Performance comparison of ML models. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Comparative data of different soil samples. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Confusion matrix of random forest model. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Evaluation metrics of selected ML models. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Block diagram of the proposed system. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Chart for applicable amount of fertilizers. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Cost analysis of irrigation controller unit. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Run times of two algorithms. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Some examples of selected Chinese characters. by Weijia Zhu (65481)

“…To address these challenges, we propose esFont, a novel guided Diffusion framework. …”

Flow chart of Fuzzy Logic based control system. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Block diagram for IoT-based irrigation system. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Flow chart of Average Value-based control system. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Hardware design for IoT-based irrigation system. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Biotransformation Dynamics and Products of Cyanobacterial Secondary Metabolites in Surface Waters by Xuejian Wang (532695)

“…Cyanobacteria produce toxic and bioactive secondary metabolites, posing risks to ecosystems and human health, yet their transformation pathways in surface waters remain unclear. We assessed biotransformation for 40 cyanopeptides including microcystins, anabaenopeptins and cyanopeptolins in surface waters and <i>in situ</i> enriched biofilm suspensions. …”

Baseline measures of participants. by Philippa Charlotte Rose Wheble (21578605)

“…<div><p>Music Performance Anxiety, a subset of social anxiety disorder (SAD), can significantly impede the lives of professional voice users (PVUs). …”

Demographic characteristics of participants. by Philippa Charlotte Rose Wheble (21578605)

“…<div><p>Music Performance Anxiety, a subset of social anxiety disorder (SAD), can significantly impede the lives of professional voice users (PVUs). …”

Trends in ESR per survey round by age group. by Alex Daama (18351052)

“…However, some men resume sex early before the recommended period. We estimated trends in prevalence and risk factors of early sex resumption (ESR) among VMMC clients in Rakai, Uganda, from 2013–2020.…”

Illustrative diagram of the three crash types. by Chun-Chieh Chao (2681722)

“…</p><p>Results</p><p>Significant risk factors for overtaking crashes included heavy goods vehicles (HGVs) as crash partners (AOR = 1.30, 95% CI 1.27–1.33), and elderly crash partners (AOR = 2.01, 95% CI = 1.94–2.09), and decreased risk in rural area with speed limits of 20–30 miles per hour (AOR = 0.45, 95% CI = 0.43–0.47). …”

Multivariate logistic regression results. by Chun-Chieh Chao (2681722)

“…</p><p>Results</p><p>Significant risk factors for overtaking crashes included heavy goods vehicles (HGVs) as crash partners (AOR = 1.30, 95% CI 1.27–1.33), and elderly crash partners (AOR = 2.01, 95% CI = 1.94–2.09), and decreased risk in rural area with speed limits of 20–30 miles per hour (AOR = 0.45, 95% CI = 0.43–0.47). …”