Due to the increasing global population and growing demand for food worldwide as well changes in weather conditions availability of water, artificial intelligence (AI) such expert systems, natural language processing, speech recognition, machine vision have changed not only quantity but also quality work agricultural sector. Researchers scientists are now moving toward utilization new IoT technologies smart farming help farmers use AI technology development improved seeds, crop protection,...

Machine learning applications are having a great impact on the global economy by transforming data processing method and decision making. Agriculture is one of fields where significant, considering crisis for food supply. This research investigates potential benefits integrating machine algorithms in modern agriculture. The main focus these to help optimize crop production reduce waste through informed decisions regarding planting, watering, harvesting crops. paper includes discussion...

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production reducing waste through informed decisions about planting, watering, harvesting crops. paper discusses current state agriculture, highlighting key challenges opportunities. It also presents experimental results that demonstrate impact changing labels accuracy data analysis algorithms. findings recommend by analyzing...

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into technologies in agriculture, focusing specifically on optimizing through (AI) strengthen processes farming. We present novel AI-powered model that leverages historical datasets, utilizing comprehensive array established machine...

Locating and removing landmines other ERW (Explosive Remnants of War) is dangerous, hazardous, time-consuming. It requires implementing multilevel on-site surveys: general non-technical surveys to mark the areas affected technical determine perimeter related minefields. This paper introduces a landmine location-based prediction model, combining military experience with machine-learning techniques spatiotemporal data, by introducing new approach for area selection adding military-based...

Sentiment analysis involves using computational methods to identify and classify opinions expressed in text, with the goal of determining whether writer's stance towards a particular topic, product, or idea is positive, negative, neutral. However, sentiment Arabic presents unique challenges due complexity morphology variety dialects, which make language classification even more difficult. To address these challenges, we conducted investigation overview techniques used last five years for...

This paper introduces an intelligent model that combines military expertise with the latest ad-vancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes. The is based on direct input val-idation from field decision-makers for their practical applicability effectiveness. With a survey polling inputs of experts, 95% responses came affirmation potential reduce threats increase operational efficiency. It includes...

Higher education is crucial for the development of states and societies improving overall quality life. However, entry into higher often influenced by factors beyond qualifications, individuals in field face suppression from controlling parties. These challenges undermine value integrity democratic processes like elections. In this paper, we study academic freedom Lebanon propose a technique that dynamically extracts might affect freedom. This comprises multiple stages: data collection,...

Abstract The oceans play an important role in our daily life and they form the lungs of planet. Subsequently, world ocean provides so many benefits for humans planet including oxygen production, climate regulation, transportation, recreation, food, medicine, economic, etc. However, suffer nowadays from several challenges ranging pollution to change destruction underwater habitat. Hence, use remote sensing technologies, like sensor networks IoT, is becoming essential order continuously...

Recently, governments and public authorities in most countries had to face the outbreak of COVID-19 by adopting a set policies. Consequently, some have succeeded minimizing number confirmed cases while other has led their healthcare systems breakdown. In this work, we introduce an efficient framework called COMAP (COrona MAP), aiming study predict behavior based on deep learning techniques. consists two stages: clustering prediction. The first stage proposes new algorithm Co-means, allowing...

Many social media apps provide privacy settings that allow users to control how their data should be processed and shared. Also, every account in these comes with default are often difficult grasp find, even for experts. Therefore, many users' may utilized outside of actual preferences. In this paper, we aim explore match people's real To end, performed a UK-based online survey where asked respondents about preferences some popular like Facebook LinkedIn. The results show the other than...

In this article, we propose UPDEMIN, a novel approach for predicting UPcoming patient situation, DEcision Making and INtervention in wireless body sensor network. Inspired by the National Early Warning Score (NEWS) system, new representation usage of all vital signs scores to create diagnostic label (DL) representing overall health situation. The formulation DL does not help only accurate prediction upcoming situation but provides decoding mechanism identify reasons deterioration which are...

This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas classifying them mine type, difficulty priority of clearance. The is based on direct input validation from field decision-makers for their practical applicability effectiveness, accurate historical data extracted databases. With a survey...

Purpose This study aims to investigate what extent the predictability of standard and poor’s 500 (S&P 500) price levels is enhanced by investors’ sentiments extracted from social media content, specifically platform X. Design/methodology/approach Two recurrent neural network (RNN) models are developed. The first RNN model merely based on historical records technical indicators. In addition variables included in model, second comprises outputs sentiment analysis, performed using TextBlob...

In botany and agriculture, classifying leaves is a crucial process that yields vital information for studies on biodiversity, ecological studies, the identification of plant species. The Cope Leaf Dataset offers comprehensive collection leaf images from various species, enabling development evaluation advanced classification algorithms. This study presents robust methodology within by enhancing feature extraction selection process. has 99 classes 64 features with 1584 records. Features are...