuring parameters.

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In the Way of having an Optimum Wireless Ad-Hoc Sensor Networks: Analysis of Deploying Homogeneous and Inhomogeneous Nodes(

Dec 1, 2019

Journal ARPN Journal of Engineering and Applied Sciences

publisher Asian Research Publishing Network

DOI 10.21533/pen

Issue 14

Volume Special 8

The emerging technology of ad-hoc sensor networks helped to increase researches in this field due to a large number of applications that uses wireless ad hoc networks such as monitoring of the environment, intelligent agriculture, structure health, earthquake prediction, industrial control and target detection in military applications. Various analyses have been suggested for optimality of ad hoc networks; in our study for a given number of nodes we use a comparative analysis by using two kinds of sensors network, the first network with different type of sensors having different connectivity range and sensing coverage and the other network with same capabilities for sensors in terms of connectivity and coverage range and the aim is to have a clear view about the utility of deploying homogeneousor inhomogeneous wireless ad hoc sensor network. Analysis of sensors deployment with a homogeneous transmission range reveals better network connectivity. Therefore, deploying sensor nodes with different transmission range does not improve the connectivity of the network when a power constraint present. In addition, using inhomogeneous nodes does not help to reduce power consumption to maintain network availability. © 2019 Medwell Journals. All Rights Reserved.