Anass Ben moula | Biotechnology | Best Researcher Award

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🌟Prof Dr. Anass Ben moula, Biotechnology, Best Researcher Award🏆

 Professor at Abdelmalek Essaadi university, Morocco

John Doe is a renowned scientist with expertise in the field of molecular biology. He holds a Ph.D. in Genetics from Harvard University and has made significant contributions to understanding the genetic basis of human diseases. Throughout his career, he has received numerous awards and honors for his research accomplishments, and his work has been widely published in top-tier scientific journals.

Author Metrics:

Scopus Profile

Orcid Profile

Google Scholar Profile

John’s publications have received high citation counts, His work is highly regarded within the scientific community, and he frequently collaborates with researchers from around the world.

Citations: This refers to the total number of times their published papers have been cited by other researchers. For the first author, their papers have been cited 254 times, while for the second author, their papers have been cited 213 times.

h-index: The h-index is a measure of both the productivity and citation impact of a researcher. An author with an h-index of 6 has published 6 papers that have each been cited at least 6 times. It’s a way to quantify both the quantity and impact of an author’s research output.

i10-index: The i10-index is another measure of citation impact that specifically focuses on the number of papers that have been cited at least 10 times. An i10-index of 4 means that each author has published at least 4 papers that have been cited at least 10 times.

Education:

John earned his Ph.D. in Genetics from Harvard University, where he conducted groundbreaking research on the role of genetic mutations in cancer development. Prior to his doctoral studies, he obtained a Bachelor’s degree in Biochemistry from Stanford University, where he graduated with honors.

Research Focus:

John’s research focuses on unraveling the molecular mechanisms underlying human diseases, particularly cancer. He employs cutting-edge genomic technologies to identify genetic mutations and aberrant signaling pathways involved in tumor initiation and progression. His work aims to uncover novel therapeutic targets for cancer treatment.

Professional Journey:

After completing his Ph.D., John joined the faculty at Yale University as an Assistant Professor of Molecular Biology. He quickly rose through the ranks and is currently a tenured Professor and the Chair of the Department of Genetics at Yale. Throughout his career, he has secured substantial research funding and has mentored numerous graduate students and postdoctoral fellows.

Honors & Awards:

John has been recognized with several prestigious awards for his scientific contributions, including the Nobel Prize in Medicine for his discovery of a key signaling pathway implicated in cancer development. He is also a Fellow of the American Association for the Advancement of Science and has received the Breakthrough Prize in Life Sciences.

Publications Top Noted & Contributions:

John has authored over 200 peer-reviewed publications in leading scientific journals, including Nature, Science, and Cell. His research has significantly advanced our understanding of cancer biology and has led to the development of targeted therapies for various types of cancer.

Supplementation of ram semen extender to improve seminal quality and fertility rate (2018):

  • Investigated the effects of supplementing ram semen extender on seminal quality and fertility rate.
  • Authors: L. Allai, A. Benmoula, MM da Silva, B. Nasser, B. El Amiri.
  • Cited by: 90
  • Year: 2018

Protective effects of Opuntia ficus-indica extract on ram sperm quality, lipid peroxidation, and DNA fragmentation during liquid storage (2016):

  • Explored the protective effects of Opuntia ficus-indica extract on ram sperm quality, lipid peroxidation, and DNA fragmentation during liquid storage.
  • Authors: L. Allai, X. Druart, M. Öztürk, A. BenMoula, B. Nasser, B. El Amiri.
  • Cited by: 44
  • Year: 2016

Effect of argan oil on liquid storage of ram semen in Tris or skim milk-based extenders (2015):

  • Studied the effect of argan oil on liquid storage of ram semen in Tris or skim milk-based extenders.
  • Authors: L. Allai, X. Druart, J. Contell, N. Louanjli, AB Moula, A. Badi, A. Essamadi, …
  • Cited by: 43
  • Year: 2015

Effect of season on scrotal circumference, semen characteristics, seminal plasma composition, and spermatozoa motility during liquid storage in INRA180 rams (2017):

  • Explored the effect of season on various parameters including scrotal circumference, semen characteristics, seminal plasma composition, and spermatozoa motility during liquid storage in INRA180 rams.
  • Authors: A. Benmoula, A. Badi, M. El Fadili, KEL Khalil, L. Allai, A. El Hilali, B. El Amiri.
  • Cited by: 32
  • Year: 2017

Does advanced age affect reproductive variables, semen composition, and liquid semen storage during different seasons in Boujaâd rams? (Year not provided):

  • Investigated the impact of advanced age on reproductive variables, semen composition, and liquid semen storage during different seasons in Boujaâd rams.
  • Authors: A. Badi, A. Benmoula, K. El Khalil, L. Allai, A. Essamadi, B. Nasser, B. El Amiri.
  • Cited by: 197

Research Timeline:

John’s research career spans over two decades, during which he has made seminal discoveries in the field of molecular biology. He has continually pushed the boundaries of scientific knowledge, with landmark achievements that have reshaped our understanding of cancer genetics and therapy.

Lotfi Chaari | Neurosciences | Best Researcher Award

Published on by Global Scholar Awards

🌟Prof. Lotfi Chaari, Neurosciences, Best Researcher Award🏆

 Professor at Toulouse INP, France

Dr. Lotfi CHAARI, a French citizen and Associate Professor HDR at Institut National Polytechnique de Toulouse (Toulouse INP), is a distinguished researcher specializing in signal and image processing, artificial intelligence, and machine learning. With an extensive background and expertise, he has made notable contributions to biomedical signal processing and remote sensing.

Author Metrics:

His excellence is evident through notable author metrics, including the receipt of the Best Paper Award in 2022 and achieving the status of IEEE Senior Member in 2019, highlighting his significant impact on the field.

Google Scholar Profile

Citations:

Total Citations: 5048

h-index:

h-index: 24

Explanation: The h-index is a measure of both the productivity and impact of a researcher’s work. An h-index of 24 means that the researcher has published at least 24 papers that have each been cited at least 24 times.

i10-index:

i10-index: 32

Explanation: The i10-index is specific to Google Scholar and represents the number of publications (articles, conference papers, etc.) by the researcher that have at least 10 citations each. An i10-index of 32 indicates that the researcher has 32 publications with at least 10 citations each.

Education:

Dr. CHAARI earned his Ph.D. in Signal and Image Processing from the University of Paris-Est Marne-la-Vallée in 2010. Subsequently, he completed a post-doctoral dissertation (HDR) at Toulouse INP in 2017.

Research Focus:

His research interests encompass a broad spectrum, including artificial intelligence, machine learning, deep learning, anomaly detection, pattern recognition, and biomedical signal and image processing. Dr. CHAARI has a particular focus on variational and Bayesian optimization, as well as inverse problems, restoration, and enhancement.

Professional Journey:

Since 2012, Dr. CHAARI has served as an Associate Professor at Toulouse INP, contributing significantly to the fields of artificial intelligence and machine learning. Prior to this, he gained valuable experience as a post-doctoral fellow at INRIA Grenoble-Rhône Alpes from 2010 to 2012.

Honors & Awards:

His contributions have been recognized with prestigious honors and awards, including the HOPE Best Workshops Paper in 2023, the Nutrients 2022 Best Paper Award, and his elevation to the grade of IEEE Senior Member in 2019. Additionally, he holds a position on the National Representative Board for EPMA.

Publications Top Noted & Contributions:

Dr. CHAARI has actively contributed to reputable journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Image Processing, and IEEE Transactions on Medical Imaging. He has also played a crucial role in conferences, including IEEE ICIP, IEEE ICASSP, EUSIPCO, and ISBI.

The impact of COVID-19 home confinement on various aspects of individuals’ lives has been extensively investigated through the ECLB-COVID19 international online survey. The results of these studies, conducted by researchers such as A. Ammar, M. Brach, K. Trabelsi, H. Chtourou, O. Boukhris, L. Masmoudi, and others, have been published in several scientific journals:

The abstract you provided describes a study focused on the detection of Mild Cognitive Impairment (MCI) using EEG and HRV (Heart Rate Variability) data. 

Objective: The study aims to develop a safe and effective method for early detection of Mild Cognitive Impairment, a condition often linked to neurodegeneration and aging, which may progress to Alzheimer’s disease.

Methodology:

  1. The study uses a dataset comprising EEG and HRV data from 15 subjects, randomly assigned to training and testing groups of healthy controls (HC) and MCI patients.
  2. Raw EEG and HRV data are analyzed to extract various features, including time, frequency, and non-linear features.
  3. A scaling step is employed to address significant disparities between features.

Machine Learning Models: Five machine learning models are evaluated for the classification task: Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). A hybrid machine learning model with a voting system is developed, combining the top-performing models for enhanced accuracy.

Results: The experimental findings demonstrate the efficacy of the proposed technique:

  • An average accuracy of 93.86% is achieved.
  • Sensitivity (true positive rate) is 93.87%.
  • Specificity (true negative rate) is 93.53%.

Conclusion: The study concludes that the developed method, utilizing a hybrid machine learning model, is effective in categorizing MCI and healthy control patients during the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) task using EEG and HRV multimodal data. The first CERAD test is highlighted as a novel biomarker for early MCI identification.

Significance: The findings suggest that combining EEG and HRV signals in a multimodal approach, along with machine learning models, can offer a reliable and efficient method for early detection of Mild Cognitive Impairment, providing a potential avenue for timely intervention and treatment.

“Effects of COVID-19 home confinement on eating behaviour and physical activity: results of the ECLB-COVID19 international online survey”

  • Authors: A. Ammar, M. Brach, K. Trabelsi, H. Chtourou, O. Boukhris, L. Masmoudi, et al.
  • Published in: Nutrients, 12 (6), 1583–2314, 2020
  • Focus: Investigates the impact of COVID-19 home confinement on eating behavior and physical activity.

“COVID-19 home confinement negatively impacts social participation and life satisfaction: a worldwide multicenter study”

  • Authors: A. Ammar, H. Chtourou, O. Boukhris, K. Trabelsi, L. Masmoudi, M. Brach, et al.
  • Published in: International Journal of Environmental Research and Public Health, 17 (17), 6237–452, 2020
  • Focus: Explores the global impact of COVID-19 home confinement on social participation and life satisfaction.

“Effects of home confinement on mental health and lifestyle behaviors during the COVID-19 outbreak: Insight from the ECLB-COVID19 multicenter study”

  • Authors: A. Ammar, K. Trabelsi, M. Brach, H. Chtourou, O. Boukhris, L. Masmoudi, et al.
  • Published in: Biology of Sport, 38 (1), 9–21, 2021
  • Focus: Examines the effects of home confinement on mental health and lifestyle behaviors during the COVID-19 outbreak.

“Psychological consequences of COVID-19 home confinement: The ECLB-COVID19 multicenter study”

  • Authors: A. Ammar, P. Mueller, K. Trabelsi, H. Chtourou, O. Boukhris, L. Masmoudi, et al.
  • Published in: PloS One, 15 (11), e0240204, 2020
  • Focus: Investigates the psychological consequences of COVID-19 home confinement.

“Globally altered sleep patterns and physical activity levels by confinement in 5056 individuals: ECLB COVID-19 international online survey”

  • Authors: K. Trabelsi, A. Ammar, L. Masmoudi, O. Boukhris, H. Chtourou, B. Bouaziz, et al.
  • Published in: Biology of Sport, 38 (4), 495–506
  • Focus: Examines the worldwide changes in sleep patterns and physical activity levels during COVID-19 confinement in a large sample of 5056 individuals.

Research Timeline:

Commencing his research journey in 2010 with a post-doctoral fellowship, Dr. CHAARI has steadily progressed, becoming an Associate Professor at Toulouse INP in 2012. Over the years, his research has consistently focused on advancing knowledge in signal and image processing, artificial intelligence, and machine learning.