Courses Teaching & Developed

Student Evaluation Scores (Overall)

Graduate (CSE 5XX): 4.34 / 5.0
Senior Undergraduate (CSE 3/4XX): 3.96 / 5.0
Freshman/Sophomore (CSE 1XX): 3.43 / 5.0

Currently Teaching

CourseTitle
CSE 3024Principles of Programming LanguagesSpringUG
Prereq: CSE 1022 (C or higher), Co-req: CSE 2013
Syntax and semantics of HLLs; data types; control structures; concurrency; procedural and data abstraction; imperative, OO, functional and logic paradigms. Examples from Ada, Lisp, Smalltalk, Prolog.
CSE/IT 3053Introduction to Computer NetworksFallUG
Prereq: CSE 222 (C or higher)
ISO/OSI protocol stack; LAN, MAN, WAN; physical layer; data link layer; MAC protocols; IEEE 802.11; IP routing; TCP/IP; network security; DNS, Email.
CSE 4052Introduction to Sensor NetworksSpringUG
Prereq: CSE 3025 and CSE 3053
WSN protocols, applications, topologies, deployment, data manipulation, mobile ad-hoc wireless communication, security. Recent research developments.
CSE 4064Intro to Soft ComputingFallUG
Prereq: MATH 2420, 3082; CSE 3044 (C or higher)
Major ANN types; fuzzy logic theory; genetic algorithms; evolutionary computing; intelligent engineering applications.
CSE 4065Intro Neural Networks ApplicationsSpringUG
Prereq: CSE 2013, 2022 (C or higher)
Biological neurons and modeling; perceptron; backpropagation; Hopfield model; Kohonen SOFM; LVQ; ART model. ANN application projects.
CSE 4089Smart & Secure Sensory SystemsSpringUG
Prereq: MATH 2420, 3082; or CSE 3044
Design of SS-WSNs toward IoT. WSN technology, protocols, neural network modeling, WSN security, critical military and civil applications.
CSE 5067Soft ComputingFallGrad
Prereq: MATH 2420, 3082; CSE 3044 (C or higher)
Multilayer feedback networks; self-organizing networks; Hopfield networks; learning algorithms; fuzzy systems; evolutionary computing; engineering applications.
CSE 5089Neural NetworksSpringGrad
Prereq: CSE 207 or CSE 3044 (C or better); MATH 2420, 382
Biological neuron models; single/multilayer ANN; backpropagation; Hopfield; SOFM; LVQ; counterpropagation; ART. Graduate-level depth and rigor.
CSE 5089Advances in Sensor NetworksSpringGrad
Prereq: Two semesters of upper division CS + consent of instructor
Advanced WSN topics; protocols; mobile ad-hoc; security; student literature presentations on latest research.

Courses Developed by Dr. Soliman

Undergraduate (6 courses developed)

  • CSE/IT 453 — Computer Networks & the Internet
  • CSE 4052 — Introduction to Sensor Networks
  • CSE 4064 — Introduction to Soft Computing
  • CSE 4065 — Intro to Neural Networks Applications
  • CSE 4089 — Smart & Secure Sensory Systems
  • CSE 454 — Computer Graphics

Graduate (11 courses developed)

  • CSE 5067 — Soft Computing
  • CSE 5089 — Neural Networks
  • CSE 5089 — Advances in Sensor Networks
  • CSE 589 — Advanced Topics in Computer Networks
  • CSE 589 — Modeling & Simulation of Computer Networks
  • CSE 589 — Wireless Security and Performance Analysis
  • CSE 589 — Internet Security and Performance Analysis
  • CSE 589 — Wireless Sensor Networks: Routing and Security
  • CSE 589 — Topics in Modeling and Simulation
  • CSE 589 — Advanced Topics in Operating Systems
  • CSE 565 — Neural Networks (original version)

Teaching Highlights

  • Developed 6 undergraduate and 11 graduate classes — first to introduce Neural Networks and Sensor Networks at NMT.
  • Introduced Computer Networking at both undergraduate and graduate levels (six courses) in the CSE Department.
  • Refined and taught Principles of Programming Languages for over 20 years.
  • Helped form the Information Technology specialization at NMT, including IT networking classes.
  • Also taught: Advanced Computer Networks, Advanced Operating Systems, Computer Graphics, Graduate Seminar, and multiple Special Topics courses.