• Tree-Based Ensemble Regression Models for Emission Prediction of a Winter Green Oil-Hydrogen Dual-Fuel Engine with Zeolite After-Treatment 

  Js, Femilda Josephin; Subramanian, Balaji; Renjit, E. Jeslin; S, Naveen Venkatesh; Sugumaran, V; Subramanian, Thiyagarajan; Kiani, Farzad; Varuvel, Edwin Geo; Matijosius, Jonas; Kilikevicius, Arturas (Elsevier, 2025)

  This study presents an emission prediction framework for a dual-fuel compression-ignition engine operated on a 20 % winter green oil–diesel blend enriched with hydrogen and equipped with a zeolite-based after-treatment system. ...

• A Program-Output Estimator for Software Testing Using Program Analysis and Deep Learning Algorithms 

  Arasteh, Bahman; Sefati, Seyed Salar; Güneş, Peri; Hosseinzadeh, Vahid; Kiani, Farzad (Springer, 2025)

  Software testing is increasingly used as a software quality control method. During testing, the program under test’s output is compared with the expected correct output using test data. Estimating the program’s correct ...

• A Metaheuristic and Neural Network-Based Framework for Automated Software Test Oracles Under Limited Test Data Conditions 

  Arasteh, Bahman; Bulut, Faruk; İnce, İbrahim Furkan; Sefati, Seyed Salar; Kusetoğulları, Hüseyin; Kiani, Farzad (Springer, 2025)

  With the growing complexity of modern software systems, the demand for effective and efficient testing techniques has become an important aspect of the software development process. Software Test Oracles (STOs) play a ...

• Analyzing EEG Data During Opium Addiction Treatment Using a Fuzzy Logic-Based Machine Learning Model 

  DehAbadi, Elnaz; Anka, Fateme Ayşin; Vafaei, Fateme; Lanjanian, Hossein; Nematzadeh, Sajjad; Afshar, Mahsa Torkamanian; Aghahosseinzargar, Nazanin; Kiani, Farzad; Abharian, Peyman Hassani (Frontiers, 2025)

  Background: Reliable noninvasive tools for assessing substance abuse treatment and predicting outcomes remain a challenge. We believe EEG-derived complexity measures may have a direct link to clinical diagnosis. To this ...

• A Bedbug Optimization-Based Machine Learning Framework for Software Fault Prediction 

  Aresteh, Bahman; Sefati, Seyed Salar; Popovici, Eduard-Cristian; İnce, İbrahim Furkan; Kiani, Farzad (MDPI, 2025)

  Predicting software faults and identifying defective modules is a significant challenge in developing reliable software products. Machine Learning (ML) approaches on the historical fault datasets are utilized to classify ...

• Machine Learning Approaches for Predicting Diesel engine Emissions Using Waste tire Pyrolysis Oil – Hydrotreated Vegetable Oil Blends 

  Mickevicius, Tomas; Matijosius, Jonas; Varuvel, Edwin Geo; Js, Femilda Josephin; M, Jerome Stanley; Zvirblis, Tadas; Anka, Ferzat; Kilikevicius, Arturas (Elsevier, 2025)

  This experimental study explores the use of blended Tire Pyrolysis Oil (TPO) with Hydrotreated Vegetable Oil (HVO) as potential substitutes for diesel fuel in compression ignition engines. The assessment of the investigation on ...

• Hybrid Whale and Artificial Rabbit Optimization for Efficient Multi‑Objective Sensor Deployment in Complex IoT Networks 

  Kiani, Farzad (Springer Nature, 2025)

  This paper presents a novel hybrid metaheuristic algorithm, combining Whale Optimization Algorithm (WOA) and Artificial Rabbits Optimization (ARO), to solve the multi-objective sensor node placement problem in dynamic and ...

• Advanced Machine Learning Techniques for Predicting Wear Performance in Graphene Oxide Particulate Interpenetrating Polymer Network Composites 

  Russel, Eastus; Madhu, S.; S., Judy; Varuvel, Edwin Geo; Santhi, G.B.; Suresh, G.; J.S, Femilda Josephin; Albeshr, Mohammed F.; Kiani, Farzad (Elsevier, 2025)

  This research investigates the wear behavior of hybrid polymeric composites made from synthetic glass and natural cotton fibers, reinforced with varying proportions of Graphene Oxide (GO) (0 %, 1 %, 3 %, 5 %, 7 %, 9 %). ...

• Advances in Mountain Gazelle Optimizer: A Comprehensive Study on its Classification and Applications 

  Anka, Ferzat; Gharehchopogh, Farhad Soleimanian; Tejani, Ghanshyam G.; Mousavirad, Seyed Jalaleddin (Springer, 2025)

  The Mountain Gazelle Optimizer (MGO) is a newly emerging nature-inspired metaheuristic algorithm based on mountain gazelles' regionally and adaptively directed behavior. It is intended to solve complex optimization problems ...

• Generating Software Architectural Model from Source Code Using Module Clustering 

  Arasteh, Bahman; Sefati, Seyed Salar; Kusetoğulları, Hüseyin; Kiani, Farzad (MDPI, 2025)

  Software maintenance is one of the most expensive phases in software development, especially when complex source code is the only available artifact. Clustering software modules and generating a structured architectural ...

• Metaheuristics Role in Image Processing and Computer Vision Applications: A Comprehensive Review 

  Şahin, M. Faruk; Anka, Ferzat (Springer, 2025)

  Meta-Heuristic (MH) algorithms have gained prominence in computer vision and image processing due to their efficacy in solving complicated, high-dimensional optimization challenges. This review study thoroughly evaluates ...

• Latency-Aware and Energy-Efficient Task Offloading in IoT and Cloud Systems with DQN Learning 

  Benaboura, Amina; Bechar, Rachid; Kadri, Walid; Ho, Tu Dac; Pan, Zhenni; Sahmoud, Shaaban (MDPI, 2025)

  The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of ...

• Unified Deep Learning Method for Accurate Brain Tumor Segmentation Using Vertical Voxel Grouping andWavelet Features 

  Şahin, M. Faruk; Yeganli, S. Faegheh; Uludağ, Gönül; Yeganli, Faezeh; Anka, Ferzat (Springer, 2025)

  Brain tumor segmentation plays a vital role in medical imaging, enabling accurate diagnosis and guiding treatment decisions. Despite notable progress driven by deep neural networks (DNNs) and multi-parametricmagnetic ...

• A Multi-Objective Deep Reinforcement Learning Algorithm for Spatio-temporal Latency Optimization in Mobile LoT-enabled Edge Computing Networks 

  Khoshvaght, Parisa; Haider, Amir; Rahmani, Amir Masoud; Gharehchopogh, Farhad Soleimanian; Anka, Ferzat; Lansky, Jan; Hosseinzadeh, Mehdi (Elsevier, 2025)

  The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) ...

• A Multi‑Objective Metaheuristic Method for Node Placement in Dynamic LoT Environments 

  Kiani, Farzad (Springer, 2025)

  This study introduces an optimal Node Placement based on Enhanced Sand Cat Swarm Optimization (NP-ESCSO) algorithm, a novel metaheuristic approach for solving the node placement problem in dynamic IoT environments. By ...

• A multi-Strategy Chimp Optimization Algorithm for Solving Global and Constraint Engineering Problems 

  Anka, Ferzat (Springer, 2025)

  The chimp optimization algorithm (ChOA) is a recently introduced metaheuristic algorithm inspired by nature. This algorithm identified four types of chimpanzee groups: attacker, barrier, chaser, and driver, and proposed ...

• A Novel Hybrid Metaheuristic Method for Efficient Decentralized LoT Network Layouts 

  Anka, Ferzat (Elsevier, 2025)

  This paper introduces a Hybrid Genetic Particle Swarm Optimization (HGPSO) method focusing on optimal and efficient sensor deployment in Wireless Sensor Networks (WSNs) and Decentralized IoT (DIoT) networks. Effective ...

• DSPCI-MTL: Dynamic Split Point Computing in Multi-task Learning Implementation with Collaborative Intelligence 

  Şahin, M.Faruk; Yeganli, S. Faegheh; Kiani, Farzad (Elsevier, 2025)

  Deep Neural Networks (DNNs) have become a crucial technology in image processing, renowned for their ability to generate effective feature maps. The integration of DNNs within Internet of Things (IoT) environments, ...

• A Novel Two-Stage Fuzzy Classification Method with Different Weight Permutations for Optimal Gis-Based Placement of Wellness and Sports Centers 

  Razavian, Behnam; Seyedbeiglou, Seyed Masoud Hamed; Tirkolaee, Erfan Babaee; Anka, Ferzat (Elsevier, 2025)

  The optimal placement of wellness and sports centers is critical to maximizing their accessibility, effectiveness, and impact on public health. Strategic location planning ensures that these facilities are conveniently ...

• A Bioinspired Method for Optimal Task Scheduling in Fog-Cloud Environment 

  Anka, Ferzat; Tejani, Ghanshyam G.; Sharma, Sunil Kumar; Baljon, Mohammed (Tech Science Press, 2025)

  Due to the intense data flow in expanding Internet ofThings (IoT) applications, a heavy processing cost and workload on the fog-cloud side become inevitable. One of the most critical challenges is optimal task scheduling. Since ...

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