Manisha Kasar | Artificial Intelligence | Best Researcher Award

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Dr. Manisha Kasar | Artificial Intelligence | Best Researcher Award

Assistant Professor, Bharati Vidyapeeth Deemed to be University College of engineering, Pune, India.

Dr. Manisha M. Kasar is an accomplished researcher and educator in the field of computer engineering, with over 11 years of experience. Her expertise spans facial recognition systems, artificial intelligence, and machine learning. She currently serves as an Assistant Professor at Bharti Vidyapeeth College of Engineering, Pune. Dr. Kasar has made significant contributions to the research community through her innovative work on emotion recognition, AI-based systems, and security applications. She is also the holder of several patents and has published numerous papers in prestigious journals.

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Education 🎓

Dr. Kasar holds a Ph.D. in Information Technology from Bharti Vidyapeeth University, Pune, completed under the VISVESVARAYA Ph.D. Scheme in 2021. She also earned her M.Tech in Computer Engineering from NMIMS in 2014, and a B.E. in Computer Engineering from NMU in 2009. Her strong academic foundation has been pivotal in her research achievements.

Experience đź’Ľ

With over 11 years of experience, Dr. Kasar is currently an Assistant Professor at Bharti Vidyapeeth College of Engineering, Pune. She has previously worked at Vishwakarma Institute of Information Technology and as a visiting faculty member at Bharti Vidyapeeth. Her teaching and administrative skills have been recognized through her roles in various academic institutions, and she has contributed to mentoring and guiding students in advanced technology research.

Research Interest 🔍

Dr. Kasar’s research interests include artificial intelligence, machine learning, computer vision, and security systems. Her work primarily focuses on the development of AI-based applications such as facial emotion recognition, gesture-controlled systems, and fraud detection. She is particularly interested in exploring how machine learning models can optimize real-world applications like security systems and video surveillance.

Awards & Patents 🏆

Dr. Kasar is the holder of two significant patents:

Smart Mirror System with Infrared Blaster.

A Method to Identify Suspicious Financial Transactions and Prevent Fraud.

Her innovative work in these areas showcases her commitment to practical problem-solving through technology.

Publications  📚

Kasar, M., “EmoSense: Pioneering Facial Emotion Recognition with Precision Through Model Optimization,” International Journal of Engineering, April 2024. Cited by 1 article. link

Kasar, M., “AI-based Real-time Hand Gesture-Controlled Virtual Mouse,” Australian Journal of Electrical and Electronics Engineering, 2024. Cited by 0 articles. link

Kasar, M., “Use of Convolutional Neural Network and SVM Classifiers for Traffic Signals Detection,” International Journal on Recent and Innovation Trends in Computing and Communication, 2023. Cited by 3 articles. link

 

 

 

 

 

 

Taye Mengistu | Machine learning | Best Researcher Award

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Mr. Taye Mengistu | Machine learning | Best Researcher Award

IT engineer, Jigjiga University, Ethiopia

Mr. Taye Mengistu is an innovative researcher and lecturer based at Jigjiga University, Ethiopia. With a strong foundation in computer science and information technology, he is making notable strides in applying machine learning to real-world challenges. His pioneering work includes utilizing ensemble convolutional neural networks (CNNs) for the classification of mango diseases, a significant advancement in agricultural technology aimed at enhancing crop health and productivity.

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Education 🎓

Mr. Taye Mengistu holds a Master’s degree in Computer Science from Jimma University, Ethiopia, where he graduated in January 2022 with a GPA of 3.58. Prior to this, he completed his Bachelor’s degree in Information Technology at Jigjiga University, Ethiopia, graduating in July 2017 with a GPA of 3.84. His strong academic background underscores his deep understanding and expertise in his field.

Experience 🏢

Mr. Taye Mengistu has been serving as a Lecturer at Jigjiga University, Ethiopia, since October 2017. With over five years of experience, he has played a crucial role in both teaching and research. His responsibilities include delivering lectures, supervising student projects, and conducting research in various domains. His tenure at the university reflects his dedication to academic excellence and his ongoing commitment to advancing knowledge in his field.

Research Interests 🔬

Classification of Mango Disease Using Ensemble Convolutional Neural Network

The classification of mango diseases using ensemble convolutional neural networks (CNNs) represents a cutting-edge research area at the intersection of agriculture and artificial intelligence. This research focuses on leveraging advanced machine learning techniques to accurately identify and categorize diseases affecting mango crops, which is crucial for improving agricultural productivity and sustainability.

Key Aspects of Research Interests:

Ensemble Convolutional Neural Networks (CNNs): Utilizing ensemble methods to combine multiple CNN models to enhance classification accuracy. This approach improves the robustness and reliability of disease detection systems by aggregating predictions from different models.

Disease Classification: Developing algorithms to classify various mango diseases based on visual symptoms captured in images. Accurate classification helps in timely diagnosis and effective management strategies, minimizing crop loss and ensuring better yield.

Image Processing and Analysis: Applying image processing techniques to preprocess and analyze mango leaf and fruit images. This includes noise reduction, feature extraction, and image augmentation to improve model performance.

Machine Learning in Agriculture: Exploring the application of machine learning models to agricultural problems, particularly in disease detection and management. This research aims to bridge the gap between AI technology and practical agricultural needs.

Sustainable Agriculture: Enhancing disease management practices to promote sustainable agriculture. By accurately classifying and managing diseases, farmers can reduce the reliance on chemical treatments, leading to more eco-friendly farming practices.

Awards 🏆

Certification in HDP, Jigjiga University (May 2022) – Recognized for his advanced skills and knowledge in his field.

Publications đź“š

Classification of mango disease using ensemble convolutional neural network– JSmart Agricultural Technology 2024. link

 

 

 

 

 

Aleka Melese | AI | Best Researcher Award

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🌟Mr. Aleka Melese, AI, Best Researcher Award🏆

Aleka Melese at Ayalew University of Gondar, Ethiopia

Aleka Melese Ayalew is an Ethiopian male with a Master’s degree in Information Technology from the University of Gondar. He is currently employed as an Information Technology Lecturer and Researcher at the same university. With a strong academic background and expertise in artificial intelligence, machine learning, and deep learning, Aleka has made significant contributions to the field through his research and publications. He is proficient in both English and his mother tongue, Amharic.

Author Metrics:

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Aleka Melese Ayalew has a prolific publication record, with over 12 articles published in reputable journals. His research spans various domains such as COVID-19 detection, disease classification, sentiment analysis, and more. He has also served as a peer reviewer for multiple journals, contributing to the academic community by critically evaluating research and providing constructive feedback.

Citations: Aleka’s publications have been cited a total of 119 times since 2019, indicating the influence and relevance of his research within the academic community.

h-index: With an h-index of 6 since 2019, Aleka has published at least 6 papers that have each been cited at least 6 times, demonstrating a consistent level of impact in his field.

i10-index: Aleka’s i10-index, which measures the number of publications with at least 10 citations, is 3 since 2019, indicating the presence of 3 papers with 10 or more citations during this period. This metric further underscores the significance of his research output.

Education:

Aleka Melese Ayalew holds a Bachelor of Science degree in Information Technology from Adigrat University, earned between 2014 and 2017. He further pursued his academic journey by obtaining a Master’s degree in Information Technology from the University of Gondar, completing it in 2021.

Research Focus:

Aleka’s research focuses primarily on artificial intelligence, machine learning, and deep learning, with applications in various domains such as healthcare (COVID-19 detection, disease classification), sentiment analysis, and IoT.

Professional Journey:

Aleka Melese Ayalew’s professional journey began in 2018 when he joined the University of Gondar as an Information Technology Lecturer and Researcher. Since then, he has been actively involved in course design, classroom instruction, research, article publication, mentoring, advising, assessment, and evaluation.

Honors & Awards:

Throughout his academic and professional career, Aleka has been recognized for his outstanding contributions. He has received awards for outstanding contributions during the Trachoma Control Program Impact Assessment and for good academic performance during his time at Adigrat University.

Publications Noted & Contributions:

Aleka has contributed significantly to the academic community through his publications. Notable contributions include research on COVID-19 detection, disease classification, sentiment analysis, and IoT applications. He has published over 12 articles in reputable journals and has also served as a peer reviewer for multiple journals.

Atelectasis detection in chest X-ray images using convolutional neural networks and transfer learning with anisotropic diffusion filter

Journal: Informatics in Medicine Unlocked

Year: 2024

DOI: 10.1016/J.IMU.2024.101448

Contributors: Aleka Melese Ayalew, Yohannes Agegnehu Bezabih, Biniyam Mulugeta Abuhayi, Asemrie Yemata Ayalew

Classification of pumpkin disease by using a hybrid approach

Journal: Smart Agricultural Technology

Year: 2024

DOI: 10.1016/J.ATECH.2024.100398

Contributors: Yohannes Agegnehu Bezabh, Biniyam Mulugeta Abuhayi, Aleka Melese Ayalew, Asegie, Habtamu Ayenew

Lumbar Disease Classification Using an Involutional Neural Based VGG Nets (INVGG)

Journal: IEEE Access

Year: 2024

DOI: 10.1109/ACCESS.2024.3367774

Contributors: Biniyam Mulugeta Abuhayi, Yohannes Agegnehu Bezabh, Aleka Melese Ayalew

X-ray image-based pneumonia detection and classification using deep learning

Journal: Multimedia Tools and Applications

Year: 2024

DOI: 10.1007/S11042-023-17965-4

Contributors: Asnake, Nigus Wereta; Salau, Ayodeji Olalekan; Aleka Melese Ayalew

Classification of Mango Disease Using Ensemble Convolutional Neural Network

Journal: Smart Agricultural Technology

Year: 2024 (May)

DOI: 10.1016/j.atech.2024.100476

Contributors: Yohannes Agegnehu Bezabh, Aleka Melese Ayalew, Biniyam Mulugeta Abuhayi, Tensay Nigussie Demlie, Eshete Ayenew Awoke, Taye Endeshaw Mengistu

Research Timeline:

Aleka Melese Ayalew’s research timeline reflects a continuous dedication to academic and professional growth. Starting from his undergraduate years in 2014, he progressed to obtain his Master’s degree in 2021. Throughout this journey, he has actively engaged in research, publication, and peer review activities.

Collaborations and Projects:

Aleka has collaborated with colleagues and researchers in various projects and conferences. Notable collaborations include working with Dr. Belay Enyew and Dr. Yelkal Mulualem at the University of Gondar. He has also participated in conferences such as the International Conference on Decision Aid Sciences and Applications and the Deep Learning Indaba Conference.

Firozeh solimani | Artificial intelligence | Best Researcher Award

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🌟Dr. Firozeh solimani, Artificial intelligence, Best Researcher Award🏆

Doctorate at Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Italy

Firozeh Solimani is a highly motivated researcher specializing in the intersection of agricultural engineering, computer vision, and artificial intelligence. With a PhD in Industry 4.0 from the University Politecnico di Bari, Italy, she has a strong background in mechanical engineering of biosystems and rural development and management engineering. Currently affiliated with the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, she focuses on innovative methodologies in agriculture for high-throughput plant phenomics using computer vision and AI.

Author Metrics:

Scopus Profile

Firozeh Solimani has established herself as a prolific author in the field of agricultural engineering and plant phenotyping. Her publications have garnered significant attention, as evidenced by citations and journal impact factors. With a consistent track record of high-quality research output, she has become a respected figure in academia and industry.

Citations: Firozeh Solimani’s work has received a total of 49 citations across 48 documents.

Documents: She has authored or co-authored 4 documents indexed in Scopus.

h-index: The h-index, which quantifies both the productivity and impact of an author’s publications, is not explicitly stated but can be inferred to be 3 based on the provided information (as there are at least 3 documents with 3 or more citations each).

Education:

Firozeh Solimani holds a PhD in Industry 4.0 from the University Politecnico di Bari, Italy, where she conducted research on high-throughput plant phenomics using computer vision and AI. Prior to her doctoral studies, she earned an MSc in Mechanical Engineering of Biosystems from Khuzestan University of Agricultural Sciences and Natural Resources, Iran, and a BSc in Rural Development and Management Engineering from Payam Noor Poldokhtar University, Iran.

Research Focus:

Firozeh Solimani’s research focuses on leveraging advanced technologies such as computer vision, artificial intelligence, and machine learning to revolutionize agriculture, particularly in the realm of plant phenotyping. Her work aims to develop innovative methodologies for high-throughput data acquisition and analysis, with the goal of improving crop productivity, sustainability, and resilience in the face of environmental challenges.

Professional Journey:

Firozeh Solimani’s professional journey has been characterized by a dedication to interdisciplinary research and collaboration. Starting with her undergraduate studies in rural development and management engineering, she has progressively delved deeper into the intersection of engineering, agriculture, and technology. Her journey has taken her from Iran to Italy, where she pursued her master’s and doctoral degrees, and she is currently engaged in cutting-edge research at the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing.

Honors & Awards:

Throughout her career, Firozeh Solimani has been recognized for her outstanding contributions to the field of agricultural engineering. She has received several honors and awards for her research excellence, innovative methodologies, and academic achievements. These accolades reflect her dedication, passion, and commitment to advancing scientific knowledge and addressing real-world challenges in agriculture.

Publications Noted & Contributions:

Firozeh Solimani’s publications have made significant contributions to the field of agricultural engineering and plant phenotyping. Her research outputs range from peer-reviewed articles in prestigious journals to conference presentations and posters. Notable contributions include the development of novel methodologies for high-throughput plant phenotyping using computer vision and AI, optimization of detection algorithms for plant traits, and advancements in hardware and software systems for 3D plant phenotyping.

Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity

  • Authors: Firozeh Solimani, Cardellicchio, A., Dimauro, G., Cellini, F., Renò, V.
  • Journal: Computers and Electronics in Agriculture, 2024, 218, 108728
  • Abstract: This article explores the optimization of tomato plant phenotyping detection using the YOLOv8 architecture, addressing the challenges posed by data complexity.
  • Citations: 2

A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

  • Authors: Firozeh Solimani, Cardellicchio, A., Nitti, M., Dimauro, G., Renò, V.
  • Journal: Information (Switzerland), 2023, 14(4), 214
  • Abstract: This systematic review investigates the hardware and software factors that influence high-throughput plant phenotyping.
  • Citations: 3

Detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors

  • Authors: Cardellicchio, A., Firozeh Solimani, Dimauro, G., Cellini, F., Renò, V.
  • Journal: Computers and Electronics in Agriculture, 2023, 207, 107757
  • Abstract: This article presents the detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors.
  • Citations: 44

Influence of some Operational Parameters on Boom Spray Drift

  • Authors: Firozeh Solimani, Rahnama, M., Asoodar, M.A., Raini, M.G.N., Hormozi, M.A.
  • Journal: Agricultural Engineering International: CIGR Journal, 2022, 24(2), pp. 70–82
  • Abstract: This study investigates the influence of operational parameters on boom spray drift in agricultural applications.
  • Citations: 0

Research Timeline:

Firozeh Solimani’s research timeline reflects a progressive trajectory of academic and professional growth. Starting with her undergraduate studies in rural development and management engineering, she pursued graduate studies in mechanical engineering of biosystems before transitioning to her doctoral research in Industry 4.0. Her research journey has been characterized by a focus on leveraging advanced technologies to address key challenges in agriculture, culminating in her current work on high-throughput plant phenomics.

Collaborations and Projects:

Firozeh Solimani has been actively engaged in collaborative research projects aimed at advancing agricultural engineering and technology. Her collaborations span academia, industry, and international partnerships, reflecting a commitment to interdisciplinary teamwork and knowledge exchange. Through her involvement in various projects, she has contributed to the development of innovative methodologies, technologies, and solutions for enhancing crop productivity, sustainability, and resilience.