The effect of reliable data transfer and efficient computer network features in Jordanian banks accounting information systems performance based on hardware and software, database and number of hosts
Bibliographic record
Abstract
Reliable data transfer protocols are algorithmic techniques that guarantee the safe and secure transport of data through networks that could experience data loss or corruption. The performance of the systems and some accounting information systems (AIS) will be negatively impacted if the real-time data is not sent. The main factors that affect the performance of computer networks are the number of users, the hardware and software and the bandwidth. The computer network performance will play a role in the performance of the banks as it is an important component of the bank infrastructure. With the advancement of information technology, network technology, and computer technology, computers have been utilized to aid AIS operations, and AIS has become an unavoidable trend of development. Therefore, the purpose of this study was to investigate Data analysis in computer networks to improve AIS performance in Jordanian banks. A questionnaire was used to obtain the information. Jordanian banks account for the bulk of the participants in the survey. A total of 115 people took part in the study. According to the conclusions of this study, communication technology networks have a statistically significant impact on the growth of Jordanian banks' improved AIS performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".