From Data to Decisions: Integrating Speech Analytics and Machine Learning in Call Centers using AI tools

Ruxsoraxon; Nargiza; Maxbuba

doi:https://doi.org/10.54216/AJBOR.130204

International Journal of Neutrosophic Science | 2025

On Complex Fuzzy Soft Graph With Operations

The goal of this paper is to study complex fuzzy soft graph (CFSG). We introduce the concept of complex fuzzy soft graph from apply complex fuzzy set on fuzzy soft graph. The notations and definitions of some operations on two complex fuzzy soft graphs presented such as union, cartesian product, tensor product, normal product and composition of two complex fuzzy soft graphs. Also, a decision-making (DM) problem on supply chain management is discussed.

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Sana Abu-Ghurra mail -

Ghada Alafif mail -

Eman A. AbuHijleh mail -

Firas Safi mail

link https://doi.org/10.54216/IJNS.260331

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

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Fusion: Practice and Applications | 2025

Administrative Empowerment at Egyptian Universities: Field Study at Al-Arish University

This study aims to evaluate the reality of administrative empowerment in Egyptian universities, with a case study conducted at Al-Arish University, considering national efforts to improve institutional performance and the quality of higher education. Using a descriptive-analytical design, data was gathered through a structured questionnaire distributed to a purposeful sample of 40 administrative staff and mid-level managers. Data gathered from the structured questionnaire were analyzed to determine the levels of administrative empowerment at each of its key dimensions by using descriptive statistical methods comprising frequency distributions and weighted means. The study investigates the effectiveness of empowerment practices and their influence on institutional outcomes. Findings reveal a broad disparity between theory and practice with the levels of empowerment ranging from low to moderate. The major hindrances to participation are poor employee participation in decision-making, a lack of managerial support, discriminatory workplace culture, and low leadership development. Restricted senior management-employee interactions were also reported to hinder participatory practices. The study recommends that empowerment policies with clarity, actionable implementation strategies, and organizational resolve are necessary for creating a positive and motivational work culture. Recommendations made include enhancing internal communication, innovation, and incentive mechanisms to improve administrative performance. Such steps are vital to ensuring a productive environment to facilitate sustainable development and institutional change. Empirical findings demonstrate the significance of strategic empowerment to aid governance reform and quality assurance in Egyptian higher education.

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Amr El Koshiry mail -

Entesar Eliwa mail -

Ahmed Abd Allah Tony mail -

Ahmed Mahmoud Lotfy El-Masry mail

link https://doi.org/10.54216/FPA.200215

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

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Journal of International Economics Research | 2025

Organizational Mechanism of Interaction of Higher Education Institutions of Belarus and Uzbekistan with Employers

Research shows that effective communication between higher education institutions and employers contributes to the development of competent personnel prepared to solve real-world problems in production and business. This issue is highly relevant given rapid changes in the economy and technological processes, requiring the constant adaptation of educational programs and advanced training for graduates. This article examines areas of cooperation between universities in Belarus and Uzbekistan and proposes a model for their interaction with enterprises in the real sector of the economy in the context of the emerging knowledge economy and the trend toward developing education as a key element of scientific, technical, and innovation policy.

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Karpovich Viktar mail -

Ponomareva Natallia mail

link https://doi.org/10.54216/JIER.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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Galoitica: Journal of Mathematical Structures and Applications | 2025

Development of Numerical Algorithms for Solving Nonlinear Partial Differential Equations

This study focuses on the development of efficient numerical algorithms for solving nonlinear partial differential equations (PDEs). The research integrates theoretical analysis and practical numerical experiments to address the challenges posed by nonlinear PDEs, which often lack closed-form solutions and exhibit sensitivity to initial and boundary conditions. Benchmark models such as Burgers’ Equation, the Korteweg–de Vries (KdV) Equation, and the Navier–Stokes Equations are highlighted due to their significance in physical and engineering applications. Traditional numerical methods—Finite Difference Method (FDM), Finite Element Method (FEM), and Finite Volume Method (FVM)—are reviewed with respect to accuracy, stability, and computational efficiency. Numerical stability concepts, including Von Neumann analysis and the CFL condition, are discussed alongside sources of error and strategies for error reduction. New algorithms were proposed by enhancing traditional schemes, incorporating adaptive mesh refinement, and integrating stability techniques. Numerical experiments on benchmark problems demonstrated improved accuracy, enhanced stability in handling nonlinear terms, and acceptable computational efficiency. The findings emphasize the importance of selecting suitable numerical methods, conducting stability analysis, and applying adaptive techniques. The study recommends higher-order schemes, conservative formulations for fluid dynamics, and double precision when necessary, ensuring reliable and reproducible computational results.

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Zahraa Ahmed Sahib mail -

Najmeh Malek Mohammadi mail

link https://doi.org/10.54216/GJMSA.120203

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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Fusion: Practice and Applications | 2025

Early Detection of Diseases in Hydroponic Saffron Crops Using a Diffused Concurrent Convolution Neural Network for Smart Farming

The detection of diseases in hydroponically cultivated saffron should be carried out as early and accurately as possible to maintain the quality of the yield, minimize losses, and promote sustainable farming practices. Manual diagnosis strategies are not suitable for high-density hydroponic systems where early symptoms tend to be subtle, as these methods are slow and rely extensively on experts. This research aims to develop a novel framework based on deep learning technology, using a Diffused Concurrent Convolutional Neural Network (DCCNN) to perform image analysis and detect diseases in saffron crops. The modified DCCNN includes a hierarchical three-stage classification pipeline consisting of crop recognition, disease detection, and Classification of the specific diseases, adding an “unknown” category for non-target or ambiguous outputs at each stage to enhance flexibility. The digression from standard deep learning techniques is justified due to the DCCNN construction, which contains a learnable diffusion layer and concurrent multi-scale convolutional blocks, and thus encapsulates strong feature propagation with fine-grained detection of complex and low-data environments. Evaluation on a specially annotated dataset of hydroponic saffron showed strong performance with up to 99.4% classification accuracy, exceeding well-known CNN baselines including EfficientNet and ResNet50. Additionally, the model processes static crop images and associated environmental sensor data, collectively referred to as 'non-sequential crop data,' focusing on spatial features without temporal dependencies. These findings confirm that the system, which is based on DCCNN, provides a transferable solution for precision disease detection in controlled-environment agriculture systems and can be extended to other high-value crops.

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Vivek Raj mail -

Gregory Allen mail -

Ananth Prabhu G. mail -

Melwin D. Souza mail

link https://doi.org/10.54216/FPA.200216

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

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Journal of International Economics Research | 2025

The Resilience–Efficiency Frontier in International Trade: Structural Changes and Cost Impacts after COVID-19

The post-pandemic period has led to a major reorientation in international economics, shifting the focus of global trade from cost efficiency to structural resilience. This study examines four key factors—supply chain diversification, reshoring initiatives, logistics disruptions, and cost shocks—to explore the transition from the era of "Fragile Efficiency" to a system focused on overall viability. Drawing on global trade data from 2024–2026 and analytical frameworks provided by the IMF, the research introduces the concept of the Resilience–Efficiency Frontier (REF). The findings show that moving from Just-in-Time (JIT) to Just-in-Case (JIC) manufacturing helps reduce the volatility caused by the Bullwhip Effect but also creates a persistent form of "Complexity Inflation." Empirical results indicate that firms are now incurring a "Resilience Premium" of 12–15% to protect their production and distribution networks. The study also emphasizes that trade is no longer purely economic but is increasingly connected to national security and environmental regulations, such as the EU’s Carbon Border Adjustment Mechanism (CBAM). This marks a clear shift from the deflationary trends that characterized global trade in the past.

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Summera Khalid mail

link https://doi.org/10.54216/JIER.020205

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2025

A Unified Nonlinear Fiber-Based Framework for Predicting Axial–Flexural Interaction in Reinforced Concrete Shear Walls

Purpose - Reinforced concrete (RC) shear walls are critical lateral-load resisting elements; however, reliable prediction of their axial–flexural interaction behavior remains difficult, particularly for irregular geometries and nonuniform reinforcement layouts. This study aims to develop an accurate and versatile analytical framework to evaluate the global axial–flexural response of RC shear walls. Design/methodology/approach - A fully nonlinear, code-independent numerical framework is formulated based on strain compatibility, equilibrium enforcement, and curvature-controlled sectional analysis. The model incorporates advanced stress–strain relationships for confined and unconfined concrete, a bilinear steel constitutive law, and a high-resolution fiber discretization scheme capable of representing arbitrary cross-sectional shapes. The framework generates complete moment–curvature responses and axial–moment (P–M) interaction diagrams under uniaxial bending. Findings - The results exhibit strong agreement with established analytical models and reported experimental trends. The framework accurately captures nonlinear degradation, neutral-axis migration, confinement effects, and the influence of reinforcement distribution on axial–flexural capacity. Practical implications - The proposed model provides a reliable tool for performance-based assessment, design, and optimization of RC shear walls beyond simplified code provisions. Originality/value - The study introduces a geometry-independent, fully nonlinear modeling approach that enables detailed evaluation of irregular RC shear walls with enhanced accuracy and practical applicability.

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Islam Ibrahim Shoheb mail -

Moustafa Metwally mail -

Intan Rohani Endut mail

link https://doi.org/10.54216/IJBES.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2025

Dispute Management in Engineering Contracts Using Artificial Intelligence

The study put forward an integrated artificial intelligence-based approach to the analysis and prediction of contracting disputes in Engineering Projects, especially through Machine Learning methods and Deep Learning methods. Current ways of managing contracts cannot effectively deal with the complicated nature of Legal Texts and do not provide for early identification of potential disputes. This developed System was built using the Python Programming Language, using key libraries for Natural Language Processing (NLP) and Machine Learning (ML). The cache of Contract Documents in all formats was transformed into numerical vectors using TF-IDF once all Document Processing and Clean-up Procedures were completed. Multiple Models were built, with trained versions of each, including Logistic Regression, SVM, Voting Classifiers and an MLP (Multi-Layer Perceptron) based Neural Network model. Since each Contracting Dispute was modelled separately to improve overall prediction accuracy, initial recommendations for resolution are generated. Results show that the MLP performed in a SUPERIOR fashion, with an Overall Model Accuracy of 88%, and F1 Score of 0.874, effectively classifying Contracting Disputes relating to Delays, Payments and Scope Variations. The application of this framework to an actual example taken from the construction industry in Syria reaffirmed the capability of automating contract text review and improving risk management. This reinforces the importance of artificial intelligence as a tool for increasing proactive decision-making and minimizing conflict in engineering projects.

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Rania Bashir mail -

Marek Salamak mail -

Sonia Ahmed mail

link https://doi.org/10.54216/IJBES.110202

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2025

From Industry Labels to Offer Prices: Measuring Ai Association Effects on IPOS

As more companies position themselves to capitalize on becoming AI-driven innovators or market disruptors rather than traditional technology firms, this raises an important question for valuation research. The purpose of this study is to collect and analyze the various datasets, indicators, and patterns available in the current landscape of initial public offerings (IPOs) that are associated with artificial intelligence (AI). To (a) evaluate the effectiveness of econometric methods used within AI-related IPO analyses based primarily on narrative valuation and financial modeling, and (b) identify which industry indicators are the most predictive of pricing outcomes within these offerings. This paper then extends the existing literature by linking the narrative and quantitative dimensions of IPO valuation with the behavioral economics of investors and underwriters. Firms from AI-intensive sectors have a valuation premium and are relatively more appealing than non-AI peers in investor sentiment and pricing expectations. This results in a framework of factors defining AI association, valuation dynamics, and narrative influence that are considered relevant for the capital formation process. Within each model, results show differential effects for companies that belong to and do not belong to AI-related industries in price formation and fundraising outcomes. By bringing together descriptive insights and regression-based evidence on AI affiliation and IPO performance, this study reinforces the possibility of narrative bias and the symbolic influence of AI association through the combined analysis of market data from technology, financial, and innovation ecosystems. There is, however, a need for greater refinement concerning these classification measures to further improve the accuracy of IPO valuation models.

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Shakhzod Saydullaev mail

link https://doi.org/10.54216/AJBOR.130203

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2025

From Data to Decisions: Integrating Speech Analytics and Machine Learning in Call Centers using AI tools

The current swift advancement of Artificial Intelligence (AI) technologies is transforming operations management by integrating real-time data-driven insights for cost optimization and improved decision-making. In this paper, we explore the fusion of artificial intelligence (AI) technologies in call center operations management, focusing on how the integration of speech-to-text, text-to-speech, and speech analytics tools is revolutionizing customer interaction and decision-making. The fusion of real-time conversational data with advanced machine learning algorithms enables organizations to extract actionable insights, optimize key performance indicators (KPIs), and enhance customer satisfaction. Furthermore, in this research, we are estimating the approximate return on investment in the benchmarked private sectors of Uzbekistan, thus contributing to the future networks in the industry. Our research work bridges the gap between theoretical AI advancements and their practical applications, contributing to the growing body of knowledge on information fusion in intelligent systems in the emerging Uzbek market.

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Ruxsoraxon Abduqayumova mail -

Nargiza Alimukhamedova mail -

Maxbuba Ismailova mail

link https://doi.org/10.54216/AJBOR.130204

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new