tackle the challenge: term paper writing guides

A List Of Great Computer Science Term Paper Ideas

Computer science is a fast changing field, where new problems and approaches emerge virtually every day. As such, it is not very difficult to pick a fascinating term paper topic on this subject.

  • Open source libraries and their impact on program development.
  • Loosely typed languages: their evolution; use; and future trends.
  • Statistical machine learning.

  • Examine the current uses of statistical machine learning techniques. Can recent advancement in this field be considered to be the birth of artificial intelligence (AI)?

  • NoSQL for big data representation.
  • Clever caching: how can it be optimized?
  • Cloud based archives: are they secure?
  • Encryption, permission models, and security layers: a comparative study of the most popular data protection methods.

  • As new types of cyber attacks emerge every year, the topic of computer security remains one of the most pressing. Compare the most popular data protection methods, or focus on a single one, explaining their past evolution and ways for future optimization.

  • Computer networking systems: is there an upper capacity limit?
  • By building larger networks we can solve more difficult tasks, but it also becomes increasingly difficult to ensure their proper operation.

  • Does the boundary between hardware and software still exist?
  • The current trend is that hardware is becoming more flexible and more programmable. Will we still be referring to “hardware” and “software” as separate entities in a decade?

  • Quantum computers: a promising direction, or a dead end?
  • Lots of effort is constantly being made by scientists in different countries to create quantum computers, which will be able to process information much faster, and have a much greater memory than their silicon analogues. Review one recent attempt in this field. Discuss how feasible its goal was, and what was actually achieved.

  • Machine translation.
  • What do computers still need in order to become better translators than humans? Is it possible to create an accurate automatic translator that is not an AI?

  • Speech recognition.
  • How are machines taught to recognize speech? What problems still need to be solved in this field?

  • Image recognition.
  • How can programs be taught to recognize images as well as humans can?

  • Virtualization.
  • Review its history, starting with Java VM and Microsoft CRL, and predict trends for future development.

  • Complexity theory.
  • Have we come any closer to solving this fundamental challenge to computer science?

  • Parallel programming.

  • The peak of compressed sensing: has it already been achieved?.

  • Complex mechanism evolution.
  • There have been experiments, in which generations of software entities have demonstrated a pattern similar to that of Darwinian evolution: self-driven complication through random mutations. What is the significance, and possible implications, of such experiments? Is this the way in which AI is likely to come into the world?

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