Browsing by Author "Das, M."

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A Heuristic Algorithm to Find a Path to be Blocked by Minimizing Traffic Disruption
(Institute of Electrical and Electronics Engineers Inc., 2020) Das, M.; Ambati, S.S.; Chandrasekaran, K.
This paper discusses the problem of finding a path to be blocked from the source to the destination for a vehicle to pass by in such a way that the traffic disruption caused is minimum. The traffic disruption caused by blocking a path is measured by estimating the number of vehicles that would have crossed any of the vertices in the path if the path had not been blocked. It also presents a heuristic algorithm 'Aggregate Traffic Minimization' (ATM) to solve the above problem. The traffic disruption caused by the path chosen by the ATM algorithm was compared with that of a popular baseline algorithm and was found that ATM outperforms the baseline alzorithm in most cases. Â© 2020 IEEE.
Bayesian Belief Network Analysis for SPAD System in Railways
(Institute of Electrical and Electronics Engineers Inc., 2024) Das, M.; Mohan, B.R.; Reddy G, R.M.; Chinmaya, C.; Umesh; Reddy G, V.M.; Vismay, P.
Even with a very strong network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even in the modern day. These occurrences, known as Signal Passed at Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach. However, when the system grows more complex, FTA too becomes more complex, and tough to maintain simplicity and ease of analysis. This opens the gateway to the exploration of other methods to model and assess such SPAD incidents and similar safety-critical systems in railways. Bayesian belief network (BBN) is considered to be a better model to represent this situation when it comes to handling complexity. This paper focuses on the implementation and advantages of the BBN model over FTA by considering the SPAD system as a case study. Both the FTA and BBN methods are then compared concerning modeling and analysis aspects. Â© 2024 IEEE.
Comparative analysis of Software Reliability using Grey Wolf Optimisation and Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2024) Kelkar, S.; Vishvasrao, S.P.; Agarwal, A.; Rajput, C.; Mohan, B.R.; Das, M.
Software reliability is a crucial aspect of software quality. In this paper, we aim to explore the application of Gray Wolf Optimization (GWO) for feature selection and classification on various software dataset, such as KC1, JM1, and PC5. We compare the performance of Machine Learning models (Random Forest, Decision Tree, Support Vector Machine, XGBoost and Neural Networks) with and without GWO-based feature selection. Our results demonstrate the effectiveness of GWO in enhancing the accuracy of software reliability analysis. Or Math in Paper Title or Abstract. Â© 2024 IEEE.
Degradation, wettability and surface characteristics of laser surface modified Mg–Zn–Gd–Nd alloy
(Springer, 2020) K.r, R.; Bontha, S.; M.r, R.; Das, M.; Balla, V.K.
This work evaluates the effects of laser surface modification on Mg–Zn–Gd–Nd alloy which is a potential biodegradable material for temporary bone implant applications. The laser surface melted (LSM) samples were investigated for microstructure, wettability, surface hardness and in vitro degradation. The microstructural study was carried out using scanning and transmission electron microscopes (SEM, TEM) and the phases present were analyzed using X-ray diffraction. The in vitro degradation behaviour was assessed in hank’s balanced salt solution (HBSS) by immersion corrosion technique and the effect of LSM process parameters on the wettability was analyzed through contact angle measurements. The microstructural examination showed remarkable grain refinement as well as uniform redistribution of intermetallic phases throughout the matrix after LSM. These microstructural changes increased the hardness of LSM samples with an increase in energy density. The wetting behaviour of processed samples showed hydrophilic nature when processed at lower (12.5 and 17.5 J/mm2) and intermediate energy density (22.5 and 25 J/mm2), which can potentially improve cell-materials interaction. The corrosion rate of as cast Mg–Zn–Gd–Nd alloy decreased by ~83% due to LSM. [Figure not available: see fulltext.]. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Design and development of an experimental setup for nanofinishing of exhaust valves using magnetorheological finishing to enhance functional performance
(Springer-Verlag Italia s.r.l., 2025) Sharma, K.; Singh, V.K.; Singh Rajput, A.S.; Das, M.
Exhaust valves in high-performance and racing engines require ultra-smooth surfaces to improve durability and operational efficiency. This study investigates the application of Magnetorheological (MR) polishing for finishing exhaust valve seats. MR fluid, consisting of micron-sized magnetic particles suspended in a carrier liquid, forms a semi-solid structure under a magnetic field, enabling precise surface finishing. An in-house experimental setup was developed, and various magnet configurations were tested to optimize the polishing zone. Computational investigations were conducted to analyze magnetic field distribution for 2-bar, 3-bar, 4-bar, and 5-bar magnet systems, with results validated using a Gauss-meter. Unlike prior MR polishing studies that focused mainly on optical or biomedical components, our work emphasizes automotive engine applications and demonstrates the optimization of a 4-magnet system to achieve uniform magnetic field distribution. The novelty lies in developing a cost-effective, adaptable, and reproducible MR polishing arrangement tailored for curved valve geometries, while addressing reproducibility through detailed experimental parameters. The primary objective was to optimize process parameters for MR polishing. Under optimal conditions—spindle speed of 750 RPM, stand-off distance of 1.5 mm, and polishing time of 17.5 min—the surface roughness (Ra) improved significantly from 0.613 ?m to 0.115 ?m. Measurements were performed using a 3D profilometer. Further surface characterization via Atomic Force Microscopy (AFM) showed a reduction in surface asperities, while Field Emission Scanning Electron Microscopy (FE-SEM) revealed fewer surface scratches. These results confirm the potential of MR polishing as an effective technique for enhancing the surface finish of critical engine components. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025.
Effect of heat treatment on microstructure, corrosion, and shape memory characteristics of laser deposited NiTi alloy
(2018) Marattukalam, J.J.; Balla, V.K.; Das, M.; Bontha, S.; Kalpathy, S.K.
The aim of this work is to study the effect of heat treatment on the microstructure, phase transformations, shape memory characteristics and corrosion behaviour of laser deposited equiatomic NiTi alloy. Dense samples of NiTi alloy were fabricated using Laser Engineered Net Shaping (LENS ) with two different laser energy densities by varying the scan speed and laser power. These samples were annealed for 30 min at 500 C and 1000 C in flowing argon, followed by furnace-cooling to room temperature. The resulting microstructures and properties were compared with the corresponding as-deposited samples. Microstructural analysis after heat treatment showed needle-shape martensite in the samples processed at lower laser energy density of 20 J/mm2, and lenticular or plate-like martensite in the samples processed at 80 J/mm2. The XRD results revealed relatively high concentration of martensite (B19?) in heat-treated NiTi alloy compared to as-processed samples. Furthermore, the heat treatment decreased the forward and reverse transformation temperatures of NiTi alloy from 80 95 C to 20 40 C, presumably due to annihilation of thermally induced defects. Interestingly, the samples annealed at 500 C showed a measurable increase of 1 2% in the shape memory recovery, from the net recovery of 8% exhibited by the as-processed NiTi alloy. The corrosion resistance of laser-processed NiTi alloy decreased upon annealing. 2018 Elsevier B.V.
Effect of heat treatment on microstructure, corrosion, and shape memory characteristics of laser deposited NiTi alloy
(Elsevier Ltd, 2018) Marattukalam, J.J.; Balla, V.K.; Das, M.; Bontha, S.; Kalpathy, S.K.
The aim of this work is to study the effect of heat treatment on the microstructure, phase transformations, shape memory characteristics and corrosion behaviour of laser deposited equiatomic NiTi alloy. Dense samples of NiTi alloy were fabricated using Laser Engineered Net Shaping (LENS™) with two different laser energy densities by varying the scan speed and laser power. These samples were annealed for 30 min at 500 °C and 1000 °C in flowing argon, followed by furnace-cooling to room temperature. The resulting microstructures and properties were compared with the corresponding as-deposited samples. Microstructural analysis after heat treatment showed needle-shape martensite in the samples processed at lower laser energy density of 20 J/mm2, and lenticular or plate-like martensite in the samples processed at 80 J/mm2. The XRD results revealed relatively high concentration of martensite (B19?) in heat-treated NiTi alloy compared to as-processed samples. Furthermore, the heat treatment decreased the forward and reverse transformation temperatures of NiTi alloy from 80 – 95 °C to 20–40 °C, presumably due to annihilation of thermally induced defects. Interestingly, the samples annealed at 500 °C showed a measurable increase of 1–2% in the shape memory recovery, from the net recovery of 8% exhibited by the as-processed NiTi alloy. The corrosion resistance of laser-processed NiTi alloy decreased upon annealing. © 2018 Elsevier B.V.
Effect of zinc and rare-earth element addition on mechanical, corrosion, and biological properties of magnesium
(2018) Kottuparambil, R.R.; Bontha, S.; Rangarasaiah, R.M.; Arya, S.B.; Jana, A.; Das, M.; Balla, V.K.; Amrithalingam, S.; Prabhu, T.R.
The present work aims to understand the effect of zinc and rare-earth element addition (i.e., 2 wt% Gd, 2 wt% Dy, and 2 wt% of Gd and Nd individually) on the microstructure evolution, mechanical properties, in vitro corrosion behavior, and cytotoxicity of Mg for biomedical application. The microstructure results indicate that the Mg-Zn-Gd alloy consists of the lamellar long period stacking ordered phase. The electrochemical and immersion corrosion behavior were studied in Hanks balanced salt solution. Enhanced corrosion resistance with reduced hydrogen evolution volume and magnesium (Mg2+) ion release were estimated for the Mg-Zn-Gd alloy as compared to the other two alloy systems. At the early stage of corrosion, formation of the oxide film inhibited the corrosion propagation. However, at the later stages, the breaking of the oxide film leads to shallow pitting mode of corrosion. The ultimate tensile strength of Mg-Zn-Gd-Nd is better than the other two alloys due to the uniform distribution of the Mg12Nd precipitate phase. The moderate strength in the Mg-Zn-Gd alloy is due to the low volume fraction of the secondary phase. The MTT (methylthiazoldiphenyl-tetrazolium bromide) assay study was carried out to understand the cell cytotoxicity on the alloy surfaces. Studies revealed that all three alloys had significant cellular adherence and no adverse effect on cells. 2018 Materials Research Society.
Effect of zinc and rare-earth element addition on mechanical, corrosion, and biological properties of magnesium
(Cambridge University Press, 2018) Kottuparambil, R.R.; Bontha, S.; Ramesh, M.R.; Arya, S.; Jana, A.; Das, M.; Balla, V.K.; Amrithalingam, S.; Prabhu, T.R.
The present work aims to understand the effect of zinc and rare-earth element addition (i.e., 2 wt% Gd, 2 wt% Dy, and 2 wt% of Gd and Nd individually) on the microstructure evolution, mechanical properties, in vitro corrosion behavior, and cytotoxicity of Mg for biomedical application. The microstructure results indicate that the Mg-Zn-Gd alloy consists of the lamellar long period stacking ordered phase. The electrochemical and immersion corrosion behavior were studied in Hanks balanced salt solution. Enhanced corrosion resistance with reduced hydrogen evolution volume and magnesium (Mg2+) ion release were estimated for the Mg-Zn-Gd alloy as compared to the other two alloy systems. At the early stage of corrosion, formation of the oxide film inhibited the corrosion propagation. However, at the later stages, the breaking of the oxide film leads to shallow pitting mode of corrosion. The ultimate tensile strength of Mg-Zn-Gd-Nd is better than the other two alloys due to the uniform distribution of the Mg12Nd precipitate phase. The moderate strength in the Mg-Zn-Gd alloy is due to the low volume fraction of the secondary phase. The MTT (methylthiazoldiphenyl-tetrazolium bromide) assay study was carried out to understand the cell cytotoxicity on the alloy surfaces. Studies revealed that all three alloys had significant cellular adherence and no adverse effect on cells. © 2018 Materials Research Society.
Evaluation for the thermokinetics of the autocatalytic reaction of cumene hydroperoxide mixed with phenol through isothermal approaches and simulations
(2018) Cao, C.-R.; Liu, S.-H.; Das, M.; Shu, C.-M.
In the petrochemical industry, estimation methods based on isothermal micro-calorimetry are used to precisely analyze the thermal hazards and risks associated with chemicals and to develop an inherently safer design (ISD). Here, a thermal activity monitor III (TAM III) was used under various isothermal conditions to obtain the thermokinetics parameters of reaction mechanisms. Cumene hydroperoxide (CHP), a typical organic peroxide, is decomposed by the action of sulfuric acid to yield phenol and acetone in equimolar quantities. CHP undergoes multiple complex reactions when an autocatalytic reaction occurs under isothermal decomposition. The following reaction scheme was considered in this study: A + nB ? (n + 1) B, A ? B, B ? C. This type of reaction generally accelerates as the reactant is consumed, and an autocatalytic substance is produced. As a result, an ISD is required for preparation, manufacturing, transportation, storage, and even elimination. The rich behavioral patterns of these autocatalytic reactions were revealed through multiple specific illustrations. 2018 Institution of Chemical Engineers
Evaluation for the thermokinetics of the autocatalytic reaction of cumene hydroperoxide mixed with phenol through isothermal approaches and simulations
(Institution of Chemical Engineers, 2018) Cao, C.-R.; Liu, S.-H.; Das, M.; Shu, C.-M.
In the petrochemical industry, estimation methods based on isothermal micro-calorimetry are used to precisely analyze the thermal hazards and risks associated with chemicals and to develop an inherently safer design (ISD). Here, a thermal activity monitor III (TAM III) was used under various isothermal conditions to obtain the thermokinetics parameters of reaction mechanisms. Cumene hydroperoxide (CHP), a typical organic peroxide, is decomposed by the action of sulfuric acid to yield phenol and acetone in equimolar quantities. CHP undergoes multiple complex reactions when an autocatalytic reaction occurs under isothermal decomposition. The following reaction scheme was considered in this study: A + nB ? (n + 1) B, A ? B, B ? C. This type of reaction generally accelerates as the reactant is consumed, and an autocatalytic substance is produced. As a result, an ISD is required for preparation, manufacturing, transportation, storage, and even elimination. The rich behavioral patterns of these autocatalytic reactions were revealed through multiple specific illustrations. © 2018 Institution of Chemical Engineers
Forecasting COVID-19 Transmission Patterns with Hidden Markov Model
(Institute of Electrical and Electronics Engineers Inc., 2024) Prakash, A.; Tomar, K.; Poptani, P.; Kumar, P.; Das, M.; Mohan, B.R.
Global health and healthcare system have faced major hurdles as a result of coronavirus. Pandemic still had impacts on community in every part of the world even with the efforts made to curb the disease transmission. We have sought to address some of these issues by utilising data sourced from JHU's CSSE [1]. This article concentrated on U.S. COVID-19 statistics concerning the number of infections and deaths in major towns. Only the relevant infection rates, death rates, and time columns were left in the pre-processing dataset. The above finding proves that the pandemic is evolving and began as a low rate of infections and deaths which increase with every passing moment. Secondly, we look at how death rates correlates with the highest infection rate. In an attempt to improve the forecast of COVID-19 spread for health care, manufacturers, economies and academic institutions this research is developed. Â© 2024 IEEE.
Formal Specification and Verification of Drone System using TLA+: A Case Study
(Institute of Electrical and Electronics Engineers Inc., 2022) Das, M.; Mohan, B.R.; Guddeti, R.M.R.
A Safety-Critical System is a System whose break-down may cause disastrous effects to the environment, damage the system, or cause loss of life. Sometimes loss or misuse of information can indirectly cause harmful impacts due to system failure. In this paper, we study the various components of a drone system and analyze the safety of this Safety-Critical System (SCS) by looking into the potential failure using Fault Tree Analysis (FTA). Drone system failure or crash has been specified and verified using the Temporal Logic of Actions (TLA+) tool. The TLA+ tool consists of mathematical notations to describe the system specification using discrete mathematical concepts or formal methods. We tried to build a TLA+ Specification and Verification for this drone system, parse it using the TLC model checker successfully, and observed the final number of states to justify the correctness of the specification. Â© 2022 IEEE.
Formal Specification and Verification of Time-Sensitive Drone Systems using TLA+: A Case Study
(Institute of Electrical and Electronics Engineers Inc., 2024) Surya, A.; Ayush, V.; Thakur, V.; Nair, V.; Das, M.; Mohan, B.R.
This research paper presents a detailed analysis of time sensitivity in drone system operations, exploring the critical impact of temporal factors on their performance and reliability using Temporal Logic of Action (TLA+), primarily aiming to enhance the reliability and safety of drone systems. The study addresses the critical need to rigorously model complex drone behaviors while considering their interactions with the environment to identify and rectify potential safety hazards and system flaws. It introduces a new dimension by emphasizing the temporal aspect in critical systems, providing a dynamic perspective on system reliability. This research introduces a real-time module to accommodate commonly used time patterns, responding to the growing demand for time-sensitive evaluations in mission-critical systems. Â© 2024 IEEE.
Hidden Markov Model for Hard Disk Drive Failure Detection
(Institute of Electrical and Electronics Engineers Inc., 2024) Harish, A.; Prakash, G.; Nair, R.R.; Iyer, V.B.; Mohan, B.R.; Das, M.
Understanding disk failures is crucial for both disk manufacturers and users, enabling the production of more dependable disk drives and the establishment of robust storage systems. Detecting disk failure has been found to be facilitated by the use of observable disk properties, especially those provided by the Self-Monitoring and Reporting Technology (SMART) system. In our paper, we leverage the capabilities of the SMART time series dataset to achieve an overall accuracy of 92% in disk failure detection. Â© 2024 IEEE.
HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction
(Institute of Electrical and Electronics Engineers Inc., 2025) Das, M.; Mohan, B.R.; Guddeti, R.M.R.
This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper designed two novel hybrid swarm-optimization algorithms (SOAs) referred to as gravitational force grasshopper optimization algorithm-artificial bee colony (GFGOA-ABC), and levy flight grasshopper optimization algorithm-artificial bee colony (LFGOA-ABC) algorithms. By combining the enhanced exploration features of LFGOA and GFGOA with the robust exploitation capacity of the artificial bee colony (ABC), the LFGOA-ABC and GFGOA-ABC algorithms are proposed. Prior to validating the HSoMLSDP framework, the LFGOA-ABC and GFGOA-ABC algorithm’s efficacy is first confirmed by experimenting on 19 benchmark functions (BFs) to assess their mean, standard deviation (SD) of optimal values, convergence rate, and convergence rate improvements. Following BFs verification, the second experiment tunes the hyperparameters of the ML models (artificial neural network, XGBOOST) to improve the defect accuracy of the SoMLDP model. The outcomes of the experiments justify a more rapid convergence rate for BFs and notable enhancements of 0.01-0.28 in software defect prediction (SDP) accuracy for NASA defect datasets when compared with state-of-the-art methods. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework. © 2013 IEEE.
HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Software Defect Prediction Framework
(IEEE Computer Society, 2025) Das, M.; Mohan, B.R.; Guddeti, R.M.R.
Defect prediction plays a crucial role for any software system across various domains, as its failure may cause unavoidable and undeniable scenarios. For reliable software, defect-free is considered as one of the most important criteria. This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper introduces a hybrid swarm optimization algorithm (SOA) referred to as the gravitational force LÃ©vy flight grasshopper optimization algorithm-artificial bee colony (GFLFGOA-ABC) algorithm. By combining the enhanced exploration feature of the gravitational force LÃ©vy flight grasshopper optimization algorithm (GFLFGOA) with the robust exploitation capacity of the artificial bee colony (ABC), the GFLFGOA-ABC algorithm is proposed. Prior to validating the HSoMLSDP framework, the LFGFGOA-ABC algorithm's performance is first confirmed by experiments on 6 benchmark functions (BFs) to assess its mean and convergence rate. Following BF verification, the second experiment tunes the hyperparameters of ML models (ANN, GB, XGB) to improve the defect accuracy of the SoMLDP model. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework. Â© 2025 IEEE.
Hybrid Bio-Optimized Algorithms for Hyperparameter Tuning in Machine Learning Models: A Software Defect Prediction Case Study
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Das, M.; Mohan, B.R.; Guddeti, R.M.R.; Prasad, N.
Addressing real-time optimization problems becomes increasingly challenging as their complexity continues to escalate over time. So bio-optimization algorithms (BoAs) come into the picture to solve such problems due to their global search capability, adaptability, versatility, parallelism, and robustness. This article aims to perform hyperparameter tuning of machine learning (ML) models by integrating them with BoAs. Aiming to maximize the accuracy of the hybrid bio-optimized defect prediction (HBoDP) model, this research paper develops four novel hybrid BoAs named the gravitational force Lévy flight grasshopper optimization algorithm (GFLFGOA), the gravitational force Lévy flight grasshopper optimization algorithm–sparrow search algorithm (GFLFGOA-SSA), the gravitational force grasshopper optimization algorithm–sparrow search algorithm (GFGOA-SSA), and the Lévy flight grasshopper optimization algorithm–sparrow search algorithm (LFGOA-SSA). These aforementioned algorithms are proposed by integrating the good exploration capacity of the SSA with the faster convergence of the LFGOA and GFGOA. The performances of the GFLFGOA, GFLFGOA-SSA, GFGOA-SSA, and LFGOA-SSA are verified by conducting two different experiments. Firstly, the experimentation was conducted on nine benchmark functions (BFs) to assess the mean, standard deviation (SD), and convergence rate. The second experiment focuses on boosting the accuracy of the HBoDP model through the fine-tuning of the hyperparameters in the artificial neural network (ANN) and XGBOOST (XGB) models. To justify the effectiveness and performance of these hybrid novel algorithms, we compared them with four base algorithms, namely the grasshopper optimization algorithm (GOA), the sparrow search algorithm (SSA), the gravitational force grasshopper optimization algorithm (GFGOA), and the Lévy flight grasshopper optimization algorithm (LFGOA). Our findings illuminate the effectiveness of this hybrid approach in enhancing the convergence rate and accuracy. The experimental results show a faster convergence rate for BFs and improvements in software defect prediction accuracy for the NASA defect datasets by comparing them with some baseline methods. © 2024 by the authors.
Hybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems
(Institute of Electrical and Electronics Engineers Inc., 2024) Goyal, G.; Sharma, K.; Anshuman; Mittal, V.; Singla, B.; Das, M.; Mohan, B.R.
Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. Â© 2024 IEEE.
Hybridization Approach to Optimize the Expected Loss of Demand in Drone Delivery Scheduling
(Institute of Electrical and Electronics Engineers Inc., 2024) Harsha, S.S.; Muddi, K.S.; Jindrali, S.S.; Reji, S.; Das, M.; Mohan, B.R.
This paper explores a hybrid-optimization approach for reducing the expected loss of delivery in drone delivery.This paper aims to give a deep knowledge about drone scheduling using machine learning and bio-optimized approaches. Using hybridization of K-Mean Clustering algorithms and Genetic algorithms, the paper makes a comparison between the performance of the above algorithm with the hybridization of hierarchical agglomerative clustering algorithms and ant colony optimization algorithms, resulting in valuable insights into drone delivery efficiency and reliability. Â© 2024 IEEE.