https://www.fox.temple.edu/directory/xueming-luo-tuf35198
Biography Xueming Luo is Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategic Management, Professor of Management Information Systems. He is the Founder/Director of the Global Institute for Artificial Intelligence and Business Analytics in the Fox School of Business at Temple University. He is interested in digital mobile marketing, omnichannel customer ...
https://guides.temple.edu/qda/qualcoder
QualCoder is free, open source software for qualitative data analysis. It has many of the features of commercial QDA software packages such as auto-coding, coding images and A/V materials, SQL database querying, and many reporting and visualization options.
https://bulletin.temple.edu/courses/mis/
The MIS department prioritizes the professional development of its students as high as the domain specific knowledge and skills students develop in many of its classes. This zero-credit, credit/no-credit, self-directed course challenges students to complete a portfolio of professional development activities which prepare students to be valued contributors and leaders in industry after they ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/AVOA_Fuzzy.pdf
A1 =BestV ulture1(i) − |X × R(i) V − (i)| × F, (14) A2 =BestV ulture2(i) − |X × R(i) − V (i)| × F, V (i + 1) = (A1 + A2)/2. (15) At this time, other Vultures compete with it and move in different directions for hunting. This behavior is modeled as in Eqs. (16) and (17).
https://cis.temple.edu/~giorgio/cis587/readings/id3-c45.html
Say they are, in increasing order, A1, A2, .., Am. Then for each value Aj, j=1,2,..m, we partition the records into those that have Ci values up to and including Aj, and those that have values greater than Aj.
https://cis.temple.edu/~latecki/Courses/CIS2033-Spring13/Modern_intro_probability_statistics_Dekking05.pdf
What we mean by this influence explained in more detail in the next chapter. Quick exercise 2.5 Consider the sample space a1, a2, a3, a4, a5, { experiment, where outcome ai has probability pi for a6} i = 1, . . . , 6. We this experiment twice in such a way that the associated probabilities
https://cis.temple.edu/~wu/research/publications/Publication_files/Joint%20Dynamic%20Grouping%20and%20Gradient%20Coding%20for%20Time-critical%20Distributed%20Machine%20Learning%20in%20Heterogeneous%20Edge%20Networks-FINAL-VERSION.pdf
in the subset with the smallest sum so far. In the case of subsets with equal sum, c oose any subset until set C is empty. While difference of the integers in set C. The first integer of this tuple is the value in set C and he rest of the i a1, a2, ..., an) and B = (b1, b2, ..., n),
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICC2024.pdf
Fig. 4: Transition States. TABLE I: Performance with different number of features ... Given the state of the system (st) and the actions in A = {a1, a2, a3, a4}, reward function r is defined as: R(s, a) = α∗D+β∗U +γ∗(1/T)+ω∗(1−F)+ζ ∗M, (2) where D is the detection accuracy, ranging from 0 to 1, where 1 represents perfect accuracy.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICDE2024_Online_Federated_Learning_on_Distributed_Unknown_Data_Using_UAVs.pdf
A(r)={A1(r), ..., An(r), ..., AN(r)}. Here, An(r) represents an ordered set of PoIs for each UAV n, specifying the sequence in which UAV n will visit the PoIs. Let cn be the total number of PoIs visited by UAV n. Then we denote An(r) = {mn 1, ..., mn , ..., mn cn}, ∀n ∈ N, where mn is the index of the i-th visited PoI by UAV n.