Hao-Ting Lin | Feature-Driven Optimization | Best Researcher Award

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Assoc Prof. Dr. Hao-Ting Lin | Feature-Driven Optimization | Best Researcher Award

Associate Professor | National Chung Hsing University | Taiwan

Dr. Hao-Ting Lin is an Associate Professor at National Chung Hsing University specializing in robotics, control systems, and bio-industrial mechatronics engineering, with a strong focus on automation, intelligent systems, and sustainable agricultural technologies. He holds advanced degrees in mechanical and control engineering and has developed expertise in the design and integration of robotic mechanisms, sensor-based control, and pneumatic power systems for poultry and agricultural applications. His professional experience includes leading over 30 completed and several ongoing research and consultancy projects, authoring 20 peer-reviewed journal papers, and publishing a book chapter on smart agricultural technologies. Dr. Lin’s research focuses on IoT-enabled humane poultry slaughtering systems, deep learning-based chicken image recognition, precision pneumatic seeders, energy-efficient control strategies for wastewater and agricultural machinery, and robotic sorting systems. He has been granted 12 patents covering innovations in pneumatic automation, exoskeleton systems, aquaculture water quality management, and intelligent sewing and seeding devices. Dr. Lin’s contributions integrate control theory, mechatronics, artificial intelligence, and real-time systems to advance productivity, animal welfare, and sustainability in agriculture and industry. His scholarly impact is reflected in 170 citations by 161 documents, 20 published documents, and an h-index of 6.

Profile: Scopus | ORCID

Featured Publications

1. Lin H.T.*, Suhendra, Development and Implementation of an IoT-Enabled Smart Poultry Slaughtering System Using Dynamic Object Tracking and Recognition. Sensors, 2025, 25(16), 5028.

2. Chen H.C., Rohman Y.F., Ashlah M.B., Lin H.T., Sean W.Y.*, Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor. Energies, 2024, 17(15), 3647.

3. Lin H.T.*, Lee Y.H., Implementing a Precision Pneumatic Plug Tray Seeder with High Seeding Rates for Brassicaceae Seeds via Real-Time Trajectory Tracking Control. Actuators, 2023, 12(9), 340.

4. Liu H.W., Chen C.H., Tsai Y.C., Hsieh K.W., Lin H.T.*, Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm. Sensors, 2021, 21(11), 3579.

5. Lin H.T.*, A Novel Real-Time Path Servo Control of a Hardware-in-the-Loop for a Large-Stroke Asymmetric Rod-Less Pneumatic System under Variable Loads. Sensors, 2017, 17(6), 1283.

 

Mohammad Amin | Gradient boosting | Young Scientist Award

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Mr. Mohammad Amin | Gradient boosting | Young Scientist Award

Mohammad Amin | RWTH Aachen University | Germany

Mr. Mohammad Yazdi is a distinguished researcher and academic with expertise in information technology, e-learning systems, and research data management. He holds advanced degrees in information systems and has built a robust academic and professional profile through his work in integrating IT solutions for collaborative research, developing interactive e-learning media, and enabling process mining for research data life cycles. Professionally, he has contributed to several high-impact projects, including the implementation of web services for integrated government systems, evaluation of FAIR data management architectures, and the orchestration of research cluster collaborations, demonstrating strong technical and leadership skills. His research focuses on e-learning as an interactive IT-based learning medium, process mining, IT resource management in research projects, and operational support systems, with a publication record spanning reputable conferences and journals. His works, including “E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi,” “How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment,” and “Event Log Abstraction in Client-Server Applications,” have collectively garnered significant citations, underscoring their academic impact. He has been recognized for his scholarly contributions and actively participates in academic dissemination through editorial roles and conference presentations. With a commitment to advancing digital transformation in education and research, Mohammad Yazdi stands out as a thought leader and innovator in his field, with 29 citations across 23 documents, 10 publications, and an h-index of 4

Profile: Google Scholar | Scopus | ORCID

Featured Publications

1. M. Yazdi*, E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi. Foristek, 2012, 2(1), 680.

2. M. Yazdi*, Implementasi Web-Service pada Sistem Pelayanan Perijinan Terpadu Satu Atap di Pemerintah Kota Palu. Seminar Nasional Teknologi Informasi & Komunikasi Terapan, 2012, 450–457, 24.

3. M. Politze, F. Claus, B. Brenger, M.A. Yazdi*, B. Heinrichs, A. Schwarz, How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment. 2020, 22.

4. M.A. Yazdi*, P. Farhadi Ghalati, B. Heinrichs, Event Log Abstraction in Client-Server Applications. 13th Int. Conf. Knowledge Discovery and Information Systems, 2021, 13.

5. M.A. Yazdi*, Enabling Operational Support in the Research Data Life Cycle. Proc. 1st Int. Conf. Process Mining – Doctoral Consortium, 2019, 13.

Sheharyar Hussain | PV-EV charging station | Best Researcher Award

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Mr. Sheharyar Hussain | PV-EV charging station | Best Researcher Award

Mentor | Tianjin University Renai College | China

Mr. Sheharyar Hussain is an accomplished Electrical Engineer, Lecturer, and Industry Mentor at the Green Power Research Institute, Tianjin Renai College, specializing in power electronics, control and automation, and robotics. He holds dual master’s degrees with a strong focus on smart grids, renewable energy, and artificial intelligence applications in power systems, and is an active IEEE member. His professional experience spans academia and industry, where he has led research initiatives on microgrids, advanced control systems, UAVs, and sustainable energy technologies, as well as guided students and industry professionals in innovative projects. His research contributions include significant publications in top-tier journals such as IEEE Transactions on Industry Applications, Journal of Modern Power Systems and Clean Energy, and IET Generation, Transmission & Distribution, focusing on energy management strategies, distribution system optimization, EV charging solutions, and predictive control for photovoltaic systems. In addition to authoring 13 SCI/Scopus-indexed journal papers, he has three patents published or under process and has collaborated extensively on smart grid and clean energy projects. Recognized for his innovative approach to energy systems, he holds professional memberships and actively engages in fostering academic-industry collaborations. His work reflects a commitment to advancing sustainable, intelligent energy solutions and mentoring the next generation of engineers. He has received 255 citations by 223 documents, authored 14 documents, and holds an h-index of 6.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

1. Meng Q.*, Hussain S., Luo F., Wang Z., Jin X., An online reinforcement learning-based energy management strategy for microgrids with centralized control. IEEE Trans. Ind. Appl., 2024, 99.

2. Meng Q.*, Jin X., Luo F., Wang Z., Hussain S., Distributionally robust scheduling for benefit allocation in regional integrated energy system with multiple stakeholders. J. Mod. Power Syst. Clean Energy, 2024, 12(5), 1631-1642.

3. Meng Q.*, Tong X., Hussain S., Luo F., Zhou F., Liu L., He Y., Jin X., Li B., Revolutionizing photovoltaic consumption and electric vehicle charging: A novel approach for residential distribution systems. IET Gener. Transm. Distrib., 2024, 18(17), 2822-2833.

4. Meng B.*, Tong X., Hussain S., Luo F., Zhou F., He Y., Liu L., Sun B., Enhancing distribution system stability and efficiency through multi-power supply startup optimization for new energy integration. IET Gener. Transm. Distrib., 2024, 1-14.

5. Saeed M.A.*, Ahmed Z., Hussain S., Zhang W., Wind resource assessment and economic analysis for wind energy development in Pakistan. Sustain. Energy Technol. Assess., 2021, 44, 101068.

Munire Sibel Cetin | Wearable Electronics | Best Researcher Award

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Dr. Munire Sibel Cetin | Wearable Electronics | Best Researcher Award

Postdoctoral researcher | Istanbul Technical University | Turkey

Dr. Munire Sibel Cetin is a Postdoctoral Researcher at Istanbul Technical University, specializing in textile engineering, soft robotics, and smart textiles. She holds a BSc, MSc, and PhD in Textile Engineering, with research expertise spanning ergonomic design, seating comfort, and mechanical-electronic systems of CNC cutters. Her current work focuses on the development of flexible conductive yarns, textile-based thermal actuators, and integrated sensor systems for soft robotic applications, demonstrating significant advances in wearable robotics through energy-efficient textile exoskeletons. Dr. Çetin has contributed to 11 peer-reviewed journal publications, including pioneering work on thermally driven 3D seamless textile actuators and highly stretchable knitted interdigital sensors for wearable technology. She has completed multiple national and international projects, including collaborations under HORIZON 2020, supervised interdisciplinary teams, and contributed to technology transfer through patents—one internationally published and another under process. She serves as a referee for seven prominent journals, reflecting her active engagement in scholarly review processes. Her research has been cited 39 times, and she has earned professional recognition for advancing textile-integrated robotic systems and ergonomic textile design. She holds memberships in professional associations and actively contributes to collaborative innovation initiatives bridging academia and industry. She has 7 citations by 6 documents, 5 documents indexed, and an h-index of 2.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

1. Atalay O.*, Ozlem K., Gumus C., Ahmed I.A.K., Yilmaz A.F., Celebi M.F., et al. Thermally driven 3D seamless textile actuators for soft robotic applications. Adv. Intell. Syst., 2024, 6(11), 2400133.

2. Yilmaz A.F.*, Ahmed I.A.K., Gumus C., Ozlem K., Cetin M.S., Atalay A.T., Ince G., et al. Highly stretchable textile knitted interdigital sensor for wearable technology applications. Adv. Sensor Res., 2024, 3(2), 2300121.

3. Cetin M.S.*, Erdem D. Determination of relationships between effective criteria in conductive yarn purchase using the DEMATEL method. Eur. J. Sci. Technol., 2019, 152–160.

4. Taherkhani B.*, Celebi M.F., Cetin M.S., Atalay A.T., Ince G., Atalay O. Thermally powered soft gripper covered with silver-coated nylon fabric heater reinforced with stainless steel yarn. Adv. Eng. Mater., 2024, 3, 2400133.

5. Yilmaz A.F.*, Ahmed I.A.K., Gumus C., Ozlem K., Cetin M.S., Atalay A.T., Ince G., et al. Highly stretchable textile knitted interdigital sensor for wearable technology applications (Adv. Sensor Res. 2/2024). Adv. Sensor Res., 2024, 3(2), 202470009.

Klara Reichard | Computer Vision Systems | Best Researcher Award

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Mrs. Klara Reichard | Computer Vision Systems | Best Researcher Award

Klara Reichard | Technical University of Munich | Germany

Mrs. Klara Reichard is a PhD candidate at the Technical University of Munich (TUM) and a member of the BMW Doctoral Program, specializing in computer vision, autonomous driving, and vision-language integration. She holds advanced degrees in computation and information sciences and works at the intersection of academia and industry to bridge theoretical research with real-world applications. Her professional experience includes collaborations with BMW Group and the University of Padova, where she has contributed to projects on automatic parking space detection, vocabulary-free semantic segmentation, and language-guided anomaly detection for open-world perception. Klara’s research focuses on developing robust perception systems that enhance the safety and intelligence of next-generation autonomous vehicles, with significant contributions such as novel methods for open-vocabulary and vocabulary-free semantic segmentation and integration into autonomous driving systems. She has authored multiple publications, including contributions to the Journal of Experimental Algorithmics and arXiv preprints, with her work accumulating over 24 citations. Klara holds one patent in progress for open-world segmentation and actively contributes to interdisciplinary research communities. She has been recognized for her innovative approach to bridging cutting-edge computer vision research with deployable industry solutions, demonstrating leadership in advancing intelligent, safe, and scalable autonomous vehicle technologies. Quotes: 25, h-index: 2, i10-index: 2

Profile: Google Scholar

Featured Publications

1. Radermacher M., Reichard K.*, Rutter I., Wagner D., A geometric heuristic for rectilinear crossing minimization. Proc. 20th Workshop on Algorithm Engineering and Experiments, 2018, 12.

2. Radermacher M., Reichard K.*, Rutter I., Wagner D., Geometric heuristics for rectilinear crossing minimization. J. Exp. Algorithmics, 2019, 24, 1–21.

3. Reichard K.*, Rizzoli G., Gasperini S., Hoyer L., Zanuttigh P., Navab N., From open-vocabulary to vocabulary-free semantic segmentation. arXiv preprint arXiv:2502.11891, 2025, 1.

4. Postels J., Strümpler Y., Reichard K.*, Van Gool L., Tombari F., 3D compression using neural fields. arXiv preprint arXiv:2311.13009, 2023, 1.

5. Reichard K.*, Rectilinear Crossing Minimization. Informatics Institute, 2016.

Michal Novotny | HealthTech and Wearables | Best Researcher Award

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Dr. Michal Novotny | HealthTech and Wearables | Best Researcher Award

Researcher | Czech Technical University in Prague | Czech Republic

Dr. Michal Novotný is a distinguished researcher at the Faculty of Electrical Engineering, Czech Technical University in Prague, specializing in biomedical signal processing, speech analysis, and neurodegenerative disease diagnostics. He holds a Ph.D. in Electrical Engineering Theory from the same university, where his doctoral work focused on automated assessment of diadochokinesis and resonance in dysarthrias associated with basal ganglia dysfunction. He also earned an Engineer’s degree in Biomedical Engineering and a Bachelor’s degree in Electronics and Multimedia, complemented by an Erasmus internship at the University of Glasgow. Dr. Novotný has served as a researcher at the Czech Academy of Sciences and currently leads and contributes to several nationally and internationally funded projects, including principal investigator roles for studies on hypomimia and speech biomarkers in Parkinson’s disease. His research focuses on objective and automated acoustic analysis of speech disorders in Parkinson’s disease, REM sleep behavior disorder, multiple sclerosis, and other neurological conditions, resulting in a robust portfolio of high-impact publications in journals such as IEEE/ACM Transactions on Audio, Speech, and Language Processing, Annals of Neurology, and npj Parkinson’s Disease. He is a recognized academic leader, teaching courses on bioengineering, signal processing, and multimedia synthesis, and supervising doctoral and master’s students. Dr. Novotný has delivered keynote lectures at international conferences, completed over 85 journal reviews, and is widely cited in the field. 974 Citations by 667 documents, 36 Documents, h-index 17

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

1. Novotny M.*, Rusz J., Čmejla R., Růžička E., Automatic evaluation of articulatory disorders in Parkinson’s disease. IEEE/ACM Trans. Audio Speech Lang. Process., 2014, 22(9), 1366–1378.

2. Rusz J., Bonnet C., Klempíř J., Tykalová T., Baborová E., Novotný M.*, et al., Speech disorders reflect differing pathophysiology in Parkinson’s disease, progressive supranuclear palsy and multiple system atrophy. J. Neurol., 2015, 262(4), 992–1001.

3. Rusz J., Hlavnička J., Tykalová T., Novotný M.*, Dušek P., Šonka K., Růžička E., Smartphone allows capture of speech abnormalities associated with high risk of developing Parkinson’s disease. IEEE Trans. Neural Syst. Rehabil. Eng., 2018, 26(8), 1495–1507.

4. Rusz J., Hlavnička J., Novotný M.*, Tykalová T., Pelletier A., Montplaisir J., et al., Speech biomarkers in rapid eye movement sleep behavior disorder and Parkinson disease. Ann. Neurol., 2021, 90(1), 62–75.

5. Rusz J., Benova B., Ruzickova H., Novotný M.*, Tykalová T., Hlavnicka J., et al., Characteristics of motor speech phenotypes in multiple sclerosis. Mult. Scler. Relat. Disord., 2018, 19, 62–69.

Israa Seliem | Bioorganic chemistry | Best Researcher Award

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Assist Prof. Dr. Israa Seliem | Bioorganic chemistry | Best Researcher Award

Assistant professor | Zagazig University | Egypt

Dr. Israa A. Seliem is a Lecturer in Pharmaceutical Organic Chemistry at the Faculty of Pharmacy, Zagazig University, with expertise in drug design, medicinal chemistry, and computational modeling. She earned her Ph.D. and M.S. in Pharmaceutical Organic Chemistry from Zagazig University and completed a research fellowship at Augusta State University, USA. Her professional contributions include teaching and supervising graduate and undergraduate research in organic and medicinal chemistry, designing curricula, and collaborating with colleagues to advance research in drug discovery. Dr. Seliem’s research focuses on the design, eco-friendly synthesis, and computational evaluation of novel therapeutic scaffolds, with significant work on anticancer, anticholinesterase, α-glucosidase, and anti-MRSA agents. She has an extensive publication record in leading journals such as RSC Advances, ChemMedChem, European Journal of Medicinal Chemistry, Bioorganic Chemistry, and Chemical Biology & Drug Design, covering areas like molecular docking, ADMET profiling, DFT studies, and drug-target interactions against EGFR, VEGFR-2, β-tubulin, and cytokines. Her work has also contributed to the development of antiviral agents targeting SARS-CoV-2 and antimycobacterial compounds. Dr. Seliem holds certifications including TEPH, IELTS, TOEFL, ICDL, and training in quality assurance, clinical pharmacology, and pharmaceutical sales. She is an active member of the scientific community through peer-reviewed publications, collaborations, and participation in research dissemination activities. Her consistent scholarly output and commitment to advancing pharmaceutical sciences make her a strong candidate for recognition through research excellence awards. She has 151 citations from 136 documents across 10 publications, with an h-index of 6.

Profile: Scopus | ORCID

Featured Publications

1. Seliem I.A.*, Design, eco-friendly synthesis, and molecular docking studies of isatin hybrids as promising therapeutic agents (anticancer, anticholinesterase inhibitor, α-glucosidase inhibitor, and anti-MRSA). RSC Adv., 2025.

2. Seliem I.A.*, Synthesis, cytotoxic activity, molecular docking, molecular dynamics simulations, and ADMET studies of novel spiropyrazoline oxindoles based on domino Knoevenagel–Michael cyclization as potent non-toxic anticancer agents targeting β-tubulin and EGFR, with anti-MRSA activity. RSC Adv., 2025.

3. Lotfy E.M., Bokhtia R.M., Abdel Fattah H.A., Seliem I.A.*, Microwave-assisted one-pot green synthesis, ADMET profiling, DFT, and molecular docking of novel 3-cyano-2-pyridone derivatives as dual EGFR and cytokine (TNF-α, IL-6) inhibitors. Eur. J. Med. Chem., 2025.

4. Seliem I.A.*, DMF-DMA catalyzed synthesis, molecular docking, in-vitro, in-silico design, and binding free energy studies of novel thiohydantoin derivatives as antioxidant and antiproliferative agents targeting EGFR tyrosine kinase and aromatase cytochrome P450 enzyme. Bioorg. Chem., 2024.

5. Seliem I.A., Panda S.S., Girgis A.S., Tran Q.L., Said M.F., Bekheit M.S., Abdelnaser A., Nasr S., Fayad W., Soliman A.A.F. et al., Development of isatin-based Schiff bases targeting VEGFR-2 inhibition: Synthesis, characterization, antiproliferative properties, and QSAR studies. ChemMedChem, 2022.

Muhammad Bilal | Statistics | Best Researcher Award

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Muhammad Bilal | Statistics | Best Researcher Award

Lecturer | Balochistan University of Information Technology | Pakistan

Muhammad Bilal is a Lecturer in Statistics at the Department of Mathematical Sciences, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), specializing in statistical modeling, time series analysis, and fuzzy systems. He earned his Ph.D. in Statistics from Abdul Wali Khan University, Mardan, following an M.Phil. and M.Sc. in Statistics from Quaid-i-Azam University, Islamabad. Professionally, he has served as Lecturer at BUITEMS, Kohat University of Science and Technology, and the Military College of Engineering (NUST), where he has contributed to curriculum design, research supervision, and academic development programs. His research focuses on advanced forecasting methods, volatility modeling, and applications of fuzzy time series in financial markets, commodity price prediction, and macroeconomic analysis. He has published impactful articles in reputed journals such as Heliyon, Scientific Reports, Journal of Big Data, and Frontiers in Energy Research, with notable works addressing crude oil market dynamics during geopolitical conflicts, rice price forecasting using multivariate fuzzy models, and volatility prediction in forex markets using hybrid machine learning frameworks. Dr. Bilal has participated in several national and international conferences and workshops, including training programs on SPSS, STATA, and university teaching methodologies, enhancing his expertise in statistical education and applied research. He is actively involved in collaborative projects with multidisciplinary teams, advancing data-driven solutions for economic and agricultural challenges. Recognized for his contributions to statistical research and education, he is a member of professional networks and has served as a peer reviewer for academic journals, contributing to knowledge dissemination and scholarly quality.

Profile: Google Scholar | ORCID

Featured Publications

Bilal M.*, Aamir M., Abdullah S., Khan F., Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models. Heliyon, 2024, 10(1), 15.

Dar L.S., Aamir M., Khan Z., Bilal M., Boonsatit N., Jirawattanapanit A., Forecasting crude oil prices volatility by reconstructing EEMD components using ARIMA and FFNN models. Front. Energy Res., 2022, 10, 991602, 8.

Omer S.O., Aamir M., Bilal M., Ullah K., Qadir A., Study of second-order Hankel determinant for starlike functions with respect to symmetric points. VFAST Trans. Math., 2023, 11(1), 52-66, 4.

Bilal M.*, Alrasheedi M.A., Aamir M., Abdullah S., Norrulashikin S.M., Rezaiy R., Enhanced forecasting of rice price and production in Malaysia using novel multivariate fuzzy time series models. Sci. Rep., 2024, 14(1), 1-17, 2.

Aamir M., Khan N., Naeem M., Bilal M., Khan F., Abdullah S., Implications of the COVID-19 pandemic on the Shanghai, New York, and Pakistan stock exchanges. Heliyon, 2023, 9(7), 2.

Md. Shakil Hossain | Natural language processing | Excellence in Research Award

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Md. Shakil Hossain | Natural language processing | Excellence in Research Award

Research Assistant | AMIR Lab | Bangladesh

Md. Shakil Hossain is a Research Assistant at AMIR Lab with expertise in artificial intelligence, humanoid robotics, and data-driven solutions. He earned a B.Sc. in Computer Science from Bangladesh University of Business and Technology, where he specialized in artificial intelligence, machine learning, neural networks, IoT, and data science. Professionally, he has served as an Assistant Robotics Engineer at Robo Tech Valley, where he led the development of educational and multipurpose humanoid robots, and as an AI Data Trainer at Invisible Technologies, contributing to high-quality datasets for machine learning systems. His current research focuses on natural language processing, hybrid deep learning models, multimodal AI, and large language model applications, with several high-impact publications in Scientific Reports, IEEE Access, Knowledge-Based Systems, and arXiv. His notable works include the Multi-task Opinion-Enhanced Hybrid BERT model for mental health analysis, hybrid transformer-based models for Arabic text classification, and novel graph-based approaches for aspect-based sentiment analysis. He has also contributed to IoT-based agricultural solutions and real-time AI model deployment. Recognized for his excellence, he has led champion teams in multiple hackathons, including the BCS ICT Fest and Cisco IoT Hackathon, and received a Research & Development Grant from BUBT for his IoT-based Smart Agro-Monitor project. He holds multiple global certifications in data analytics, computer vision, and responsible AI, and has actively organized robotics Olympiads. Md. Shakil Hossain’s combined technical expertise, impactful research, and leadership in innovation make him a strong candidate for this award.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

MM Hossain*, MS Hossain, MF Mridha, M Safran, S Alfarhood, Multi-task opinion-enhanced hybrid BERT model for mental health analysis. Sci. Rep., 2025, 15(1), 3332.

MM Hossain*, MS Hossain, M Safran, S Alfarhood, M Alfarhood, A hybrid attention-based transformer model for Arabic news classification using text embedding and deep learning. IEEE Access, 2024.

MM Hossain*, MS Hossain, MS Hossain, MF Mridha, M Safran, TransNet: deep attentional hybrid transformer for Arabic posts classification. IEEE Access, 2024.

MM Hossain*, MS Hossain, S Chaki, MR Hossain, MS Rahman, ABM Ali, CrosGrpsABS: Cross-attention over syntactic and semantic graphs for aspect-based sentiment analysis in a low-resource language. arXiv Preprint, 2025, arXiv:2505.19018.

MS Hossain*, MM Hossain, MS Hossain, MF Mridha, M Safran, EmoNet: Deep attentional recurrent CNN for X (formerly Twitter) emotion classification. IEEE Access, 2025.

Jiahao Luo | Smart Agriculture | Best Researcher Award

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Jiahao Luo | Smart Agriculture | Best Researcher Award

Graduate Student | Xihua University | China

Mr. Jiahao Luo is a graduate student at Xihua University specializing in multi-machine collaborative scheduling and path planning for intelligent agricultural mechanization. He holds a strong academic background in optimization algorithms and their applications to complex agricultural systems, focusing on methodological innovation and practical implementation. His professional experience includes leading research on traversal path planning and collaborative scheduling for corn harvesting and transportation in challenging hilly terrains, integrating Dijkstra’s algorithm with improved Harris Hawk Optimization to enhance efficiency and safety. Jiahao has published six SCI-indexed papers, including one in a Q1 journal, three in Q2, and two in Q3, showcasing a consistent record of impactful contributions to high-quality research. His work advances hybrid algorithms that combine evolutionary computation with local search, addressing real-world challenges such as terrain complexity, dynamic obstacles, and operational coordination, ultimately improving mechanization in agriculture. In addition to his research output, Jiahao has contributed to three consultancy or industry projects and holds three patents under process, reflecting the translational value of his work. His efforts significantly bridge the gap between theory and application, supporting sustainable, technology-driven farming practices with both academic and industrial relevance.

Profile: Scopus 

Featured Publication

Luo J.*, Intelligent Path Tracking for Single-Track Agricultural Machinery Based on Variable Universe Fuzzy Control and PSO-SVR Steering Compensation. Agriculture Switzerland, 2025.