Author name cluster

Muhammad Naeem

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6 papers

1 author row

FM Conference 2026 Conference Paper

Efficient Multi-level Mine Dewatering Using Uppaal Stratego

Muhammad Naeem
Cristina Seceleanu
Alf J. Isaksson
Tiberiu J. Seceleanu

Abstract Effective water management in underground mining requires maintaining safe reservoir levels while minimizing the high energy costs of continuous pumping. Although flexible electricity pricing enables cost-aware operation, traditional threshold-based controllers cannot exploit this flexibility efficiently. This paper presents an industrial case study on efficient mine dewatering using reinforcement-learning-based control synthesized with the Uppaal Stratego framework. A baseline threshold controller is first implemented, followed by a reinforcement-learning controller trained on forecast inflows and day-ahead electricity prices to minimize pumping costs while limiting pump switching. To ensure safety during learning without distorting the optimization objective, we introduce a pre-shield that blocks unsafe transitions. We formally show that this pre-shield is maximally permissive with respect to a monotonicity safety objective. Simulation results demonstrate that the learning-based strategy reduces total energy consumption by up to 40% compared to threshold-based control, while maintaining safe operation in all scenarios.

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EAAI Journal 2025 Journal Article

A disc-spherical fuzzy combined compromise solution for efficient smart grid energy management

Shahzaib Ashraf
Wania Iqbal
Muhammad Naeem
Vladimir Simic
Bushra Batool
Dragan Pamucar

The imperative inclusion of sustainable and renewable energy sources into smart grid networks underscores the need for resilient and eco-friendly energy infrastructures. The problem addressed in this paper is the selection of the most effective technique for integrating renewable energy into smart grids using decision-making frameworks. Even with the progress made in integrating renewable energy, the complexity and unpredictability of choosing the optimum energy sources are still difficult for current decision-making frameworks to adequately represent. To solve this, the study aims to develop an integrated multiple attribute decision-making (MADM) methodology by utilizing a combined compromise solution (CoCoSo) and disc spherical fuzzy (D-SF) approach. The introduction of innovative logarithmic-based operations enhances the efficiency of D-SF sets. Novel operational laws for D-SF numbers are established, and corresponding aggregation operations as specified by these laws are proposed, exploring their excellent properties. The D-SF method employs the criteria importance through intercriteria correlation (CRITIC) method for attribute weight computation. Lastly, a MADM approach is presented, namely the CRITIC-based D-SF CoCoSo method, to rank the best energy sources for smart grid network integration. A comparative study is carried out among the suggested approach and current techniques to assess its feasibility and practicability. Furthermore, a sensitivity investigation shows the resilience of the proposed methodology. Our findings reinforce the critical role of renewable resources in achieving sustainable energy goals and demonstrate how smart energy grids enhance efficiency, reliability, and adaptability in managing diverse energy sources through the proposed decision-making methodology.

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EAAI Journal 2025 Journal Article

Multi-criteria group decision-making method using Spherical Fuzzy Z-Numbers for smart technology revolution in municipal waste management

Shahzaib Ashraf
Muhammad Naeem
Chiranjibe Jana
Maria Akram
Gerhard-Wilhelm Weber

Due to the new phenomenon of globalization and the fast growing cities, there is drastic pressure on the conventional methods of waste management hence the need for innovative management wastage that is proactive to the theme of environmental and economic challenge of the modern world. This paper aims at investigating the following research question: How has the introduction of smart technologies impacted waste management in the context of a mid-sized city that is in the cross-road of generating more wastes while at the same time, concerns over ecological attainability. They present a new type of Spherical Fuzzy Z-Number Sets in organizational environments dealing with multi criteria group decision making where it possess higher order of uncertainty than conventional fuzzy sets. For handling the multi facility nature of multi criteria group decision making in waste management, we propose the so-called Additive Ratio Assessment method when the attribute weights are unknown. To ensure that the criteria weights are determined objectively in this research, the CRITIC (CRiteria Importance Through Intercriteria Correlation) technique is used. The first part of the study provides a theoretical framework of spherical fuzzy Z-numbers concerning accuracy, scoring functions, and operations. Then we introduce this framework to actual multi criteria group decision making cases in municipal waste management to show that how Spherical Fuzzy Z-Number can facilitate decision-making processes by dealing with the vagueness and fuzziness of stakeholder preferences. The TODIM (Tomada de Decisao Iterativa Multicriterio) technique is used in this to validate and compare for efficiency of the proposed additive ratio assessment method. Besides, this research will not only enhance the theoretical aspect of fuzzy decision making but also present a feasible and effective framework for handling unsound and random decision making problems especially within the context of urban waste management.

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EAAI Journal 2025 Journal Article

Selection of Internet of Things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy Schweizer–Sklar aggregation model

Shahzaib Ashraf
Muhammad Naeem
Wania Iqbal
Hafiz Muhammad Athar Farid
Hafiz Muhammad Shakeel
Vladimir Simic
Erfan Babaee Tirkolaee

The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer–Sklar (SS) τ -norm and τ -conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS τ -norm and τ -conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators’ efficacy but also underscores their relevance in real-world decision-making scenarios.

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YNIMG Journal 2012 Journal Article

Electrophysiological signatures of intentional social coordination in the 10–12Hz range

Muhammad Naeem
Girijesh Prasad
David R. Watson
J.A. Scott Kelso

This study sought to investigate the effects of manipulating social coordination on brain synchronization/de-synchronization in the mu band. Mu activation is associated with understanding and coordinating motor acts and may play a key role in mediating social interaction. Members of a dyad were required to interact with one another in a rhythmic finger movement coordination task under various instructions: intrinsic where each member of the dyad was instructed to maintain their own and ignore their partner's movement; in-phase where they were asked to synchronize with their partner's movement; and anti-phase where they were instructed to syncopate with their partner's movement. EEG and movement data were recorded simultaneously from both subjects during all three tasks and a control condition. Log power ratios of EEG activity in the active conditions versus control were used to assess the effect of task context on synchronization/de-synchronization in the mu spectral domain. Results showed clear and systematic modulation of mu band activity in the 10–12Hz range as a function of coordination context. In the left hemisphere general levels of alpha-mu suppression increased progressively as one moved from intrinsic through in-phase to anti-phase contexts but with no specific central–parietal focus. In contrast the right hemisphere displayed context-specific changes in the central–parietal region. The intrinsic condition showed a right synchronization which disappeared with the in-phase context even as de-synchronization remained greater in the left hemisphere. Anti-phase was associated with larger mu suppression in the right in comparison with left at central–parietal region. Such asymmetrical changes were highly correlated with changing behavioral dynamics. These specific patterns of activation and deactivation of mu activity suggest that localized neural circuitry in right central–parietal regions mediates how individuals interpret the movements of others in the context of their own actions. A right sided mechanism in the 10–12Hz range appears to be involved in integrating the mutual information among the members of a dyad that enables the dynamics of social interaction to unfold in time.

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EAAI Journal 2010 Journal Article

A comparative study of heuristic algorithms: GA and UMDA in spatially multiplexed communication systems

Sajid Bashir
Muhammad Naeem
Syed Ismail Shah

A performance comparison of genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA) as decoders in multiple input multiple output (MIMO) communication system is presented in this paper. While the optimal maximum likelihood (ML) decoder using an exhaustive search method is prohibitively complex, simulation results show that the GA and UMDA optimized MIMO detection algorithms result in near optimal bit error rate (BER) performance with significantly reduced computational complexity. The results also suggest that the heuristic based MIMO detection outperforms the vertical bell labs layered space time (VBLAST) detector without severely increasing the detection complexity. The performance of UMDA is found to be superior to that of GA in terms of computational complexity and the BER performance.

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