Yazar "Farhan, Hameed Mutlag" seçeneğine göre listele

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  • Küçük Resim
    Yayın
    Modeling and optimization of NLOS underwater optical channels using QAM-OFDM technique
    (Multidisciplinary Digital Publishing Institute (MDPI), 2026) Hamdullah, Noor Abdulqader; Çevik, Mesut; Farhan, Hameed Mutlag; Duru, İzzet Paruğ
    Due to increasing human activities underwater, there is a growing demand for high-speed underwater optical communication (UOWC) data links for security surveillance, environmental monitoring, pipeline inspection, and other applications. Line-of-sight communication is impossible under certain conditions due to misalignment, physical obstructions, irregular usage, and difficulty adjusting the receiver orientation, especially when used in environments with mobile users or submerged sensor networks. Therefore, non-line-of-sight (NLOS) optical communication is used in this study. Advanced modulation schemes—quadrature amplitude modulation and orthogonal frequency-division multiplexing (QAM-OFDM)—were used to transmit the signal underwater between two network nodes. QAM increases the data transfer rate, while OFDM reduces dispersion and inter-symbol interference (ISI). The proposed UOWC system is investigated using a 532 nm green laser diode (LD). Reliable high-speed data transmission of up to 15 Gbps is achieved over horizontal distances of 134 m, 43 m, 21 m, and 5 m in four different aquatic environments—pure water (PW), clear ocean (CLO), coastal ocean (COO), and harbor II (HarII), respectively. The system achieves effectively error-free performance within the simulation duration (BER < 10−9), with a received optical signal power of approximately −41.5 dBm. Clear constellation patterns and low BER values are observed, confirming the robustness of the proposed architecture. Despite the limitations imposed by non-line-of-sight (NLOS) communication and the diversity aquatic environments, our proposed architecture excels at underwater long-distance data transmission at high speeds.
  • Kapalı Erişim
    Yayın
    VEM-IDSs: enhancing an ensemble learning method of voting approach for binary classification of intrusion detection system
    (Springer Science and Business Media Deutschland GmbH, 2026) Awad, Omer Fawzi; Çevik, Mesut; Farhan, Hameed Mutlag; Kocakoyun Aydoğan, Şenay
    In the realm of cybersecurity, the responsibility for protecting information and data for the Internet of Things is significant, for the prevention of intrusiors from using the data by detecting the vulnerabilities, improve the mechanism, and developing strategies for protecting IoT networks to repel attacks. Intrusion Detection Systems (IDSs) are one of the strategies that can monitor the network to protect data. Machine Learning algorithms are used to increase IDS efficiency and achieve high security. Our study: conducts experiments using ensemble voting (Soft and Hard) classification to detect and classify data in IoT. Our experiments contain two stages. First, build models for three algorithms (K-Nearest Neighbor (KNN), Logistic Regression (LR), and Random Forest (RF). Second, building an ensemble voting classifier consisting of three algorithms. For the two stages, we proposed Edge-IIoTset dataset to classify attacks as benign or malicious, (RF_LR) models result of soft voting have a high accuracy.