Recent Advances in Computer Science and Communications - Volume 18, Issue 3, 2025

In this study, we investigate human target detection techniques for infrared target recognition in complicated backdrops using deep learning approaches. This study uses the human target detection of infrared images in various scenes as its research object.

A test was conducted using MatlabR2010a and the real-time C language development platform. The findings indicate that, in the unoptimized case, the computing speed of the method was only 0.34 seconds. However, following optimization, its performance significantly increased to meet the real-time performance requirements.

The research findings presented in this work will be crucial to the relief and rescue efforts following engineering mishaps.

This study is innovative in that it develops a deep learning-based model for human target detection in infrared photos and thoroughly examines and improves its functionality.

Diabetic Retinopathy (DR), a significant cause of vision loss globally, is characterized by retinal damage caused by diabetes. Early detection is vital to prevent irreversible blindness, yet challenges remain in accurately identifying DR stages and enhancing blood vessel visibility in fundus images. This paper aims to develop an early detection methodology for DR, addressing the need for early diagnosis and the difficulties in distinguishing DR severity through fundus imaging. The challenges in the early detection of Diabetic Retinopathy (DR) and enhancing blood vessel visibility in fundus images are multifaceted, including issues such as data imbalance, image noise, and complex patterns. By addressing these challenges through advanced ML techniques and image processing methodologies, the proposed methodology in the paper aims to overcome the limitations in early detection and severity assessment of DR, contributing to improved patient outcomes and vision preservation.

This study utilizes Machine Learning (ML) to analyze complex patterns in fundus color images of DR, employing spatial domain filtering to reduce image noise and address data imbalances across DR severity levels through data augmentation. A Convolutional Neural Network (CNN), enhanced with a Gabor filter, is applied for stage-specific DR detection and to pinpoint infected areas. The dataset includes 1000 color fundus images, with a 70:30 split for training and testing, respectively. The adoption of a Gabor filtering technique aims to refine the model’s performance further.

The incorporation of a CNN with a Gabor filter has shown outstanding efficacy in detecting DR from fundus color images, achieving a training accuracy of 97.5%, validation accuracy of 96.5%, Cohen kappa score of 89.76%, and testing accuracy of 95.87%. This method effectively illustrates the disease-affected areas in the fundus images provided.

Through a comparative analysis of image processing techniques, this research highlights the advantages of using advanced DR analysis for image preprocessing. The proposed CNN-Gabor filter approach demonstrates significant success in identifying diabetic retinopathy in fundus color photographs and accurately delineating the affected regions, contributing valuable insights to the field of medical image processing.

The ability of fuzzy rule-based systems to handle the imprecision and uncertainty present in real-world problems is quite impressive. The assessment of any fuzzy rule-based system in itself is a complex task because its performance is dependent on two parameters, namely accuracy and interpretability. Accuracy is assessed based on quantitative results, whereas interpretability is entirely subjective and may vary according to domain expertise.

To develop an efficient fuzzy rule-based system, interpretability and accuracy should be taken into account, along with a good trade-off relationship between them. An interpretable fuzzy rule-based system has been developed in this paper for the classification of thyroid disease for an accurate diagnosis and prognosis of the thyroid.

The knowledge base for the classifier is built using three fuzzy rule induction algorithms (fuzzy decision tree, Wang Mendel, and fast prototyping) and two membership function generation schemes (k-means and HFP). Using these rule induction algorithms and membership functions, six different fuzzy-based classifiers have been suggested to deal with thyroid disorders, and their performance has been evaluated using accuracy and interpretability parameters.

Among them, using hfp and kmeans partition with fuzzy decision tree rule induction algorithm, the classifier attained the highest accuracy of 95.8% and 93.5% with a good interpretability factor of 0.023 and 0.033, respectively, which shows a quite satisfactory result.

The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis.

Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives.

This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances.

In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively.

Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis.

This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.

One of the challenging problems facing the modern Internet is spam, which can annoy individual customers and wreak financial havoc on businesses. Spam communications target customers without their permission and clog their mailboxes. They consume more time and organizational resources when checking for and deleting spam. Even though most web users openly dislike spam, enough are willing to accept lucrative deals that spam remains a real problem. While most web users are well aware of their hatred of spam, the fact that enough of them still click on commercial offers means spammers can still make money from them. While most customers know what to do, they need clear instructions on avoiding and deleting spam. No matter what you do to eliminate spam, you won't succeed. Filtering is the most straightforward and practical technique in spam-blocking strategies.

We present procedures for identifying emails as spam or ham based on text classification. Different methods of e-mail organization preprocessing are interrelated, for example, applying stop word exclusion, stemming, including reduction and highlight selection strategies to extract buzzwords from each quality, and finally, using unique classifiers to Quarantine messages as spam or ham.

The Nave Bayes classifier is a good choice. Some classifiers, such as Simple Logistic and Adaboost, perform well. However, the Support Vector Machine Classifier (SVC) outperforms it. Therefore, the SVC makes decisions based on each case's comparisons and perspectives.

Many spam separation studies have focused on recent classifier-related challenges. Machine Learning (ML) for spam detection is an important area of modern research. Today, spam detection using ML is an important area of research. Examine the adequacy of the proposed work and recognize the application of multiple learning estimates to extract spam from emails. Similarly, estimates have also been scrutinized.

3D printing is a rapidly growing technology with features of enhanced customizability, reduced errors, zero material waste, reduced costs, and quick turnaround times. In this work, the data were collected from the Derwent Innovation and Web of Science databases for patent and publication search, respectively.

The results were critically analysed and correlated with the global and Indian market growth. USA (with 5 out of the top ten patent contributing companies), China, Germany, France, and Taiwan were determined to be the top countries with the maximum number of patents on 3D printing technology. Both patents and publications exhibited consistent growth until 2011. From 2012 onwards, the rate of patent filings began to surpass that of academic publications, indicating a shift in the dynamics.

This trend has continued over the years, leading to a notable difference between the number of patents (19,322) and publications (10,571) in the year 2022. India has been found to rank 8^th in 3D printing innovation and research, globally.

In this study, the global and Indian market growth has been observed and the opportunities and challenges for the Indian market have been critically studied.