Mohd Anjum’s research while affiliated with Aligarh Muslim University and other places

The most innovative Service-Optimized Logging for Resource Allocation (SOL-RA) is in situations that support 6G connection. The CyberTwin architecture optimizes the quality of service provided to end users in 6G communications by using the dependability of terahertz connections and interactions of a machine-type nature. Elastically sharing elastic resources among users is made possible by the proposed resource allocation strategy, which makes use of proprietary logger functions. This ensures that efficient allocation is achieved without overlapping or lengthy wait periods. Within the framework of SOL-RA, a categorization mechanism is implemented for dense requests, which differentiates them as either stationary or priority services. Resource allocations are dynamically done based on this categorization, which enhances the organization's responsiveness to different needs. Through a tree classifier learning mechanism, CyberTwin logs play an essential part in the processing of requests and the preparation of resources. This helps to ensure that resources are distributed without inefficiency. Requests that are generating a backlog are identified and processed in parallel by the system, which significantly reduces the amount of time that is wasted waiting. In order to guarantee a specialized distribution for the categorized outputs, resource allocations are directed by CyberTwin data logs. An examination of the performance of the SOL-RA scheme takes into account important metrics such service latency, service backlog, resource usage, and request-to-response ratio. This research offers insights into the efficacy of the scheme in maximizing service quality and resource consumption in 6G settings.

[This corrects the article DOI: 10.1371/journal.pone.0315251.].

Credit risk assessment uses finance-based behavioural patterns for micro-lending purposes and organizations. The repayment behaviour and credit stability patterns are analyzed across varying repayment tenures and financed amounts. Due to limited borrower data and fluctuating financial patterns, micro-lending platforms have substantial hurdles when it comes to effectively evaluating credit risk. This article introduces a Collaborative Filtering Method using Lending Pattern Analysis (CFM-LPA). The proposed method is enhanced through collaborative federated learning, enabling the analysis of these patterns. This approach evaluates the return rate, credit limit, and consumer response behaviours. Federated learning processes one or more of these factors to assess diverse lending patterns. Based on these evaluations, the behavioural factor is updated for each return period, influencing the credit risk for subsequent return periods and supporting the financial stability of micro-lending operations. The model is trained individually on the identified factors, allowing the behavioural factor to be filtered. New credit risks are identified using this filtered factor from the previous return period. These insights help define new behavioural patterns for the specified credit limit. The proposed method enhances risk detection accuracy by 14.03% and improves return rate analysis by 13.28% across financed amounts. The above abstract is also graphically presented.

In the context of Industry 4.0, improving energy efficiency in smart factories has emerged as a key priority to drive sustainable industrial growth. However, identifying optimal energy-saving solutions is challenging due to the inherent uncertainty and complexity in decision-making. This study addresses these challenges by proposing a multi-criteria decision-making (MCDM) framework that leverages intuitionistic fuzzy sets (IFSs) to manage ambiguity in the evaluation process. To advance this framework, we develop a suite of novel aggregation operators (AOs), including the intuitionistic fuzzy softmax Dubois-Prade (IFSDP), intuitionistic fuzzy softmax interactive Dubois-Prade weighted average (IFSIDPWA), intuitionistic fuzzy softmax interactive Dubois-Prade ordered weighted average (IFSIDPOWA), and intuitionistic fuzzy softmax interactive Dubois-Prade weighted geometric (IFSIDPWG), which effectively handle uncertainty and vagueness in the criteria assessments. The method based on the removal effects of criteria (MEREC) is utilized to objectively determine the criteria weights, ensuring a robust evaluation structure. For ranking, the alternatives are evaluated through the ranking of alternatives using functional mapping of criteria sub-intervals into a Single Interval (RAFSI) approach. A case study involving eight criteria and five energy-saving solutions demonstrates the framework’s feasibility, with results confirming the effectiveness of our AOs and RAFSI technique in guiding decision-makers toward sustainable energy solutions for smart factories. This framework is poised to support sustainable manufacturing practices in Industry 4.0, fostering greener and more efficient industrial operations.

As the global population grows, urbanization depletes water resources and significantly reduces cropland available for agriculture. This study proposes a Big Data Analytics-Integrated Agriculture Resource Management Framework (BDA-ARMF) to optimize resource utilization and enhance farm sustainability. The integration of BDA in agriculture offers substantial advantages, including improved management of consumer demand, enhanced farm operations, sustainable food production and better alignment of supply with demand. The framework combines BDA with the Internet of Things and cloud computing to improve accuracy, intelligence and sustainability in agriculture. Efficient data-driven farming requires actionable insights to minimize resource waste and environmental contamination. The proposed model outperforms previous approaches, delivering significant improvements in water management (97.8%), prediction accuracy (97.6%), production efficiency (96.4%), resource consumption reduction (11.5%) and risk assessment enhancement (94.7%). The proposed framework reduces resource waste and mitigates environmental impact, enabling sustainable agricultural systems and efficient, data-driven farming practices.

Aims In the dynamic landscape of healthcare, integrating Artificial Intelligence paradigms has become essential for sophisticated brain image analysis, especially in tumor detection. This research addresses the need for heightened learning precision in handling sensitive medical images by introducing the Fragmented Segment Detection Technique. Background The ever-evolving healthcare landscape demands advanced methods for brain image analysis, particularly in detecting tumors. This study responds to this need by introducing the Feature Segmentation and Detection Technique (FSDT), a novel approach designed to identify brain tumors precisely using MRI images. The focus is on enhancing detection accuracy, even for diminutive tumors. The primary objective of this study is to introduce and evaluate the efficacy of FSDT in identifying and sizing brain tumors through advanced medical image analysis. The proposed technique utilizes cross-section segmentation and pixel distribution analysis to improve detection accuracy, particularly in size-based tumor detection scenarios. Methods The proposed technique commences by fragmenting the input through cross-section segmentation, enabling meticulous separation of pixel distribution in various sections. A Convolutional Neural Network then independently operates sequentially on the minimum and maximum representations. The segmented cross-section feature, exhibiting maximum accuracy, is employed in the neural network training process. Finetuning of the neural network optimizes feature distribution and pixel arrangements, specifically in consecutive size-based tumor detection scenarios. Results The FSDT employs cross-sectional segmentation and pixel distribution analysis to enhance detection accuracy by leveraging a diverse dataset encompassing central nervous system CNS tumors. Comparative evaluations against existing methods, including ERV-Net, MRCNN, and ENet- B0, reveal FSDT's superiority in accuracy, training rate, analysis ratio, precision, recall, F1-score, and computational efficiency. The proposed technique demonstrates a remarkable 10.45% increase in accuracy, 14.12% in training rate, and a 10.78% reduction in analysis time. Conclusion The proposed FSDT emerges as a promising solution for advancing the accurate identification and sizing of brain tumors through cutting-edge medical image analysis. The demonstrated improvements in accuracy, training rate, and analysis time showcase its potential for effective realworld healthcare applications.

The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to offer patient/user-related services that are ubiquitous and personalized. The authorized repository stores ubiquitous data for which access-level securities are granted. These security measures ensure that only authorized entities can access patient/user health information, preventing unauthorized entries and data downloads. However, recent sophisticated security and privacy attacks such as data breaches, data integrity issues, and data collusion have raised concerns in the healthcare industry. As healthcare data grows, conventional solutions often fail due to scalability concerns, causing inefficiencies and delays. This is especially true for multi-key authentication. Dependence on conventional access control systems leads to security flaws and authorization errors caused by static user behaviour models. This article introduces an Opportunistic Access Control Scheme (OACS) for leveraging access-level security. This approach is a defendable access control scheme in which the user permissions are based on their requirement and data. After accessing the healthcare record, a centralized IoT security augmentation and assessment is provided. The blockchain records determine and revoke the access grant based on previous access and delegation sequences. This scheme analyses the possible delegation methods for providing precise users with interrupt-free healthcare record access. The blockchain recommendations are analyzed using a trained learning paradigm to provide further access and denials. The proposed method reduces false rates by 11.74%, increases access rates by 13.1%, speeds up access and processing by 12.36% and 13.23%, respectively, and reduces failure rates by 9.94%. The OACS decreases false rates by 10.64%, processing time by 15.62%, and failure rates by 10.95%.

Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves, blood vessels, retinal health, and the impact of diabetes on the optic nerves. Fundus disorders are a major global health concern, affecting millions of people worldwide due to their widespread occurrence. Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy. As a result, accurate fundus detection is essential for early diagnosis and effective treatment, helping to prevent severe complications and improve patient outcomes. To address this need, this article introduces a Derivative Model for Fundus Detection using Deep Neural Networks (DMFD-DNN) to enhance diagnostic precision. This method selects key features for fundus detection using the least derivative, which identifies features correlating with stored fundus images. Feature filtering relies on the minimum derivative, determined by extracting both similar and varying textures. In this research, the DNN model was integrated with the derivative model. Fundus images were segmented, features were extracted, and the DNN was iteratively trained to identify fundus regions reliably. The goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally, taking into account the least possible derivative across iterations, and using outputs from previous cycles. The hidden layer of the neural network operates on the most significant derivative, which may reduce precision across iterations. These derivatives are treated as inaccurate, and the model is subsequently trained using selective features and their corresponding extractions. The proposed model outperforms previous techniques in detecting fundus regions, achieving 94.98% accuracy and 91.57% sensitivity, with a minimal error rate of 5.43%. It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead, thereby improving operational efficiency and scalability. Ultimately, the proposed model enhances diagnostic precision and reduces errors, leading to more effective fundus dysfunction diagnosis and treatment.