Climate Change and Sustainability: A Review

Balaji Vijayan; Ankitha; Chandana; Likitha

doi:https://doi.org/10.54216/JCHCI.090202

Fusion: Practice and Applications | 2025

Image recognition via Local 3-bit Binary Patterns

The current study introduces a trainable object detection model that can be taught to detect an object of a given class within an unconstrained scene. The researchers of the current study use this advanced system in the detection of Relics images, which involves a calculation of Local 3bit Binary Patterns (3bit-LBP). The key highlights of the current work include the integration and analyses of the utilization of the Multi-Support Vector Machine Classification (MSVMC) and Integral image computation analysis. The experimental outcomes of the current study indicate that the method of 3bit-LBP is superior to other methods in accuracy and stability, especially when images of different illumination and object rotation were tested. The researchers further conducted a comparative performance evaluation showing that the presented system gives better detection rates as compared to the conventional strategies, revealing the efficiency in real-world applications. Finally, it is important to note that the implications of the results can be applied to uses beyond just relic detection. To conclude, the current work advances the knowledge of how to improve the functionality of object recognition algorithms further in the context of image recognition systems.

groups

Abdulaziz Saleh Alraddadi mail -

Essam O. Abdel-Rahman mail

link https://doi.org/10.54216/FPA.200106

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Hybridization of Deep Learning Model with Optimization Algorithm for DNA Based Genetic Disorders Detection and Classification

Genetic diseases are diseases produced by anomalies in the DNA of the person. These abnormalities may be larger-scale chromosomal mutations or irregularities in the particular gene. These diseases significantly influence some body functions and systems and are hereditary or develop automatically. Traditional models such as genetic testing and karyotyping might fail to identify complex or rare modifications, requesting more detailed techniques namely whole-genome sequencing (WGS). In recent decades, regardless of important technological evolution, uncommon genetic diseases continue to cause problems, with a significant portion of patients (50–66%) remaining unidentified according to clinical condition alone. An accurate analysis is important to provide equal support to patients and their relations, despite particular therapeutic intrusions. Presently, machine learning (ML), and in detail the DL subspecialties, have been utilized to determine clinically relevant prediction devices in other medical areas. For mental disorders, ML methods have presented major promise in forecasting either diagnosis or prediction in mental disorders. In this manuscript, we design and develop a Hybrid Deep Learning and Metaheuristic Optimization Algorithm for Detecting Genetic Disorders (HDLMOA-DGD) model. The proposed HDLMOA-DGD algorithm's main goal is to detect and classify genetic disorders using an advanced deep-learning model. At first, the Z-score normalization is employed in the data pre-processing phase for converting an input data into a uniform format. Moreover, the proposed HDLMOA-DGD model implements a hybrid deep learning model of the temporal convolutional network, bi-directional long- and short-term memory network, and Self-Attention mechanism (TCN-BiLSTM-SA) technique for the classification process.  At last, the modified gannet optimization algorithm (MGOA)-based hyperparameter selection process is performed to optimize the detection and classification results of the TCN-BiLSTM-SA system. The experimental validation of the HDLMOA-DGD model is verified on a benchmark dataset and the results are determined regarding several measures. The experimental outcome underlined the development of the HDLMOA-DGD model in the genetic disorder detection process.

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S. Puvaneswari mail -

G. Indirani mail

link https://doi.org/10.54216/FPA.200107

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Some Results on Neutrosophic Graphs in Neutrosophic Topological space

A tools and techniques of neutrosophic graph have found many applications in different areas such as topology, networks, computer of science, etc. In addition, neutrosophic graph is a generalization of intuitionistic fuzzy graph. Therefore, in this paper we study some characteristics of neutrosopheic graphs (NTCG) and some basic definitions. Moreover we investigate several kinds of arcs,  -strong, -strong,  -arc, and  -strong, -strong,  –arc in neutrosopheic graphs (NTCG) , Finally we give neutrosophic -bridge and neutrosophic -bridge (NTC -bridge) and some interesting properties of  neutrosophic bridge (NTCB), which is being taught for the first time, and obtain several important properties.

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Gazwan Haider Abdulhusein mail -

Dalia Raad Abd mail -

Wadei Faris AL-Omeri mail

link https://doi.org/10.54216/IJNS.260319

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Explainable Artificial Intelligence Driven Intrusion Detection System for Enhancing Reliability and Interpretability in IoT Based Network Security Solutions

The implementation of Intrusion Detection Systems (IDS) remains crucial for network security yet high-dimensional data alongside class imbalance issues decrease their functionality. Machine learning-based IDS models, which use traditional approaches experience difficulties in providing explanations about their prediction results. An IDS framework enhancement with explainable AI (XAI) methods aims at improving the system's transparency throughout this study. The data processing includes KNN imputation combined with K-Means SMOTE to handle missing information and class imbalance problems. When selecting features the model uses a merged methodology combining Pearson Correlation with Mutual Information and Sequential Forward Floating Selection (SFFS) algorithms for optimization. Light Gradient Boosting Model (LGBM) serves as the classification model that produces higher accuracy than competing methods with 90.71% for UNSW-NB 15 and 96.98% for CICIDS-2017. By using SHAP-based explain ability, the system provides worldwide and specific model interpretations that enable users to trust IDS prediction results. The experimental findings validate that the proposed methodology succeeds in simplifying the system while improving its classification functionality and delivering stronger interpretability properties to tackle weaknesses of current IDS technologies. The examination presents important findings for the development of secure network protection technologies, which operate with transparency.

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Purshottam J. Assudani mail -

N. V. S. Pavan Kumar mail -

K. Mohanambal mail -

R. Chitra mail

link https://doi.org/10.54216/JISIoT.170116

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Secure Real-Time Information Sharing in Artificial Intelligence Driven Freight Forwarding for Green Supply Chains

The integration of artificial intelligence (AI) and real-time information sharing is transforming the freight forwarding industry, enabling more sustainable and efficient green supply chains. However, the increasing reliance on interconnected systems raises significant cybersecurity challenges, particularly regarding secure data exchange and protection of sensitive information. This paper explores the critical role of cryptographic models and secure communication protocols in safeguarding real-time data sharing among AI-driven logistics networks. We analyze key security challenges faced by IoT-enabled freight systems and propose robust encryption and key distribution strategies to ensure confidentiality, integrity, and resilience. Our findings highlight the importance of secure information management in advancing sustainable, cyber-resilient supply chains that support environmental goals while maintaining operational efficiency.

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Apeksha Garg mail -

Sudha Vemaraju mail

link https://doi.org/10.54216/JCIM.160209

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Unconstrained Neutrosophic Nonlinear Programming Problems Gradient Projection Method

Nonlinear programming is one of the most important methods used to obtain the optimal solution to many real-world problems. Given the importance of this method, numerous studies and research have been conducted in recent years with the aim of providing methods that help find the optimal solution. These studies and research have resulted in a basic structure used to find these solutions. This structure initially indicates that the optimal solution can be found at any boundary point in the feasible region, at a point within the feasible region, or at a discontinuity point. In this research, we present some of the important foundations and principles of nonlinear programming and the gradient projection method used in searching for the optimal solution to unrestricted nonlinear programming problems. We will reformulate these foundations and principles using neutrosophic logic concepts as a complement to our previous research, the aim of which is to provide a new vision for some operations research methods, a neutrosophic vision. Our focus will be on the improvement these concepts offer when used in the field of applied mathematics, through the more accurate and comprehensive solutions we obtain, which provide a margin of freedom commensurate with the Given the reality we live in, and the changes that can occur to the data of the actual issue under study, this requires decision makers to prepare many appropriate alternatives for each change.

groups

Maissam Jdid mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.260320

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Developed acceptance sampling plans for the Shanker distribution based on truncated life tests

This paper introduces new acceptance sampling plans for situations where the life test is terminated at a predetermined time. The minimum sample sizes needed to guarantee a specified average lifetime are determined for different acceptance numbers, confidence levels, and ratios of the fixed test duration to the defined average lifetime. The Shanker distribution is adopted to represent the lifetimes of test units, with its mean serving as the quality indicator. Furthermore, the operating characteristic function values for the proposed sampling plans, along with the associated producer's risk, are provided. Examples are included to demonstrate how to use the tables effectively. An application of a real data set is used to illustrate the usefulness of the suggested acceptance sampling plans.

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Amer Ibrahim Al-Omari mail -

Rehab Alsultan mail

link https://doi.org/10.54216/IJNS.260321

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Crossing Cubic Structures Applied to Hoop Algebras

Recent years have witnessed remarkable developments in fuzzy logic, with interval-valued fuzziness and negative structures emerging as powerful tools for modeling inaccurate phenomena. The crossing cubic structures (CCs), as a generalization of the bipolar fuzziness structures, represent a comprehensive mathematical framework capable of dealing with a wide range of fuzziness and contradictory data, thus expanding research prospects in this area. This paper has made a new contribution to some algebraic structures by investigating the concept of CCs on algebraic substructures in a hoop algebra. The concepts of crossing cubic sub-hoops (CC − SHs) and crossing cubic filters (CCFs) are introduced, and a deeper understanding is sought to analyze their characteristics. The effect on the relationship between CC − SHs and CCFs is revealed, and the characterizations of CC − SHs and CCFs are analyzed.

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Anas Al-Masarwah mail -

Fawziah Alharthi mail -

Noor Bani Abd Al-Rahman mail

link https://doi.org/10.54216/IJNS.260322

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

PhyGital Fit: An AI-Driven Virtual Footwear Solution Integrating Generative AI, AR and Foot Morphology Analysis for Personalized Fit

Rapid development has been seen in Artificial Intel license (AI), which has transformed the retail industry, including online shopping. Selecting the right size of shoes that varies with brands and design is one of the biggest challenges in the E-Commerce footwear industry. This research focuses on AI Powered virtual shoe fitting system using Lens Studio Software. In this, customers are able to try shoes virtually through augmented reality and customized 3D foot models. This innovation solves size issues and benefits online footwear retailers, resulting in greater customer satisfaction. The role of Lens Studio software includes the creation of customized shoes, 3D shoes models, lenses, and size accuracy with the foot tracking mechanism.

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Abhimanyu Sangale mail -

Nikita Bhawar mail -

Rutuja Gholap mail -

Bhoomi Raut mail -

Kanchan Suryavanshi mail

link https://doi.org/10.54216/JCHCI.090201

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Climate Change and Sustainability: A Review

Climate change, driven by human activities like burning fossil fuels, deforestation, and industrial agriculture, is one of the most urgent global challenges. The rise in greenhouse gases (GHGs), such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), is contributing to global warming, sea level rise, and extreme weather events, with developing nations being particularly vulnerable. To address this, sustainability has become a key focus, involving the need to meet present demands without compromising the ability of future generations to meet theirs. Mitigation strategies include reducing emissions, transitioning to renewable energy sources like solar, wind, and hydropower, improving energy efficiency, and using reforestation to absorb carbon dioxide. Adaptation efforts, such as drought-resistant crops and resilient infrastructure, help communities cope with the impacts of climate change. The circular economy, which emphasizes resource efficiency, waste reduction, and recycling, further supports environmental sustainability. Governments, corporations, and individuals must also prioritize social justice, ensuring that underserved areas most affected by climate change receive the necessary support. Through collective action, we can work towards a sustainable future for all.

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Balaji Vijayan Venkateswarulu mail -

Ankitha K. mail -

Chandana L. mail -

Likitha M. mail

link https://doi.org/10.54216/JCHCI.090202

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new