New Trends in Computer Sciences DOI

Published: April 11, 2023

Agriculture plays a crucial role in driving economic growth worldwide.With the continuous of global population, need for food production and labor has become increasingly demanding.However, agriculture faces various challenges throughout entire process, from planting to harvesting.Key obstacles include inadequate chemical application, pest disease infestation, improper irrigation, drainage, weed control, yield forecasting.These have sparked discussions concerns about automating agricultural practices.The advent artificial intelligence (AI) brought significant changes field agriculture.This transformative technology offers solutions safeguard against threats such as population growth, climate change, disputes, security concerns.By harnessing sensors incorporating them into robots drones, AI can assist with crop monitoring other vital tasks.This not only improves worker safety but also mitigates impact on natural ecosystems, enabling maintenance affordable prices while ensuring increased meet needs our expanding population.This proposed study is designed highlight paradigm shift taking place agriculture, placing strong emphasis integration AI-driven drones cultivation, irrigation monitoring.It underscores tremendous potential these evolving technologies address associated feeding growing simultaneously promoting sustainable farming practices.By leveraging AI, robotic promising future where processes are more efficient, productive, environmentally friendly.

Language: Английский

Cancer detection and segmentation using machine learning and deep learning techniques: a review DOI
Hari Mohan

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(9), P. 27001 - 27035

Published: Aug. 22, 2023

Language: Английский

Citations

34
A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics DOI
Hari Mohan, Joon Yoo

Journal of Cancer Research and Clinical Oncology, Journal Year: 2023, Volume and Issue: 149(15), P. 14365 - 14408

Published: Aug. 4, 2023

Language: Английский

Citations

22
An Explainable Artificial Intelligence Model for the Classification of Breast Cancer DOI Creative Commons
Tarek Khater, Abir Hussain, Riyad Bendardaf

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 1

Published: Aug. 24, 2023

Breast cancer is the most common among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed malignant cells. Early detection breast crucial for enhancing survival rate. Therefore, artificial intelligence has revolutionized healthcare can serve as a promising tool early diagnosis. present study aims develop machine-learning model classify provide explanations results. This could improve understanding diagnosis treatment by identifying important features tumors way they affect classification task. best-performing achieved an accuracy 97.7% using k-nearest neighbors precision 98.2% based on Wisconsin dataset 98.6% neural network with 94.4% diagnostic dataset. Hence, this asserts importance effectiveness proposed approach. research explains behavior model-agnostic methods, demonstrating that bare nuclei feature in area’s worst are factors determining malignancy. work provides extensive insights into particular characteristics suggests possible directions expected investigation future fundamental biological mechanisms underlie disease’s onset. findings underline potential machine learning enhance therapy planning while emphasizing interpretability transparency intelligence-based systems.

Language: Английский

Citations

16
Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques DOI
Hari Mohan, Joon Yoo, Serhii Dashkevych

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Language: Английский

Citations

0
Demystifying diagnosis: an efficient deep learning technique with explainable AI to improve breast cancer detection DOI Creative Commons
Ahmed Alzahrani,

Muhammad Ali Raza,

Muhammad Zubair Asghar

et al.

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2806 - e2806

Published: April 16, 2025

As per a WHO survey conducted in 2023, more than 2.3 million breast cancer (BC) cases are reported every year. In nearly 95% of countries, the second leading cause death for females is BC. Breast and cervical cancers 80% deaths middle-income countries. Early detection can help patients better manage their condition increase chances survival. However, traditional AI models frequently conceal decision-making processes mainly tailored classification tasks. Our approach combines composite deep learning techniques with explainable artificial intelligence (XAI) to enhance interpretability predictive accuracy. By utilizing XAI examine features provide insights into its classifications, model clarifies rationale behind decisions, resulting an understanding concealed patterns linked detection. The strengthens practitioners’ health researchers’ confidence (AI)-based models. this work, we introduce hybrid bi-directional long short-term memory-convolutional neural network (BiLSTM-CNN) identify using patient data effectively. We first balanced dataset before BiLSTM-CNN model. (DL) presented here performed well comparison other studies, 0.993 accuracy, precision 0.99, recall F1-score 0.99.

Language: Английский

Citations

0
A Systematic Review of Machine Learning Algorithms for Breast Cancer Detection DOI

Aryan Sai Boddu,

AIMAN JAN -

Tissue and Cell, Journal Year: 2025, Volume and Issue: 95, P. 102929 - 102929

Published: April 25, 2025

Language: Английский

Citations

0
Machine Learning Approach for the Classification to Overcome Traffic Sign Detection Challenge DOI
Fatima Qanouni, Hakim El Massari, Noreddine Gherabi

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 383 - 389

Published: Jan. 1, 2025

Language: Английский

Citations

0
Analysis of Depressed Patients’ Sentiments Towards Treatment: AI Approach for Healthcare Professionals DOI

Maria El-Badaoui,

Noreddine Gherabi, Fatima Qanouni

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 64 - 71

Published: Jan. 1, 2025

Language: Английский

Citations

0
The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review DOI
Hakim El Massari, Noreddine Gherabi, Fatima Qanouni

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 410 - 415

Published: Jan. 1, 2025

Language: Английский

Citations

0
Road Accident Detection using SVM and Learning: A Comparative Study DOI Open Access
Fatima Qanouni, Hakim El Massari, Noreddine Gherabi

et al.

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(5)

Published: Jan. 1, 2024

Everyday, a great deal of children and young adults (aged five to 29) lives are lost in road accidents. The most frequent causes driver's behavior, the streets infrastructure is lower quality delayed response emergency services especially rural areas. There need for automatics accident systems detection that can assist recognizing accidents determining their positions. This work reviews existing machine learning approaches detection. We propose three distinct classifiers: Convolutional Neural Network CNN, Recurrent Convolution R-CNN Support Vector Machine SVM, using CCTV footage dataset. These models evaluated based on ROC curve, F1 measure, precision, accuracy recall, achieved accuracies were 92%, 82%, 93%, respectively. In addition, we suggest an ensemble strategy maximize strengths individual classifiers, raising 94%.

Language: Английский

Citations

2