Background: planter fasciitis is a common condition
seen in adults and sport men, it is characterized by dull
pain in the heel, especially when getting up and
standing on the foot in the morning or after sitting for a
long time.
Recently low level laser therapy is used as a method of
treatment.
Objective: to evaluate the benefit of laser therapy in
treatment of planter fasciitis.
Methods: Out of twenty five patients with planter
fasciitis exposed to laser therapy. Laser used is (diode
type) given in two cessions per week for four weeks,
time for each cession is about (12 minutes).
Results: complete recovery seen in (32%) of patients,
moderate improvement in (16%), mild improvement in
(24%), no response in (28%), deterioration (0%).
Conclusion: laser therapy is safe for treatment of
planter fasciitis, free of side effects, very effective as
an alternative to surgery and local steroid injection,
with no deterioration in the condition during or after
laser therapy
Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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