Deep Architectures for Content Moderation and Movie Content Rating

Rating a video based on its content is an important step for classifying video age categories. Movie content rating and TV show rating are the two most common rating systems established by professional committees. However, manually reviewing and evaluating scene/film content by a committee is a tedious work and it becomes increasingly difficult with the ever-growing amount of online video content. As such, a desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection in the context of content moderation and movie content rating. The project page is available at https://github.com/fcakyon/content-moderation-deep-learning

Instagram Fake and Automated Account Detection

Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predictions systems, and unhealthy social network environment. This study is related with the detection of fake and automated accounts which leads to fake engagement on Instagram. Prior to this work, there were no publicly available dataset for fake and automated accounts. For this purpose, two datasets have been published for the detection of fake and automated accounts. For the detection of these accounts, machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines and Neural Networks are applied. Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is proposed to handle the unnatural bias in the dataset. To deal with the unevenness problem in the fake dataset, Smote-nc algorithm is implemented. For the automated and fake account detection datasets, 86% and 96% classification accuracies are obtained, respectively

Biological Treatment of Hydraulic Fracturing Produced Water

Hydraulic fracturing enables the enhanced recovery of hydrocarbons from shale formations while generating large volumes of produced water, i.e. wastewater from hydraulic fracturing. Treatment of produced water for reuse or final disposal is challenged by both high salinity and the presence of organic compounds. This dissertation is focused on the biological treatment of produced water using a mixed-culture biofilm approach to remove the available electron donors and therefore, potentially limit microbial growth, biocide use, and fouling of wells (during reuse) and membranes (during treatment prior to final disposal). Conventional activated sludge treatments are intolerant of high salinity, thus a biofilm approach was proposed to provide a more robust treatment method for high salinity produced waters. First, a preliminary evaluation on COD biodegradation (as acetate and guar gum) in synthetic and real produced waters was performed. Biodegradation was mainly driven by salinity; however, microbial activity was observed at salt concentrations as high as 100,000 mg/L TDS. Next, the effect of glutaraldehyde (GA), a commonly used biocide in hydraulic fracturing, on biodegradation of organic chemicals that are commonly used in fracturing fluids, is investigated. Results demonstrated that glutaraldehyde can affect the observed lag period and half-lives of the compounds, depending on the compound. Finally, the biodegradation of produced waters were evaluated in seven samples from different wells. Results showed a negative correlation between salinity and biodegradation rates. Moreover, variable biodegradation rates were observed at the same salt concentration. Finally, a Ra-226 biosorption was evaluated in synthetic and real produced waters to determine the efficacy of Ra-226 removal by a halophilic microalga D. salina. This study contributes to the understanding of biological treatment applicability in produced water management. The proposed biofilm approach could further encourage the use of similar approaches in produced water treatment and possibly in other industrial wastewaters containing high salinity and toxic chemicals. The evaluation of the biocide effect on biodegradation can enhance the understanding and accuracy of environmental model predictions for bio-treatment, bio-remediation, and pollution transport. Ultimately, this dissertation will contribute to more sustainable and economical produced water management strategies

Compressive Photon-Sieve Spectral Imaging

We develop a new compressive spectral imaging modality that utilizes a coded aperture and a photon-sieve for dispersion. The 3D spectral data cube is successfully reconstructed with as little as two shots using sparse recover

PERSISTING THROAT PAIN AFTER COVID-19 INFECTION: A CASE REPORT OF SUBACUTE THYROIDITIS

Case Report: A 25-year-old female patient was admitted to the family medicine clinic with complaints of sore throat, fever, and right ear pain that persisted for 15 days despite receiving ant biotherapy treatment. In her medical history, she had a COVID-19 infection 30 days ago. The thyroid examination revealed painful palpation with a slight increase in the size of the thyroid gland. The patient's blood tests and thyroid ultrasonography were evaluated as subacute thyroiditis.Although it is impossible to establish a definite causal relationship between COVID-19 and subacute thyroiditis in this case, we think such a relationship is possible.Since thyroid-related diseases should also be considered in case of sore throat that persists despite treatment and thyroid examination should not be skipped during a physical examination, it is deemed appropriate to present this case with literature.A 25-year-old female patient was admitted to the family medicine clinic with complaints of sore throat, fever, and right ear pain that persisted for 15 days despite receiving ant biotherapy treatment. In her medical history, she had a COVID-19 infection 30 days ago. The thyroid examination revealed painful palpation with a slight increase in the size of the thyroid gland. The patient's blood tests and thyroid ultrasonography were evaluated as subacute thyroiditis. Although it is impossible to establish a definite causal relationship between COVID-19 and subacute thyroiditis in this case, we think such a relationship is possible.  Since thyroid-related diseases should also be considered in case of sore throat that persists despite treatment and thyroid examination should not be skipped during a physical examination, it is deemed appropriate to present this case with literature

DroBoost: An Intelligent Score and Model Boosting Method for Drone Detection

Drone detection is a challenging object detection task where visibility conditions and quality of the images may be unfavorable, and detections might become difficult due to complex backgrounds, small visible objects, and hard to distinguish objects. Both provide high confidence for drone detections, and eliminating false detections requires efficient algorithms and approaches. Our previous work, which uses YOLOv5, uses both real and synthetic data and a Kalman-based tracker to track the detections and increase their confidence using temporal information. Our current work improves on the previous approach by combining several improvements. We used a more diverse dataset combining multiple sources and combined with synthetic samples chosen from a large synthetic dataset based on the error analysis of the base model. Also, to obtain more resilient confidence scores for objects, we introduced a classification component that discriminates whether the object is a drone or not. Finally, we developed a more advanced scoring algorithm for object tracking that we use to adjust localization confidence. Furthermore, the proposed technique won 1st Place in the Drone vs. Bird Challenge (Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques at ICIAP 2021)

Marine Carotenoids: Biological Functions and Commercial Applications

Carotenoids are the most common pigments in nature and are synthesized by all photosynthetic organisms and fungi. Carotenoids are considered key molecules for life. Light capture, photosynthesis photoprotection, excess light dissipation and quenching of singlet oxygen are among key biological functions of carotenoids relevant for life on earth. Biological properties of carotenoids allow for a wide range of commercial applications. Indeed, recent interest in the carotenoids has been mainly for their nutraceutical properties. A large number of scientific studies have confirmed the benefits of carotenoids to health and their use for this purpose is growing rapidly. In addition, carotenoids have traditionally been used in food and animal feed for their color properties. Carotenoids are also known to improve consumer perception of quality; an example is the addition of carotenoids to fish feed to impart color to farmed salmon

Mapping artificial intelligence adoption in hepatology practice and research: challenges and opportunities in MENA region

BackgroundArtificial intelligence (AI) is increasingly relevant to hepatology, yet real-world adoption in the Middle East and North Africa (MENA) is uncertain. We assessed awareness, use, perceived value, barriers, and policy priorities among hepatology clinicians in the region.MethodsA cross-sectional online survey targeted hepatologists and gastroenterologists across 17 MENA countries. The survey assessed clinical and research applications of AI, perceived benefits, clinical and research use, barriers, ethical considerations, and institutional readiness. Descriptive statistics and thematic analysis were performed.ResultsOf 285 invited professionals, 236 completed the survey (response rate: 82.8%). While 73.2% recognized the transformative potential of AI, only 14.4% used AI tools daily, primarily for imaging analysis and disease prediction. AI tools were used in research by 39.8% of respondents, mainly for data analysis, manuscript writing assistance, and predictive modeling. Major barriers included inadequate training (60.6%), limited AI tool access (53%), and insufficient infrastructure (53%). Ethical concerns focused on data privacy, diagnostic accuracy, and over-reliance on automation. Despite these challenges, 70.3% expressed strong interest in AI training., and 43.6% anticipating routine clinical integration within 1–3 years.ConclusionMENA hepatologists are optimistic about AI but report limited routine use and substantial readiness gaps. Priorities include scalable training, interoperable infrastructure and standards, clear governance with human-in-the-loop safeguards, and region-specific validation to enable safe, equitable implementation