Some Types of Nth-Locally Compactness Spaces

Rehab; Jamal; Salsabiela; Ala

doi:https://doi.org/10.54216/IJNS.250332

Journal of Intelligent Systems and Internet of Things | 2025

Intelligent Crop Disease Detection and Classification Using Deep Convolution Neural Network with Honey Badger Algorithm on Image Data

Cotton is the most significant cash crop in India. Each year cotton production is decreasing because of the attack of the disease. Plant diseases are usually produced by pathogens and pest insects and reduce the yield to a large scale if not controlled in time. The hour requires an effective plant disease diagnosis system that can assist the farmers in their farming and cultivation. Nevertheless, cotton production is harmfully affected by the presence of viruses, pests, bacterial pathogens, and so on. For the past decade or so, numerous image processing or deep learning (DL)--based automated plant leaf disease recognition techniques have been established but, unluckily, they infrequently focus on the cotton leaf diseases. Therefore, this article develops an Intelligent Detection and Classification of Cotton Leaf Diseases Using Transfer Learning and the Honey Badger Algorithm (IDCCLD-TLHBA) model with Satellite Images. The proposed IDCCLD-TLHBA technique intends to determine and classify various kinds of cotton leaf diseases using satellite imagery. In the IDCCLD-TLHBA technique, the wiener filtering (WF) model is used to reduce noise and enhance image quality for subsequent analysis. For feature extraction, the IDCCLD-TLHBA technique applies the MobileNetV2 model to capture relevant features from the satellite images while maintaining computational efficiency. In addition, the stacked long short-term memory (SLSTM) method is employed for the classification and recognition of cotton leaf diseases. Eventually, the honey badger algorithm (HBA) is used to optimally select the parameters involved in the SLSTM model to ensure a better configuration of the network to enhance results. The performance validation of the IDCCLD-TLHBA method is carried out against the benchmark dataset and the stimulated results highlight the better results of the IDCCLD-TLHBA model across the existing techniques.

groups

Daniel Arockiam mail -

Azween Abdullah mail -

Valliappan Raju mail

link https://doi.org/10.54216/JISIoT.140215

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Automated Agricultural Crop Type Mapping Using Fusion of Transfer Learning and Tasmanian devil Optimization Algorithm on Remote Sensing Imagery

At present, the application of remote sensing (RS) data achieved from satellite imagery or unmanned aerial vehicles (UAV) has become common for crop classification procedures, i.e. crop mapping, soil classification, or prediction of yield. The classification of food crop utilizing RS images (RSI) is one of the major applications of RS in farming. It contains the usage of aerial or satellite images for classifying and identifying dissimilar kinds of food crops developed in an exact region. This data is beneficial for estimation of yield, crop monitoring, and land management. Meeting the conditions for examining these data needs more refined techniques and artificial intelligence (AI) technologies, which deliver essential support. Recently, the usage of deep learning (DL) for crop type classification with RS images could help sustainable farming practices by providing appropriate and precise data on the kinds and features of crops. In this study, we offer an Automated Agricultural Crop Type Mapping Utilizing Fusion of Transfer Learning and Tasmanian Devil Optimization (AACTM-FTLTDO) algorithm on Remote Sensing Imagery. The primary goal of the AACTM-FTLTDO methodology is to accurately detect and classify crop types for more precise agricultural monitoring using remote sensing technologies. To accomplish that, the AACTM-FTLTDO model employs a fusion of transfer learning techniques involving three models such as SqueezeNet, CapsNet, and ShuffleNetV2 to capture diverse, multi-scale spatial and spectral features. For the crop type classification and detection process, the auto-encoder (AE) classifier can be employed. Eventually, the tasmanian devil optimization (TDO) technique was deployed to modify the hyperparameter of the AE technique for ensuring optimal model configurations and reducing computational complexity. A wide range of experimentation studies is made and the results are examined under numerous measures. The comparative study shows that the AACTM-FTLTDO technique performs better than existing approaches

groups

Daniel Arockiam mail -

Azween Abdullah mail -

Valliappan Raju mail

link https://doi.org/10.54216/JCIM.150206

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

A Comprehensive Data Fusion Analysis for Virtual Tourism Systems

Recently, it has been observed that the tourism industry is undergoing a fundamental change due to the rapid development of virtual tour technologies, especially artificial intelligence. This paper therefore aims to provide an overview of this new development from the early 2000s to the current environment in global tourism. We present, in a historical context, the main developments and applications of virtual tours and AI through a systematic review of literature, industry reports and empirical data from different sectors of the tourism industry. Our findings suggest that the adoption of the technologies under review, enhanced by data fusion, has significantly reshaped the way tourism experiences are conceptualized, delivered, and consumed. Data fusion combines information from multiple sources, enabling richer insights and a more comprehensive understanding of traveller behaviours and preferences. While virtual tours have emerged as a powerful tool for destination marketing, cultural preservation, and accessibility, AI, combined with data fusion, has also transformed the landscape by enabling more personalized travel planning, responsive customer service, and data-driven decision-making. This integration allows tourism providers to create seamless and engaging experiences tailored to individual needs, making tourism more accessible and efficient. In each case, these innovations have raised important questions about authenticity, sustainability, and the future of traditional tourism business models. We will present a critical comparison of virtual and physical tourism experiences in different regions and market segments, providing insights into the interplay of technological innovation, economic imperatives, and socio-cultural dynamics in the digital age. We conclude by reflecting on the implications for post-pandemic recovery, responsible tourism and global cultural exchange through virtual tours and AI. The findings of the study add to the growing body of knowledge on the digitalization of tourism and provide useful insights for practitioners, policy makers and researchers interested in the rapidly changing landscape of this industry.

groups

Muhammad Eid Balbaa mail -

Olim Astanakulov mail -

Oybek Khayitov mail -

Kurbon Rakhmanov mail -

Sanjar Mirzaliev mail

link https://doi.org/10.54216/FPA.170223

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Computation of Weighted PI Index of Lexicographic product graphs and for Silicates Networks

The study of chemical compounds’ molecular structures is one of the most cutting-edge uses of graph theory, along with computer science, nanochemistry, network design in electrical and electronic engineering, and the depiction of graphs in Google Maps. The degree and distance between vertices of a graph are the basis for examining topological indices. The formula for computing the Weighted Padmakar Ivan index (WPI) of a graph G is PIw(G) = P e∈E(G) [(dG(u) + dG(v)][|V (G)| − NG(e)].

groups

Hemalatha Rangasamy mail -

Kanagasabapathi Somasundaram mail -

Sandhiya Pechimuthu mail

link https://doi.org/10.54216/IJNS.250328

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Estimation of Population Mean using Neutrosophic Exponential Estimators with Application to Real Data

One of the traditional problems in survey sampling is to estimate the population parameter like mean variance etc. This article investigates the mathematical derivations and application of neutrosophic statistics to address the challenges posed by imprecise, indeterminacies or ambiguous data, such as daily stock prices, weather forecast, social media sentiment and temperatures. The suggested estimators are highly useful for computing results while working with unclear, hazy, and neutrosophic-type data. These estimators produce answers that are interval-form rather than single-valued, which may give our population parameter a better chance of being off. We propose three novel neutrosophic exponential ratio-type estimators for the population mean, utilizing information of neutrosophic auxiliary variables. Expressions for bias and mean square error (MSE) of these estimators are derived using first-order approximations to assess their performance in terms of accuracy. To demonstrate their effectiveness, we apply the proposed estimators to real-life neutrosophic data sets. Additionally, a simulation study shows that our estimators outperform existing methods in terms of MSEs and percentage relative efficiency (PREs). This study further expands its originality by including pre-existing estimators into the neutrosophic framework, showcasing its versatility and adaptability. The results suggest that neutrosophic statistics provide a robust framework for analyzing uncertain data, facilitating more reliable decision-making in various applications.

groups

Anjali Singh mail -

Poonam Singh mail -

Prayas Sharma mail -

Badr Aloraini mail

link https://doi.org/10.54216/IJNS.250329

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Lindel ”Ofness Spaces in NTH Topological Spaces

In this study, the lindel”of property of spaces will be examined across nth topologies, referred to as nthlindel” of spaces. Furthermore, the characteristics of these spaces will be analyzed in relation to lindel¨o]f spaces and tri-Lindelf spaces. Several theoretical results have been presented and proven, and various well-known theorems concerning Lindel?f spaces have been extended to accommodate nth topologies. An illustrative examples are provided to support the findings.

groups

Jamal Oudetallah mail -

Rehab Alharbi mail -

Salsabiela Rawashdeh mail -

Ala Amourah mail

link https://doi.org/10.54216/IJNS.250330

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

Fusion: Practice and Applications | 2025

The effectiveness of using Box-to-Box technology to develop some of the composite physical and technical capabilities of footballers

This study aimed to measure the impact of using "Box-to-Box" technology in improving physical and technical abilities for football players under 19 years old at Najma Sinai Sports Club, North Sinia the research highlights the global appeal of football but also offers insight into how advancements in training can help to improve player performance, some teams tend to cling old-school tactics which undermine progress. The study evaluated a 12-week "Box-to-Box" training program using an experimental design with pre and post intervention measurements for 23 players. The results showed that while agility, endurance, speed, and muscle strength test scores significantly improved; passing accuracy and dribbling efficiency were also enhanced during composite skill performance. These findings reaffirm that "Box-to-Box" Training is the way to go for developing key competencies and improving performance, in general. The study suggests including this new technology in traditional training routines, asserting that it has now become essential for player assessment and improvement. It also proposes a wider perspective on the long-term use of "Box-to-Box" technology in different populations and sports, as well as new functional training for specific football positions.

groups

Amr Mohamed El Koshiry mail -

Entesar Eliwa mail -

Ahmed Abd Allah Tony mail -

Ahmad Shalgham mail

link https://doi.org/10.54216/FPA.170224

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Improving the Prediction of Evaporation Variable in Mosul Dam Using ARIMA Model and Time Series Analysis

Evaporation plays a significant role in managing water resources and is an important indicator in risk and crisis management, particularly in operating reservoirs and dams. Precise predictions of evaporation rates are crucial to effective water resource management, and various modelling methods, including AI and autoregression, have been employed to create accurate models. This makes it more important to use innovative technology to continuously monitor this phenomenon with accurate scientific results, allowing decision-makers to be aware of and prepare for potential drought risks and crises. In this study, therefore, we propose the establishment of a mechanism that would include analyzing and exploring the data used in this study (Evaporation) and cleaning up the impurities of actual and lost values to obtain accurate data that would serve as actual inputs to ARIMA model that will adopt in this study, This mechanism would contribute to the performance and efficiency of this model using time series data to accurately predict future trends of evaporation plants in the water of the Mosul dam. Our objective is to explain the diversity of climate policies and actions using a data-based approach to analyzing integrated parameters over the years, etc. This is complemented in depth by how different methods of extracting data behaviour are used to study model forecasts. This collaborative study aims to enhance future studies by using more comprehensive datasets with more learning models. The researchers believe in the power of sharing knowledge and are thus committed to sharing the results of other causes outside of global warming that contribute to climate change.

groups

Khalid MK Khafaji mail -

Bassem Ben Hamed mail

link https://doi.org/10.54216/FPA.170225

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Cubic Spherical Linguistic Neutrosophic Topological Space

In this article, we introduce and establish a novel concept called ’cubic spherical linguistic neutrosophic topological spaces’ by employing cubic spherical linguistic neutrosophic sets and topological frameworks. Various foundational definitions, theorems, and properties are provided along with illustrative examples.

groups

S. Sathyapriya mail -

V. Jeyanthi mail -

Said Broumi mail

link https://doi.org/10.54216/IJNS.250331

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025