Analyzing the local Lindelöf proper function and the local proper function of deep learning in bitopological spaces

Ali A.; Hamza; Eman; Anas

doi:https://doi.org/10.54216/IJNS.260223

Journal of Intelligent Systems and Internet of Things | 2025

Improving the Reliability of Wireless Sensor Network Assisted IoT Network with a Cluster-Based Chain-Tree Routing Protocol

The primary objective of designing routing protocols for Wireless Sensor Networks (WSNs) is to extend the network lifetime by optimizing the use of the limited battery energy of the sensor nodes. To improve conservation of energy and longevity of the network in WSNs, this study proposes a Cluster-based Chain-Tree Routing Protocol (CCTRP). Integrating tree based chain and cluster routing methods in WSNs is the primary objective of this study. This new CCTRP adopts a sector-based vertical network-partitioning scheme that divides network into sectors and it again vertically partitions the nodes too form various size of clusters. Then, Minimum Spanning Tree (MST) is created based on the kruskal’s Algorithm through a Chain Leader (CL) node serving as the receiver and chain is formed from CLs of last level cluster to Base Station (BS) in each sector. Using the BS, remaining energy and distance to the next CL node, CCTRP determines the Cluster Leader (CL) or Chain CL node in each cluster. For data transport, it also selects the shortest paths. When the energy that remains in the node is ready to be exhausted, the transition is executed according to this protocol. This results in a significant improvement of the average network lifespan. Finally, the CCTRP protocol outperforms the current protocols in terms of network performance, according to the simulation results.

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R. Lalitha mail -

A. V. Senthil Kumar mail

link https://doi.org/10.54216/JISIoT.160220

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Adversarially Robust 1D-CNN for Malicious Traffic Detection in Network Security Applications

While threats in cyberspace are in a state of constant evolution, the use of AI in cyber defense has numerous opportunities and dangers. This paper evaluates adversarial robustness for deep learning networks in network security applications by introducing a novel one-dimensional CNN model for malicious traffic detection. We conducted rigorous end-to-end processing and analysis of network traffic data, using a balanced dataset of 200,000 connections (46.52% benign, 53.48% malicious). Our model architecture includes three convolutional blocks (32, 64, and 128 filters, respectively) with batch normalization and dropout mechanisms (0.3 and 0.2, respectively). We use standardized feature scaling, label encoding for categorical features, and stratified sampling to maintain class distribution integrity.  Our proposed approach achieved remarkable performance metrics compared to standard approaches with a 95% AUC-ROC result (15% better than baseline CNN models) and detection rate of 99.99% malicious traffic (compared to 98.5% with standard architectures). The model demonstrates better robustness with only 10 false negatives out of 107,895 malicious samples, a 67% enhancement compared to current state-of-the-art systems. Training dynamics show great stability with minimal overfitting (validation/training loss difference of only 0.01), indicating good generalization ability.

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Baraa Mohammed Hassn mail -

Esraa Saleh Alomari mail -

Jaafar Sadiq Alrubaye mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/JCIM.160113

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Modify Block Chain Environment based on Post-quantum Algorithms

Blockchain technology provides reliable data storage and secures transactions, however, is not suitable for devices with low resources because of its high computational and resource requirements. As quantum computing develops, it poses concerns regarding a cryptographic integrity of blockchain, making them more vulnerable to attacks. Blockchain technology is being used to enhance security and performance. The application of the post-quantum Ascon algorithm in a blockchain setting is presented in this paper. The Ascon hashing algorithm offers a lightweight, efficient architecture for resource-constrained applications, including mobile devices or Internet of Things-based blockchains. By providing high-speed hashing, authentication features, and defense against quantum attacks, it enhances performance and guarantees strong security without putting a strain on network infrastructure. The experimental results show using the Ascon algorithm in a blockchain environment is successful in reducing resource usage and execution time and significantly increasing randomness and unpredictability. Post-quantum Ascon algorithms overcome the drawbacks of traditional technologies and ensure that blockchain systems continue to withstand the new risks posed by quantum computing while increasing overall efficiency

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Rasha Hani Salman mail -

Hala Bahjat Abdul Wahab mail

link https://doi.org/10.54216/JCIM.160112

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Computer Vision of Smile Detection Based on Machine and Deep Learning Approach

Smile detection and recognition have been a key component of sentiment analysis, social robotics, human-computer interaction, and mental health monitoring before the advent of deep learning. Understanding and accurately identifying smiles can provide deep insights into human behavior, strengthen communication systems, and enhance adaptive responses in AI interfaces. This paper is a comprehensive review of algorithms developed for smile detection and recognition, and categorizes their main approaches into three traditional computer vision techniques: feature-based, machine learning-based, and deep learning-based. These techniques rely on handcrafted features such as edges, geometric features of the face, and texture, which give interpretability and limited adaptability. This paper explores feature extraction methods such as geometric and histogram-based features (e.g., histograms of directed gradients). In addition, this paper evaluates the effectiveness of traditional classifiers, including support vector machines that use machine learning-based methods, leveraging algorithms such as support vector machines (SVMs), extracted features to classify smiles with improved accuracy. Deep learning techniques, especially convolutional neural networks (CNNs) and hybrid methods provide end-to-end learning capabilities, extracting features directly from raw pixel data and enabling real-time performance. These frameworks, including recurrent neural networks (RNNs) for temporal analysis, generative adversarial networks (GANs) for data augmentation, and graph neural networks (GNNs) for structural analysis, have also pushed the boundaries of smile detection in dynamic and challenging environments. It also aims to provide a comprehensive overview of these classical methods, and analyze their strengths, limitations, drawbacks, and performance across diverse datasets of the proposed databases by focusing on describing these datasets and researchers’ methods of working on them as benchmarks for their research, and highlighting their importance in the environments and their contributions to the development of smile detection algorithms in the field of computer vision. Among these datasets are datasets such as CK+, FER2013, AffectNet, and Jaffe in developing, training, and evaluating smile detection and recognition algorithm models. By comparing these methodologies, our paper recommends directing future research towards more efficient, robust, and scalable solutions for smile detection and recognition in diverse applications.

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Huda Lafta Majeed mail -

Oday Ali Hassen mail -

Dhyeauldeen A. Farhan mail -

Yu Yu Gromov mail -

Kavita Sheoran mail -

Geetika Dhand mail

link https://doi.org/10.54216/JCIM.160115

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A New Automated System Approach to Detect Digital Forensics using Natural Language Processing to Recommend Jobs and Courses

A resume is the first impression between you and a potential employer. Therefore, the importance of a resume can never be underestimated. Selecting the right candidates for a job within a company can be a daunting task for recruiters when they have to review hundreds of resumes. To reduce time and effort, we can use NLTK and Natural Language Processing (NLP) techniques to extract essential data from a resume. NLTK is a free, open source, community-driven project and the leading platform for building Python programs to work with human language data. To select the best resume according to the company’s requirements, an algorithm such as KNN is used. To be selected from hundreds of resumes, your resume must be one of the best. Therefore, our work also focuses on creating an automated system that can recommend the right skills and courses to help the desired candidates by using Natural Language Processing to analyze writing style (linguistic fingerprints) and also used to measure style and analyze word frequency from the submitted resume. Through semantic search and relying on individual resumes, forensic experts can query the huge semantic datasets provided to companies and institutions and facilitate the work of government forensics by obtaining official institutional databases. With global cybercrime and the increase in applicants seeking work and leveraging their multilingual data, Natural Language Processing (NLP) is making it easier. Through the important relationship between Natural Language Processing (NLP) and digital forensics, NLP techniques are increasingly being used to enhance investigations involving digital evidence and leverage the support of NLP for open-source data by analyzing massive amounts of public data.

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Shahlaa Mashhadani mail -

Rajaa Mrayeh Mohammed mail -

Nishtha Jatana mail -

Charu Gupta mail -

Oday Ali Hassen mail -

Shweta Jindal mail

link https://doi.org/10.54216/JCIM.160116

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Early DDoS Attack Detection Using Lightweight Deep Neural Network

In the digital age, e-commerce platforms are critical components of the global economy, facilitating seamless transactions and interactions between businesses and consumers. The digital infrastructure of these institutions is frequently attacked, either to hack or disrupt online services, leading to significant financial losses and damage to reputation. The most famous of these attacks are DDoS attacks, which lead to an increase in the volume of traffic to the platform's website beyond the capacity of the servers, thus causing the platform to respond slowly and crash and customers to be unable to access it. The increase in these attacks causes significant material damage to institutions, whether in the loss of revenues or the cost of responding to attacks. This work presents a robust DDoS attacks early detection model that can be adopted on e-commerce platforms using a lightweight one-dimension Convolutional neural network. The proposed model leverages the efficiency of deep learning with the lightweight architecture to analyze network traffic in real time, identifying patterns indicative of an impending DDoS attack. The balance between high detection accuracy with computational efficiency makes it suitable for real-time implementation in diverse e-commerce environments. DNN is trained on a comprehensive dataset of network traffic, encompassing both normal and attack scenarios, to ensure it can distinguish between legitimate traffic spikes and malicious activity. DDoS Evaluation Dataset CIC-DDoS2019 and CICIDS2017 are used in the experimental and accuracy achieved 0.98 and 0.99 in these two datasets respectively.

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Ahmed F. Almukhtar mail -

Noor D. AL-Shakarchy mail -

Mais Saad Safoq mail

link https://doi.org/10.54216/FPA.190228

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Smart Accıdent Detectıon using IoT Technology

Road accidents and emergency services delay are the main significant issues. To overcome these issues need to develop a system. Efficient handling of accidents through the immediate detection and provide timely aid are more crucial. Accident detection and emergency system depends on IoT (Internet of things) with minimum delay are gaining significant attention towards industry and academic literature. Several researches are investigated using IOT technology to detect accidents. In this work, we proposed an effective accident detection method by employing five sensors not only to detect accident but also to report type of accident such as collision, no accident, roll over or fall off. In addition to that, the status of the accident is communicated to the IBM Watson Cloud platform. The incoming data received in the node red platform is integrated with the Google Maps to show location and other information about the accident that can be accessed by the hospital through website and sending alert messages to victim acquaintances. In addition, two Machine Learning (ML) models based on K-Nearest Neighbor (KNN) model and the Naïve Bayes (NB) model are compared to find out the best accident detection model. It is noticed that the KNN model is the very effective ML model, which employed to know the accident status and to enhance the system by providing patient’s details, a kill switch and sending messages often until acknowledgement is received.

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Sindhuja M. mail -

Vijay Murugan S. mail -

Elarmathi S. mail

link https://doi.org/10.54216/JCHCI.090104

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Joint PAPR and Spectrum Sensıng in CRNS: A VLSI-Based Approach for Secondary User Integration

In Cognitive Radio Networks (CRNs), Peak-to-Average-Power-Ratio (PAPR) reduction is crucial for mitigating distortion in signals while optimizing spectral efficiency. This work offers a novel strategy for effectively reducing that PAPR in CRN systems, especially when secondary users are incorporated, by utilizing VLSI (Very-Large-Scale Integration) design approaches. The proposed strategy investigates VLSI methods for PAPR reduction, such as Partial-Transmit-Sequence (PTS) techniques. The system is appropriate for CRN applications because it can accomplish real-time PAPR reduction while preserving low power consumption and compact size by implementing these approaches in VLSI hardware. This could entail particular strategies for controlling PAPR with secondary users, such as joint PAPR and spectrum sensing approaches, dynamic power allocation, or user scheduling algorithms. Utilizing the predetermined values of pilot tones, the suggested decoder investigates every possible combination of weighting variables to determine which combination the transmitter has chosen and employed. There appears to be no data rate loss with the proposed decoder since it does not require any more pilot tones. This study next gives a digital execution of the described PTS decoder and illustrates its low power qualities, as well as the design and the encoder required at the transmitter to operate the suggested system is being developed using VLSI. The suggested architecture makes it easier for SUs to integrate with CRNs seamlessly. It allows SUs to effectively take advantage of available spectrum opportunities while complying with CRN restrictions and reducing interference with primary users by tackling PAPR and spectrum sensing concurrently. Furthermore, the study discusses the difficulties of incorporating secondary users into CRNs while retaining PAPR management.

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P. Shanmuga Sundaram mail -

M. Vasanthi mail -

P. Sangeetha mail

link https://doi.org/10.54216/JCHCI.090105

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhanced Malware Classification: A Hybrid Model Utilizing Denoising Autoencoder and CNN based on visualization method

In the last few years, technology has developed so rapidly that many malware applications are available in the software market. Cybercrimes are increasing day by day with the usage of malware applications. Traditional approaches are not as effective in detecting malware. This study introduces a novel method for distinguishing malware from benign software applications using deep learning models like Denoising Autoencoder and Convolutional Neural Network. Initially, we extract binary code from the applications and transform it into grayscale images. Then, utilizing a denoising autoencoder, we improve the quality of the grayscale images by eliminating noise, and the Convolutional Neural Network uses processed images as input. Finally, the Convolutional Neural Network is employed to differentiate between malicious and benign applications. We test this methodology on the dataset that contains 10,810 malware and 1082 benign files. The suggested model obtains an accuracy of 97% and an F1-score of 96% and performs better than some traditional methods.

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Thippireddy Harika mail -

Gera Pradeepini mail

link https://doi.org/10.54216/JCIM.160117

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Analyzing the local Lindelöf proper function and the local proper function of deep learning in bitopological spaces

It is essential to create new mathematical strategies to deal with everyday problems since they require a lot of data and ambiguity. The best tool for doing this is proper functions, which are the most common mathematical technique. In order to generate suitable functions, we investigate several set operators. A connection between symmetry and certain types of proper functions and their classical topologies can be made. As a result of this symmetry, we can examine the traits and behaviors of traditional topological notions through settings, and vice versa. We describe a new class of proper functions in this paper and launch a preliminary investigation into them. These functions are referred to as pairwise local proper functions and pairwise local Lindel¨of proper functions in bitopological spaces. In general topology, we also establish the connection between this new class of proper functions and other classes of generalized functions already in existence. Regarding the new ideas, a number of relationships, necessary and sufficient conditions, examples and counter-examples are provided. In addition, a different argument for the pairwise regularity of a pairwise Hausdorff and pairwise locally compact bitopological space is presented. As part of this research, we also look at the images and inverse images of specific bitopological features under these functions. A few product theorems pertaining to these concepts were finally discovered.

groups

Ali A. Atoom mail -

Hamza Qoqazeh mail -

Eman Hussein mail -

Anas Owledat mail

link https://doi.org/10.54216/IJNS.260223

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

Details open_in_new