http://hdl.handle.net/20.500.12323/606 2026-09-30T14:25:21Z 2026-09-30T14:25:21Z Akram, Hafiz Muhammad Azeem Talatahari, Siamak Jhangeer, Adil http://hdl.handle.net/20.500.12323/8372 2026-09-25T05:51:24Z 2026-09-19T00:00:00Z

Title: Engineering audit protocol for trustworthy electricity-theft detection on non-stationary smart-meter data Authors: Akram, Hafiz Muhammad Azeem; Talatahari, Siamak; Jhangeer, Adil Abstract: Electricity theft is the dominant non-technical loss in advanced metering infrastructure, threatening smart-grid reliability and utility revenue worldwide. Despite a proliferation of deep-learning detectors, the data on which they are evaluated is rarely audited for leakage; undetected leakage in the evaluation pipeline can inflate the performance these detectors report. This paper reframes electricity-theft detection as a critical-infrastructure integrity problem and proposes a model-agnostic engineering audit that exposes such inflation before deployment. The audit applies four validation gates: a data-provenance audit, a temporal integrity check, a leakage detection gate, and a realistic distribution-shift test; results are reported under a three-tier disclosure standard of full population, stable regime, and audited cohort, with a 12-item checklist. On the State Grid Corporation of China dataset, whose missingness exhibits a 41.44 percentage-point structural break in a single month at January 2016, six of eight architectures lose 28.5% to 32.6% of their reported precision-recall area under the curve (PR-AUC; absolute drop 0.109 to 0.134) once the shortcut is removed. Customer-grouped split leakage is operationally negligible (maximum |ΔPR-AUC| = 0.0035), and an audit reveals detection reliability varies by a factor of seven across 22 subgroups. A permutation-importance probe identifies consumption scale and variability computed over observed days, together with a direct contribution from the missingness rate, as the signals correlated with the regime and available to every detector, indicating the likely mechanism of the inflation. The gap between reported and audited performance gives utilities a verification step that current reporting practice does not provide.

2026-09-19T00:00:00Z Huseynova, Humay http://hdl.handle.net/20.500.12323/8347 2026-08-24T07:01:26Z 2019-01-01T00:00:00Z

Title: Twitter Social Network Data Mining in Healthcare Authors: Huseynova, Humay Abstract: Social media offers a wide range of opportunities to share information. In recent years transparency and open sharing via social media have significantly increased, hence, social media and healthcare became of interest among researchers, physicians, and doctors. Twitter is playing a significant role among other social media types because of its usability, so we can say that its use in healthcare is undeniable. It means that it is possible to see how much Twitter is capable to make sense in solving health problems. The main purpose of my research is to analyze in what capacity twitter is useful for healthcare, diseases, and treatments by using needed twitter data. I am going to use twitter mining techniques with simple Python codes to analyze tweets, to study the nature of sharing public health information on Twitter and to make a prediction for Azerbaijan environment by getting a reasonable conclusion. Description: School of Science and Engineering Major: 266147 - Software of Computer Systems and Networks Supervisor: Dr. Mahammad Sharifov Advisor: Behnam Kiani

2019-01-01T00:00:00Z Karakulak, Ertugrul Paralı, Levent Koç, Muhterem Tatardar, Farida Sarı, Ali Mevsim, Ersoy Fataliyeva, Valida http://hdl.handle.net/20.500.12323/8133 2025-10-21T06:25:57Z 2025-10-15T00:00:00Z

Title: Enhanced piezoelectric performance of multilayered piezoelectric nanogenerator based on the PVDF/PZT/graphene Electrospun for IoT-based remote monitoring Authors: Karakulak, Ertugrul; Paralı, Levent; Koç, Muhterem; Tatardar, Farida; Sarı, Ali; Mevsim, Ersoy; Fataliyeva, Valida Abstract: This research focused on improving the performance of piezoelectric nanogenerators by utilizing a piezoelectric nanogenerator (PNG) design that combines stacked piezoelectric electrospun nanofibers with conductive layers placed between them. A polyvinylidene fluoride (PVDF)/lead zirconate titanate (PZT)/unmodified graphene nanoplatelet (GNP) based multilayered structure (MLS) was produced as a parallel connection using a layer-bylayer assembly technique. At a vibrational frequency of 20 Hz, under a resistance load of 50 kΩ, the four-layered PNG reached an open-circuit voltage of 0.18 V(VRMS), a maximum electrical power of 0.166 µW (PRMS) by drawing a current of 1.82 µA (IRMS). The four-layered PNG, which exhibits high capacitance and low impedance characteristics, has increased the full charging voltage (3.96 V) to 80% compared to a single-layered PNG (2.2 V). Furthermore, the electrical power obtained from the four-layered PNG was approximately 4.38 times higher than the single-layered one. The resulting multilayered PNG (M-PNG) can be utilized effectively in self-powered wireless e-health systems for detecting human movement.

2025-10-15T00:00:00Z Ojaghi, Javid Nuriyeva, Sevinj Salayeva, Samira Eldarov, Mahammad Akparov, Zeynal http://hdl.handle.net/20.500.12323/7960 2025-06-04T08:36:41Z 2025-04-21T00:00:00Z

Title: Allelic diversity of Azerbaijani bread wheat (Triticum aestivum L.) by SSR markers Authors: Ojaghi, Javid; Nuriyeva, Sevinj; Salayeva, Samira; Eldarov, Mahammad; Akparov, Zeynal Abstract: The objectives of this research entailed studying genetic variation in 50 Azerbaijani wheat accessions from 6 different botanical varieties using simple sequence repeat (SSR) markers. On the basis of the seven SSR primers used in this work, 42 different alleles were observed among the wheat accessions studied with an average of six alleles per locus. Polymorphism information content (PIC) ranged from 0.428 to 0.772, revealing the existence of rich genetic diversity in Azerbaijani wheat accessions. The highest PIC values were calculated with the Xgwm190, Xgwm337, and Xgwm261 SSR primers with a mean value of 0.561. Cluster analysis representing the Nei genetic distance index among all samples divided the genotypes into nine separate groups. The ninth cluster included 12 genotypes, accounting for 24% of all genotypes analyzed. This group also included var. erythrospermum 3 and var. erythroleucon 9, which could not be distinguished based on the seven microsatellite markers; this may be due to their sharing of a similar genetic background. Samples of var. milturum botanical varieties were located at notable genetic distances from the other studied samples. These findings clearly indicate that SSR analysis can be used as a powerful tool for estimating the genotypic similarities, genetic diversity, and fingerprinting of Azerbaijani local wheat varieties.

2025-04-21T00:00:00Z