Illustration of the transformed binary images of normal, benign, and malignant samples into grayscale. by Tehnan I. A. Mohamed (16846175)

Performance evaluation of different schemes for time-insensitive tasks. by Shehr Bano (20421706)

Performance evaluation of different schemes for time-insensitive tasks. by Shehr Bano (20421706)

Decoding evidence for feature triplets on test tasks. by Sam Hall-McMaster (10343795)

Demonstration of algorithm convergence. by Quyuan Wang (8724972)

“…To maintain dynamic market equilibrium, we develop two types of pricing algorithms, one based on stepped price adjustments for selected sellers, and another based on smoothed adjustments for all sellers. …”

Tasks with varying sizes. by Faheem Nawaz Tareen (18319868)

“…This results in the successful execution of most of the tasks within their deadline. In addition, <i>EOTE</i> − <i>FSC</i> modifies the task sequencing with a deadline algorithm for the fog node to optimally execute the offloaded tasks in such a way that most of the high-priority tasks are entertained. …”

Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

Decoding results. by Sam Hall-McMaster (10343795)

HIV screening algorithm. by Wei Li A. Koay (21094209)

Assigning different sizes of task. by Faheem Nawaz Tareen (18319868)

“…This results in the successful execution of most of the tasks within their deadline. In addition, <i>EOTE</i> − <i>FSC</i> modifies the task sequencing with a deadline algorithm for the fog node to optimally execute the offloaded tasks in such a way that most of the high-priority tasks are entertained. …”

Standard deviation for a varying number of tasks. by Faheem Nawaz Tareen (18319868)

“…This results in the successful execution of most of the tasks within their deadline. In addition, <i>EOTE</i> − <i>FSC</i> modifies the task sequencing with a deadline algorithm for the fog node to optimally execute the offloaded tasks in such a way that most of the high-priority tasks are entertained. …”

An overview of PHyPO. by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S8 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S6 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S5 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S17 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S11 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S10 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S4 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”

S2 Data - by Shehr Bano (20421706)

“…The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques.…”