Kazim Ali

Bio: Kazim Ali is an academic researcher. The author has contributed to research in topics: Deep learning & Image (mathematics). The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Otomycosis: an overview in hyderabad karnataka region

Abstract: Otomycosis is a common clinical problem in Hyderabad Karnataka region because of the hot, humid climate of this region. The infection can be diagnosed clinically on the basis of symptoms like itching, otalgia, discharge, blockage, hearing loss and presence of debris resembling wet blotting paper appearance in the external auditory meatus. It is common in males and occurs more in the 21- 30 year age range. The most common symptoms in our review were itching followed by hearing loss, otorrhoea and blockage. Otomycosis was predominantly unilateral with left ear affected more. The species of fungus causing the disease in our center is Aspergillus Niger. Drugs like clotrimazole/ lidocaine, acetic acid hydrochloride (eardrops) or Gentian violet are indispensible topical agents in the management of otomycosis. Gentian violet should only be used as treatment of last resort because it discolors the external auditory canal giving a poor cosmetic appearance during treatment.

Deep Image Restoration Model: A Defense Method Against Adversarial Attacks

Abstract: These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely affects the performance or prediction. In this scenario, we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly again. We proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results evidence. We have used MNIST and CIFAR10 datasets for experiments and analysis of our defense method. In the end, we have compared our method to other state-ofthe-art defense methods and proved that our results are better than other rival methods.

Run Away from History

Abstract: Run Away from History Kazim Ali (bio) I’m at the tail end of this conversation but also at the tail end of history. Fanny Howe says five Black boys on a corner (any corner) are “runaways from history.” Meaning they are still enslaved. Still a slave. Tongo Eisen-Martin says, “I am arrested all the time for nothing.” And yet there is no white crime in America, statistically speaking, no police crime. When a dog wagged its tail and approached Christian Cooper in Central Park a white woman began screaming. That is the mythic beginning of the end for a Black man: a white woman screaming. It doesn’t matter what laws apply to his body because Blackness is the first law. It happened exactly when George Floyd was expiring. I thought of his breathlessness when I read about Amy Cooper screaming into her phone in Central Park. “Cooper,” every newspaper found it necessary to mention, “is of no relationship to Cooper.” Floyd being pressed from breath is what Cooper in her brutal imagination wanted to happen to Cooper. Fanny is the mother of a novelist and a designer, mother-in-law of another novelist and a poet, aunt to a novelist, sister to a poet, daughter of an actress and a Pink Boy, mentored by a playwright called Beckett, mentor to an aspirant named Ali, who followed her from the Vineyard to the Western shore, as did the characters in her novels, obsessed also with understanding race in America, trying to understand what is the worth of a person. I saw Tongo Eisen-Martin recite his poetry twice, and each time—for twenty minutes or a half hour—he recited off the page, only from memory, no, not from memory, membery, but from breath. In breath his lines arose. I kept checking against the page because I thought he might be engaging in a more ancient practice of inspiration, but no, not that: the words were as inscribed. [End Page 139] What words inscribe laws on bodies? Old Western ones that began with the representation of wealth: value placed upon labor and bodies required to transfer wealth. But from whom to whom? One Cooper was looking for birds. But in Black life in America the most ordinary event brushes up against death. [End Page 140] Kazim Ali Kazim Ali is the author of numerous books of poetry, fiction, nonfiction, and cross-genre work. He is professor and chair of the Department of Literature at the University of California San Diego. Copyright © 2022 State University of New York

Automated Registration of Multiangle SAR Images Using Artificial Intelligence

Abstract: Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.

A microbiological study of otomycosis

Abstract: Aim of the study: To determine the fungi causing otomycosis and the associated bacterial pathogens in a rural set up in clinically suspected cases of otomycosis. Methodology: Ear swabs were collected from 100 clinically suspected cases of otomycosis and from 50 persons who were apparently healthy. All the samples were processed by direct microscopy of KOH mount and Gram staining. Cultures were carried out for both fungal and bacterial isolates, which were identified by standard procedures. The data were statistically analysed. Results: Among 150 samples, 51.3% yielded only fungal growth, 23.3% grew bacteria only and 19.3% showed mixed growth of fungi and bacteria. Major fungal isolates were Aspergillus spp. and Candida spp. One sample grew A. sydowii which is an uncommon agent causing otomycosis. Major bacterial isolates were Staphylococcus aureus and Pseudomonas spp. All pathogenic bacteria were sensitive to routinely used antibiotics. Samples taken from healthy persons did not yield any fungal or bacterial pathogens. Keywords: Otomycosis, Aspergillus spp. Candida spp

Otomycosis treated with Nufookh (Insufflation) therapy in Unani system of medicine: A case report

Abstract: Otomycosis is a fungal infection of external auditory canal characterized by itching, pain, sensation of ear blocking, impaired hearing and/ or ear discharge. Two diagnosed cases of otomycosis were treated with nufookh (insufflation) therapy at E.N.T. OPD, HSZH Govt. Unani Medical College, Bhopal, Madhya Pradesh, India in 2017. Previously, the both patients were being treated with different allopathic antifungal ear drops and got temporary relief. The nufookh (insufflation) therapy of a compound Unani formulation, containing Mur makki, Kundur, Suhaga, Rasout and Phitkari was used for the treatment of both cases and showed significant result. It is recommended that this cost effective therapy with no reported adverse effect can be used for the treatment of uncomplicated cases of otomycosis.

To study the prevalence & clino-mycological profile of Otomycosis in Saurashtra & Kutch region of Gujarat

Abstract: Background: Otomycosis is a superficial, sub-acute or chronic infection of the external auditory canal. It is worldwide in distribution with a higher prevalence in the hot, humid, and dusty areas of the tropics and subtropics. A wide variety of fungi can cause Otomycosis. Objectives: To determine the demographic profile & spectrum of fungi involved in Otomycosis in saurashtra & Kutch region of Gujarat. Materials and Methods: A retrospective study of 101 clinically suspected cases of otomycosis were conducted. The sample are taken by two sterile cotton swabs comprised of secretions, pus and debris from the external auditory canal. Direct microscopic examination by 10% KOH, Gram stain & cuiture on Sabouraud Dextrose Agar medium was done. Identification of fungi done by lacto phenol cotton blue, GERM TUBE TEST & growth on CORNMEAL AGAR and Hichrome Media ysed for species identification. Result: Out of 101 cases, 76(75.24%) were Culture Positive and 74(73.26%) were KOH positive. Out of 74 KOH positive, 65(87.83%) cases were culture positive. Predominantly males (52.6%) were affected. The most affected age group is 31-40 yrs of age (23.68%). Aspergillus niger (52.63%) was the predominant species isolated followed by Aspergillus flavus (36.84%), Candida spp, Penicillium spp. & Rhizopus spp. The disease is predominantly unilateral. Conclusion: Otomycosis should be suspected clinically and should be confirmed by laboratory diagnosis to prevent unnecessary use of antibiotics & there by antibiotic resistance. Mycological diagnosis is important to differentiated from bacterial infection, since symptoms (pruritus, otalgia, otorrhea and hypacusis) are not specific. Keywords: Aspergillus, Otomycosis.

Clinical and Microbiological Epidemiology of Otomycosis in the Centre Region of Cameroon

Abstract: Otomycosis is a superficial, sub-acute or chronic infection of the external auditory canal, characterized by pruritis, inflammation, pain and itching commonly seen in tropical and subtropical regions of the world. Various host and environmental factors can predispose a person to otomycosis. However, a clinical presentation along with otoscopic observations of the patients shows fungal and bacterial infections. Proper identification of causative agents is necessary in order to prevent recurrences and complications such as hearing lost. The aim of our study was to determine the fungi and bacteria pathogens causing otomycosis and to derive association of risk factors with otomycosis of the clinically diagnosed patients. A descriptive cross-sectional study was conducted in the otorhinolaryngology department at the University Teaching hospital and the Central hospital over a period of one year. A total of 250 clinically diagnosed patients of otomycosis of age above one year were included in the study. We evaluated age and sex distribution, predisposing factors and complaints of the clinically diagnosed patients for otomycosis. All samples collected were transported and evaluated by both direct microscopic examination and culture method for bacteria and fungi examination, which were identified by standard procedures. Among 250 samples, 46.22% yielded fungal growth, 21.33% grew bacteria only and 32.44% showed mixed growth of fungi and bacteria. Major fungal isolates were Aspergillus (n=121) including 75 isolates of Aspergillus section Nigri, 20 isolates of Aspergillus section Flavi, 13 isolates of Aspergillus section Fumigati, 8 isolates of Aspergillus section Nudilante and 5 isolates of Aspergillus section Terrei. 48 isolates were identified as Candida species. Major bacterial isolates were Staphylococcus aureus (n=45) followed by Pseudomonas species (n=26), Klebsiella species (n=21), Escherichia coli (n=7) and Proteus species (n=3). This study highlights the highest isolation of Aspergillus section Nigri in cases of clinically diagnosed otomycosis patients at the two reference hospital in Yaounde, Cameroon with high prevalence seen in patients using antibiotic eardrops as a mean of treatment from pains and itching.