Background: In video forgeries, the insertion, duplication and deletion of frames are the most common forgeries that are used by attackers to alter targeted videos for malicious intent. Researchers have proposed the use of active and passive technologies for detecting video forgeries over the years. Active approaches are used to detect the occurrence of alterations in digital video with the use of embedded features such as digital signature and watermarks. However, techniques that are based on active approaches are only applicable to specialized hardware devices. A passive technique, on the other hand, detects forgery using the behavioral cues encoded in a video. In this paper, a passive video forgery detection system based on frame similarity analysis is presented.Inter frame forgeries (Insertion, Deletion, and Duplication) were detected using the proposed technique, which was unaffected by scene changes.The technique has the overall performance of 98.07% precision, 100% recall and 99.01% accuracy.

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Call admission control (CAC) is one of the radio resource management techniques that regulates and provide resources for new or ongoing calls in the network. The existing CAC schemes wastes bandwidth due to its failure to check before degrading admitted real-time calls and it also increases the call dropping probability (CBP) and calling blocking probability (CBP) of real-time calls due to the delay incurred when bandwidth is degraded from them. This paper proposed an enhanced adaptive call admission control (EA-CAC) scheme with bandwidth reservation. The scheme employs a prior-check mechanism that ensured bandwidth to be degraded will be enough to admit the new call request. It further incorporates an adaptive degradation mechanism that degrades non-real time calls before degrading the RT calls. The performance of the EA-CAC scheme was evaluated against two existing schemes using Vienna LTE system level simulator. The EA-CAC scheme exhibits better performance compared to the two schemes in terms of throughput, CBP, and CDP of RT calls without sacrificing the performance of NRT calls.

Collaborative filtering recommender system suffers from data sparsity problem due to its reliance on numerical ratings to provide recommendations to users. This problem makes it difficult for the system to compute accurate similar neighbours for the items and provide good quality recommendations. Existing methods fail to pre-process the missing ratings of the new items and to predict cold items to the active users which lead to poor quality recommendations. In this work, a sparsity reduction method is presented to improve the quality of recommendations. The method utilises Bi-Separated clustering algorithm to cluster the ratings matrix simultaneously into users and items bi-clusters based on ratings classification. It also employs Bi-Mean Imputation algorithm to fill the missing ratings in the bi-clusters using the estimated means. The method then performs the traditional collaborative filtering process on the new rating matrix for cold items prediction. The experimental results demonstrated that compared to the existing method, the proposed BiSCBiMI improves density of the rating matrix by 5.75%, 10.73% and 7.35% as well as Mean Absolute Error (MAE) of the new items prediction for all of the considered datasets. The results indicated that, the proposed approaches are effective in reducing the data sparsity problem as well as items prediction, which in turn returns good quality recommendations.