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. …”

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. …”

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

“…ESR prevalence was higher among participants who reported to have more than one sexual partner than participants with one partner, aPR = 1.59, 95% CI: [1.16,2.20]. In the fourth survey from 2018–2020, ESR prevalence was significantly higher among participants with primary education than participants with post-primary, aPR = 2.38, 95% CI: [1.31, 4.30]. …”

Supplementary file 1_“We find that…” changing patterns of epistemic positioning in research writing.docx by Yanfang Yang (408813)

“…</p>Results<p>Our findings show that epistemic positioning has significantly decreased across all four disciplines over time, with writers increasingly preferring less use of epistemic markers in pursuit of an objective, data-based, and scientific style.…”

Diagnostic criteria for Alcoholic cardiomyopathy. by Fei Yan (128878)

“…</p><p><b>Conclusion:</b> In 2021, ACM remained a significant global health burden, particularly affecting middle-aged and elderly populations with distinct demographic disparities. …”

PCA-CGAN model parameter settings. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

Wavelet transform preprocessing results. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

PCAECG_GAN. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

MIT dataset expansion quantities and Proportions. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

Experimental hardware and software environment. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

PCA-CGAN K-fold experiment table. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

Classification model parameter settings. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”