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Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ4/r/gumushane/13770
  TREE TOPOLOGY-BASED RESOURCE ALLOCATION AND DISCOVERY ALGORITHM FOR ONE HOP

  Genemo, Musa Dima; Saroja, Yalamanchili; Gopi, Kistam; Mutyam, Veeramani; Reddy, Bhargavi Peddi; Navatejareddy, Ramireddy; Madala, Srinivasa Rao; Sai, Nimmagadda Yaswanth; Koteswararao, K.

  The components of a distributed system include multiple computing nodes which may use different types of hardware and connect to each other through a communication network that operates without shared memory or clock systems. The primary advantage of distributed systems enables organizations to share resources which results in faster computing times together with increased data availability and system reliability. The process of resource sharing requires organizations to first identify their resources and then assign those resources before anyone can access them. Computer systems implement caching to store frequently used data and program segments. The new caching technique called virtual caching enables a host node to delegate its cache authority for specific pages to nearby nodes who can use its partial cache space. The virtual caching protocol specifies neither the client node's process for accessing virtual cache nor its method for retrieving virtual cache from distant servers. The resource discovery and allocation problem needs us to develop a solution for this particular issue. The resource donation process requires us to find donor nodes who have extra resources while we need to assess which resources will be given to deficient nodes at each connected network point. Our goal is to achieve complete fulfillment of requests which deficient nodes make for resources. The importance of virtual caching lies in the fact that it doesn't require physical movements of cache. The requirements of the system determine when virtual cache allocation can be modified. The proposed heuristics provide efficient performance because they require minimal time and perform limited communication operations. We assess distribution quality through a comparison between heuristic distribution results and the distribution outcomes from the ILP problem solution. We present and assess several heuristics which aim to reduce unfulfilled resource requests that arise from resource-deficient nodes during their resource search process which has a maximum distance of two hops. The paper restricts its analysis to single hop operations only. The paper demonstrates through its research that this algorithm reaches optimal solution status based on our established benchmark criteria. The resource distribution for bounded hops restricts its operations to one-hop resource distribution. Every resource-surplus node transfers additional nodes to other nodes while maintaining its own resource balance without reaching resource-deficient status. Load distribution cannot be divided into infinite parts. Our research focuses on measuring the fulfillment level for each resource-deficient node request.

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• BildiriYalnız künye2026/r/gumushane/12245
  Advancing Digital Wallet Security Through Multi-Factor Authentication and Homomorphic Encryption-Based Privacy Preservation

  Genemo, Musa Dima; Kumar, Kotha Anil; Sadat, Quazi Taif; Santosh, Kathari; Sreeram, G.; Raj, I. Infant

  Digital wallets have been provided by FinTech businesses thus reengineering the financial services but nonetheless there is a possibility of keyloggers, bogus personas and assaults of the server with conventional systems using PIN authentication. The current multi-factor authentication configuration usually leaves sensitive biometric information exposed as the logins are processed. To address these security issues, the paper proposes the MHESecAuth that employs Multi-Factor Homomorphic Encryption and Smart Contracts to authenticate. The permission to make transactions is secure since the system operates with PINs, OTPs and no one can view the biometrics. As per experimental research, the system can be accurate at 96.4 percent, false acceptance rate is 0.08 percent, false rejection rate is 0.6 percent and encryption consumes 160ms. The framework optimizes security, privacy and performance against both MFA, encryption-only and combined solutions and is therefore suitable in the development of quantum-secure digital wallets and still blockchain-based audit and transparency.

  İncele
• BildiriYalnız künye2025/r/gumushane/14320
  Multi Objective Reinforcement Tuning for Responsible Generative AI in Automated Financial Advisory Systems

  Bhandarkar, Vishakha D; Genemo, Musa Dima; Muda, Iskandar; Murugan, Rekha; Sreeram, G.; Dharani, N.

  Generative AI in a financial context poses risks to the areas of regulatory compliance, accuracy in the content being delivered, and appropriateness in terms of emotion. This research proposes ReFiGen, a framework that can be used to create compliant financial content that is compliant with the regulatory framework and the sentiment of the users. Following domain-specific fine-tuning, ReFiGen blends sentiment alignment with reinforcement learning to optimize on compliance. It demonstrates better performance than baseline models with a high data accuracy rate of 98.35 percent and can generate a fluent output that is emotionally congruent, and regulation-minded. The results of experiments indicate that ReFiGen is both clear and coherent and exactly follows financial guidelines. This practice affirms that the combination of unstructured compliance modeling and affective control is a successful aspect of LLMs. ReFiGen is one of the milestones on the journey of trustworthy and ethically aligned AI in the financial advisory market.

  İncele
• BildiriYalnız künye2023Scopus SJRQ4/r/gumushane/14497
  Quantum Convolutional Neural Network for Agricultural Mechanization and Plant Disease Detection

  Genemo, Musa

  Agricultural research is essential in addressing the challenges of food production and meeting the needs of a growing population. Severe crop disorders can lead to food insecurity, potentially causing disastrous effects on agricultural production, such as yield losses, reduced crop quality, and even complete crop failures. By leveraging computer vision techniques, it is possible to develop systems that detect signs of crop diseases at an early stage, facilitating prompt intervention and management. Environmental changes, such as rainfall and biosolids, can cause plants to become inflamed by microorganisms. While manual observation and assessment of leaf conditions have traditionally been used for crop disease detection, there is increasing interest in leveraging technology to automate and enhance the process. Manual detection of crop diseases based on leaf conditions is subjective, time consuming, and vulnerable to errors. Utilizing computer vision methods to detect plant diseases helps improve plant health. The concept of applying quantum convolutional neural networks (QCNNs) for agricultural mechanization and plant disease detection is an interesting area of research that combines quantum computing and deep learning techniques. However, it is important to note that quantum computing is still in its early stages, and practical implementations of QCNNs for real-world applications, including agriculture, are not yet widespread. The proposed work of paper focuses on the detection of leaf blight disease by classifying leaves into late blight, early blight, and healthy leaves. This work is divided into two stages: segmentation and classification. For segmentation, MobileNetV2 is used, and an emerging quantum machine learning technique named QCNN is used for classification. The main objective is to provide and propose an effective and fast automated technique to detect and classify blight disease, which will help farmers identify blight disease easily. The proposed model achieved a classification accuracy of 0.969. Furthermore, the results were further validated against recent findings in the field. The presented outcome implies that the model’s performance is remarkable.

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  AtıfScopus12·OpenAlex8

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• Derleme MakaleAçık Erişim2022Scopus SJRQ4/r/gumushane/12745
  Suspicious activity recognition for monitoring cheating in exams

  Genemo, Musa Dima

  Video processing is getting special attention from research and industries. The existence of smart surveillance cameras with high processing power has opened the for making it conceivable to design intelligent visual surveillance systems. Know a day it is very possible to assure invigilators safety during the examination period. This work aims to distinguish the suspicious activities of students during the exam for surveillance examination halls. For this, a 63 layers deep CNN model is suggested and named "L4-BranchedActionNet". The suggested CNN structure is centered on the alteration of VGG-16 with added four blanched. The developed framework is initially turned into a pre-trained framework by using the SoftMax function to train it on the CUI-EXAM dataset. The dataset for detecting suspicious activity is subsequently sent to this pre-trained algorithm for feature extraction. Feature subset optimization is applied to the deep features that have been obtained. These extracted features are first entropy coded, and then an ant colony system (ACS) is used to optimize the entropy-based coded features. The configured features are then input into a variety of classification models based on SVM and KNN. With a performance of 0.9299 in terms of accuracy, the cubic SVM gets the greatest efficiency scores. The suggested model was further tested on the CIFAR-100 dataset, and it was shown to be accurate to the tune of 0.89796. The result indicates the suggested frameworks soundness.

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  AtıfScopus33·WoS16·OpenAlex47

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