Concept of the Loss function in the case of Supervised Machine Learning and Physics Machine Learning approach. by Akshansh Mishra (21001499)

A. The boxplot displays the distribution of enrichment scores for disease-related immune functions between the control group and the experimental group. by Min-xiao Wang (20868922)

Flowchart of proposed fitness function algorithm. by Ferhat Yuna (21519851)

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Overnight technician routing and scheduling problem with time windows and balanced workloads: a bi-objective zebra optimization algorithm by Abolfazl Gharaei (21803416)

“…The performance evaluation and validation results revealed that the proposed ML-based BOZOA provides very good performance in solving TRSPTWs at a variety of scales with respect to the optimality criteria, including, number of taken iterations, infeasibility, optimality error and complementarity compared with both an exact solver and two inspired algorithms from ZOA.</p> <p><b>Highlights</b></p><p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p><p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p><p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p><p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p><p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p><p>Experiments using real data to show the performance of the model and solution method</p><p></p> <p>An ML-based bi-objective zebra optimisation algorithm to treat large-scale TRSPs</p> <p>Centroid-based clustering on the population of zebras to avoid bias towards a specific search space</p> <p>Making a trade-off between exploration and exploitation of the feasible region in the developed algorithm</p> <p>A new MINLP model of a weighted bi-objective TRSP with limited capacity depots</p> <p>Workload function, penalty function for lateness, subcontracts, time windows for tasks and breaks</p> <p>Experiments using real data to show the performance of the model and solution method</p>…”

Scenarios for algorithms using IEEE 33 bus. by Bachirou Bogno (20485724)

Scenarios for algorithms using IEEE 69 bus. by Bachirou Bogno (20485724)

System diagram for agricultural land mapping using satellite image. by Gourab Saha (8987405)

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Drug Release Nanoparticle System Design: Data Set Compilation and Machine Learning Modeling by Shan He (121220)

“…Herein, 11 different AI/ML algorithms were used to develop the predictive AI/ML models. …”

Overview of the comparative analysis of deconvolution model performances using synthetic spatial data in four different scenarios and six different mixture percentages. by Nuray Sogunmez Erdogan (21533872)

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A-B. The boxplots present the distribution of scores for IRF4 and ELANE immune-related functions in low-expression and high-expression groups. by Min-xiao Wang (20868922)

Objective function values of different algorithms on individual problem instances. by Panfei Li (22379086)

The parameters of algorithms. by Le Huu Binh (18340233)

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SP results of algorithms in the CEC 2009 test functions. by Vahid Goodarzimehr (21343124)

MS results of algorithms in the CEC 2009 test functions. by Vahid Goodarzimehr (21343124)

The effect of test set size and number of nodes on the values of confusion matrix using SVM combined with the uniform window. by Abeer Aljohani (18497914)

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The box plot depicting the differential expression validation of the hub genes in the external dataset GSE90081. by Devi Soorya Narayana Sasikumar (22331743)

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The Venn diagram showing the intersection of common genes between WGCNA and DEFeGs. by Devi Soorya Narayana Sasikumar (22331743)

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The classification report of the generated LASSO, RF, and SVM models. by Devi Soorya Narayana Sasikumar (22331743)

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a-c: (a) The nomogram model developed for the prognostic prediction of hub genes from the study. by Devi Soorya Narayana Sasikumar (22331743)

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a-h: The expression differences of the 8 hub genes in 18 immune infiltrating cells. by Devi Soorya Narayana Sasikumar (22331743)

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