Modeling Sustainable Design Practices for Service Complexes - Systematic Review

Batoul; Alaa J.

doi:https://doi.org/10.54216/IJBES.080202

Journal of Cybersecurity and Information Management | 2024

Dense-BiGRU: Densely Connected Bi-directional Gated Recurrent Unit based Heart Failure Detection using ECG Signal

Heart failure, a state marked by the heart's inefficiency in pumping blood adequately., can lead to serious health complications and reduced quality of life. Detecting heart failure early is crucial as it allows for timely intervention and management strategies to prevent progression and improve patient outcomes. The effectiveness of integrating ECG and AI for heart failure detection stems from AI's capacity to meticulously analyze extensive ECG datasets, facilitating the early identification of nuanced cardiac irregularities and enhancing diagnostic precision. While the current research lacks sufficient accuracy and is burdened by complexity issues. To overcome this issue, we proposed a novel Densely Connected Bi-directional Gated Recurrent Unit (Dense-BiGRU) model for accurate heart failure detection. In this work, we enhanced collected ECG signal in terms of performing multiple data pre-treatment including as denoising, powerline interference and normalization utilizing Collaborative Empirical Mode Decomposition (CEMD) algorithm, Adaptive Least Mean Square (Adaptive LMS) and min-max normalization method, respectively. Here, we utilized the LiteStream_Net layer for extracting appropriate feature from pre-processed signal. Finally, based on extracted features heart failure detection is implemented through introducing Dense-BiGRU algorithm. The proposed research is implemented using MATLAB simulation tools, and its validation is conducted through various simulation metrics including accuracy, recall, precision, F1-score, and AUC. The results of the implementation demonstrate that the proposed research surpasses existing state-of-the-art methodologies.

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Vinitha V. mail -

V. Parthasarathy mail -

R. Santhosh mail

link https://doi.org/10.54216/JCIM.140204

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

LSTM-NAS-Net: Enhanced Brain Tumor Segmentation in MRI and CT Images using LSTM-Autoencoder-based Neural Architecture Search

Brain Tumour (BT) a mass or a lump or a growth which occurs due to abnormal cell division or unusual growth of cells in the brain tissue. Initially, the two major types of BT are Primary BT and Secondary BT, the tumour that originate from brain is known as Primary BT and it may be cancerous or non-cancerous. The tumour the initiates from other part of the body and spreads to the brain is stated as secondary BT.  Diagnosing BT generally involves a multiple investigation method, such as MRI, CT, PET, SPECT as well as the neurological examinations and blood investigations, whereas some of the patients may need biopsies to evaluate the tumour size and stage. Here we use MRI and CT images for BT segmentation whereas these modalities play a major role in diagnosing, treating, planning and monitoring the BT patients. Moreover, the multimodal data can provide a quantitative information’s about the tumour size, shape, volume and texture. While segmenting the BT the lack of segmentation methods and the interpretability of the segmented regions are limited. To overcome this, we propose a novel LSTM autoencoder bas NAS method which is used for the extracting the BT features and these features can be fused using Contextual Integration Module (CIM) and segmented using the Segmentation Guided Regulizer (SGR) which helps to overcome the stated issues. Finally, the performance metrices are calculated by comparing with the state-of -the -art methods and our method achieves a best segmenting metrices.

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Santhosh Kumar mail -

S. P. Sasirekha mail -

R. Santhosh mail

link https://doi.org/10.54216/JCIM.140205

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Secondary Partial Ordering of Neutrosophic Fuzzy Matrices

In this article, we define secondary generalized inverse of a neutrosophic fuzzy matrices whenever exists. . Also, the S-ordering for the set of neutrosophic fuzzy matrices are defined and characterized. A necessary and sufficient condition for the existence of secondary generalized inverse of neutrosophic fuzzy matrices with the help of S-ordering is obtained.

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Divya Shenoy Purushothama mail

link https://doi.org/10.54216/IJNS.240431

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Modelling a Request and Response-Based Cryptographic Model For Executing Data Deduplication in the Cloud

Cloud storage is one of the most crucial components of cloud computing because it makes it simpler for users to share and manage their data on the cloud with authorized users. Secure deduplication has attracted much attention in cloud storage because it may remove redundancy from encrypted data to save storage space and communication overhead. Many current safe deduplication systems usually focus on accomplishing the following characteristics regarding security and privacy: Access control, tag consistency, data privacy and defence against various attacks. But as far as we know, none can simultaneously fulfil all four conditions. In this research, we offer a safe deduplication method that is effective and provides user-defined access control to address this flaw. Because it only allows the cloud service provider to grant data access on behalf of data owners, our proposed solution (Request-response-based Elliptic Curve Cryptography) may effectively delete duplicates without compromising the security and privacy of cloud users. A thorough security investigation reveals that our approved safe deduplication solution successfully thwarts brute-force attacks while dependably maintaining tag consistency and data confidentiality. Comprehensive simulations show that our solution surpasses the evaluation in computing, communication, storage overheads, and deduplication efficiency.

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Doddi Suresh Kumar mail -

Nulaka Srinivasu mail

link https://doi.org/10.54216/JCIM.140206

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Panoptic Segmentation with Multi-Modal Dataset Using an Improved Network Model

For biomedical image analysis, instance segmentation is crucial. It is still difficult because of the intricate backdrop elements, the significant variation in object appearances, the large number of overlapping items, and the hazy object borders. Deep learning-based techniques, which may be separated into proposal-free and proposal-based approaches, have been frequently employed recently to overcome these challenges. The existing approaches experience information loss due to their concentration on either local-level instance features or global-level semantics. To solve this problem, this work proposes an improved dense Net ( ) that mixes instance and semantic data. The suggested  promotes the acquisition of semantic contextual information by the instance branch by linking instance prediction and semantic features via a residual attention feature integration strategy. The confidence score of each item is then matched with the accuracy of the prediction using a dense quality sub-branch that is created. A consistency regularisation technique is also proposed for the robust learning of segmentation for instance branches and the semantic segments tasks. By proving its utility, the proposed  outperforms prevailing approaches on various biomedical datasets.

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Koppagiri Jyothsna Devi mail -

Gouranga Mandal mail

link https://doi.org/10.54216/JCIM.140207

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Drought Prediction with Feature Enhanced LSTM Model using Metaheuristic Optimization Algorithms

The impact of drought builds on all three fronts of economy, environment, and society is devastating. Predicting its arrival and duration is highly important to arrange any sort of mitigation plans. The association of detailed relationship between multiple variables makes drought prediction a highly complex task. Especially influence of global warming, polar sea extent variations and their influence on overall ocean temperature have altered the seasonal rainfall behaviors all over the world. In the midst of it, predictions centered on the history of rainfall levels become inaccurate. The proposed system is an optimized deep learning prediction model integrating indigenous knowledge (IK) is proposed to predict the drought. IK expressed in human language is translated using fuzzy function and fed to an improved Long Short Term Memory (LSTM) model. The LSTM model hyperparameters are optimized using a hybrid of Particle Swarm Optimization (PSO) with firefly to produce the meta-heuristics algorithm which will provide the best performance in presence of integration of IK features into modern meteorological features which solves the problem of local minima in LSTM hyperparameter optimization. The performance of the proposed results were tested compared with the meteorological information gathered by the Karnataka Natural Disaster Monitoring Centre (KNDMC) for the district named Chitradurga of the Karnataka state in India. The proposed system which is  Indigenous Knowledge merged along the cross model attention network can produce at least 1.4% higher Nash–Sutcliffe model efficiency coefficient (NSE) and 30% lower Mean Absolute Error (MAE) in the prediction of Standard Precipitation Index (SPI) compared to Convolution Neural Networks (CNN) and LSTM based time series prediction models.

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Leelavathy S. R. mail -

A. Mary Mekala mail

link https://doi.org/10.54216/JCIM.140208

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

CL-FusionBEV: A Cross-Attention Based Fusion Model for Camera and LiDAR in Bird’s Eye View Perception

In autonomous navigation, the ability to detect 3D objects from a Bird’s-Eye View (BEV) perspective is essential. Nevertheless, many obstacles remain before LiDAR and camera data can be effectively combined. We propose CL-FusionBEV, a novel framework for sensor fusion that enhances Three-dimensional object recognition in the BEV domain. This method structures LiDAR point clouds for improved spatial feature extraction while converting camera data into BEV format via an implicit learning technique. An implicit fusion network and a multi-modal cross-attention mechanism facilitate seamless sensor interaction, ensuring comprehensive feature integration. Additionally, a self-attention mechanism of BEV enhances broad-scale reasoning and data extraction, improving the detection of occluded and distant objects. By efficiently synchronising data from several sensors, the suggested method improves feature uniformity and resolves spatial inconsistencies. It further leverages adaptive feature selection to enhance robustness against sensor noise and varying conditions. We evaluate CL-FusionBEV on the nuScenes dataset, achieving achieved a 73.3% mAP and a 75.5% NDS on the nuScenes benchmark, with vehicle and pedestrian detection accuracies of 89% and 90.7%, respectively. Our model demonstrates superior robustness in challenging conditions such as low visibility and dense urban environments. CL-FusionBEV maintains high efficiency with real-time inference, making it suitable for deployment in autonomous systems. Extensive experiments show our strategy routinely beats cutting-edge techniques, especially in detecting small and distant objects. By addressing key sensor fusion challenges in the BEV domain, CL-FusionBEV offers a notable advancement in Three-dimensional object recognition, ensuring high accuracy, efficiency, and reliability for real-world driving scenarios.

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S. P. Samyuktha mail -

S. Renuka mail -

R. Shakthi Priyaa mail -

Angel Meriba D. S. mail -

Maheshwari M. mail -

Megavarshini M. mail -

S. Malathi mail

link https://doi.org/10.54216/FPA.190202

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Integrating Novel Mechanisms for Threat Detection in Enhanced Data Classification using Ant Colony Optimization with Recurrent Neural Network

In new technologies like fog computing, edge computing, cloud computing, and the Internet of Things (IoT), cybersecurity concerns and cyber-attacks have surged. The demand for better threat detection and prevention systems has increased due to the present global uptick in phishing and computer network attacks. In order to identify irregularities and attacks on the network, which have increased in scale and prevalence, threat identification is essential. However, the community is forced to investigate and create novel threat detection approaches that are capable of detecting threats using anomalies due to the increase in network threats, the growth of new methods of attack and computations, and the requirement to ensure security measures. A novel mechanism is employed to identify threats in a data based on optimized deep learning. The main aim of this paper is the usage of data classification system based on Deep Learning (DL). The proposed mechanism employed the TCP (Transmission Control Protocol) communication protocol to extract data from loud IoT (Internet of Things) networks for the purpose of threat detection. To perform feature extraction an Ant Colony Optimization (ACO) is utilised, through Recurrent Neural Network (RNN), the attacks in data are classified and detected. Additionally, the suggested approach has been evaluated and trained using the BOUN DDoS contemporary dataset, which comprises a variety of attack types and allows for the effectiveness of the framework to be determined to compare it to previous approaches. The Findings indicate that the suggested approach achieved higher accuracy in DDoS attack identification in comparison with Traditional deep learning methods. The existing method detects the generic attack with lower efficiency however; the proposed mechanism achieves better accuracy in both the detection of the DDoS attack and the detection of regular traffic.  

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Vivek alias M. Chidambaram mail -

Karthik Painganadu Chandrasekaran mail

link https://doi.org/10.54216/JCIM.140209

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Fuzzy Sampling Strategy Based on IPD

If a production process modification is implemented with the intention of enhancing product quality, IPD models a suitable probability distribution for the number of sample defects. If a manufacturing process intervention is done with the purpose of increasing product quality, IPD models a suitable probability distribution for the sample's total number of defects. When the production process with the interference parameter is considered, fuzzy sampling plans based on IPD are found to be more effective than the current strategy. The Intervened Poisson distribution is used to develop single sampling strategy for such lots when there is ambiguity regarding the percentage of defective items. With fuzzy probability, the plan's operating characteristic curve is obtained. The mean of outgoing quality is derived using fuzzy parameters.  

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V. Jemmy Joyce mail -

K. Rebbeca Jebaseeli Edna mail -

Evanzlin P. mail -

Bazil Wilfred C. mail

link https://doi.org/10.54216/JCIM.140210

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2024

Modeling Sustainable Design Practices for Service Complexes - Systematic Review

In the realm of sustainable urban design, this peer-reviewed article presents a systematic review focusing on contemporary practices in mixed-use development. Emphasizing a holistic approach, the study examines renewable energy utilization, energy efficiency methodologies, sustainable materials, water conservation strategies, green space integration for ecological enhancement, and social interaction through design, waste management, and smart transportation systems. Methodologically, a framework incorporating bibliometric and content analyses was employed, reviewing scholarly literature from 2010 to 2023 using key search terms like "mixed use," "sustainable design," and "renewable energy." The findings highlight global trends including widespread adoption of renewable energy technologies, innovations in energy-efficient building methods, advances in sustainable materials, effective water resource management strategies, and the role of green spaces in urban biodiversity. The study underscores the importance of promoting social cohesion, enhancing waste management, and integrating intelligent transportation networks for sustainable urban living. It concludes on the imperative of integrated approaches, policy frameworks, technological innovations, and community engagement to achieve comprehensive sustainability in urban environments. This research contributes significantly to the discourse on sustainable urban development by addressing urban challenges and offering adaptable frameworks and practical solutions. It serves as a valuable resource for scholars, practitioners, and policymakers in architecture and urban planning, advocating for environmentally conscious, socially inclusive and economically viable development practices.

groups

Batoul Hasanin mail -

Alaa J. Kadi mail

link https://doi.org/10.54216/IJBES.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new