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professionalowl · 5 months ago

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@astriiformes tagged me to post about the books I have on my to-be-read list for 2025, and I promised myself I'd get through at least twelve of them, so here's a selection of the "at least twelve" I want to get through. I'm a habitual non-fiction reader because when I was 12 I hated everything "written for twelve year olds" and switched my focus to pop science (and to a lesser extent history) instead, so I am, consequently, much better at judging and picking up quality non-fiction than I am finding and reading fiction. I'm trying to spice it up by adding some (science...) fiction to the list this year, as I did last year.

I'm currently reading the very last one, Selfish Genes to Social Beings, and am hoping (in vain?) to get it done before I get back to university on Sunday. Currently stalling on chapter 6 of 13-or-so, so probably not. (It doesn't help that like...see...I've been to the optician four times in the past three weeks...and I've come away with a new prescription three of those times, with more on the way...and on top of all that, I have a "dissertation to draft" or something. It's hard.) It's very readable, though, I'd recommend it.

Tagging: @magiefish, @t4tbruharvey, @specialagentartemis and...whoever else I've interacted with who has a long list of things to read on their plate.

Detailed exposition below the cut, because one of my other goals is to get used to expositing more frequently and more clearly:

Fiction:

On The Origin Of Species and Other Stories (Bo-Young Kim, 2021) - I was looking for short story collections to try and get back into the habit of reading at lunchtime, and a user I follow recommended this one. The theme is, roughly, "posthumanist stories about evolution" and it's great so far - I've only read the first one, but. Like. (Staring out over the water) Man.

Children of Ruin (Adrien Tchaikovsky, 2019) - I really liked Children of Time, which is about artifically-evolved hyperintelligent spiders colliding with the descendants of the dying civilisation that accidentally created them while trying to force-evolve servile primates. They make a computer out of ants and store a person on it. It absolutely ruled the whole way through, so obviously I have to read the sequels - I hear this one is about octopi, but I'm about 12 pages in, so who can say.

Absolution (Jeff VanderMeer, 2024) - again, I finished Annihilation, Authority, and Acceptance last summer, specifically because I discovered he was dropping a new one, and really enjoyed them. I think my sibling has made off with this one for now, though.

Non-fiction:

Planetary Social Thought: The Anthropocene Challenge to the Social Sciences (Nigel Clark and Bronislaw Szerzynski, 2021) - my best friend got me this for my birthday. I've read a small chunk of the literature on the Anthropocene, and the concept succeeds and fails in ways I find really interesting, so I'll happily read an entire book about it.

The Serviceberry (Robin Wall Kimmerer, 2024) - I've been meaning to read Braiding Sweetgrass since forever, and the economic angle of this one really intrigues me - I've encountered similar ideas in the course of reading anthropology, but one of my missions for this year is to start reading indigenous authors directly instead of just letting anthropologists paraphrase them, so I'd like to get a hold of a copy at some point.

Rebirding: Restoring Britain's Wildlife (Benedict Macdonald, 2019) - I read the first chapter of this in Year 13, which was about the history of Britain's relationship with its wildlife, and it was horrifying. I must read more.

The Museum of the Wood Age (Max Adams, 2022) - my brother gave this to me for Christmas and it looks awesome. It's about wood technology - "basic devices" like screws, levers, and wheels, as the blurb calls them - and its adaptability. I'm always a fan of flipping the script on quote-unquote "basic technologies" and simplicity/complexity is a favourite issue of mine, so I'm quite excited about this one!

The History of Magic: From alchemy to witchcraft, from the Ice Age to the present (Chris Gosden, 2020) - picked it up at a local bookshop for £6 (a steal) last Christmas because it was there, started reading it, got distracted, never made it past the second chapter. What I did read was very good, as is everything else I've read by Chris Gosden - albeit very broad in scope, so it'll be interesting to see what he chooses to cover! Probably one for after I graduate in June. Most of these are for after I graduate in June.

Black Spartacus: The Epic Life of Toussaint Louverture (Suhir Hazareesingh, 2020) - another book I got halfway through and then dropped when school started again, way back in 2021. I distinctly remember that it was impressively well-written and engaging, and I'm not usually one for biographies, but the guy is really compelling, as are the details Hazareesingh includes on the specifics of the Haitian Revolution and the links between revolutionary action and Haitian culture. It's high on my "HAVE to finish this at some point" list.

What An Owl Knows (Jennifer Ackerman, 2023) - Ackerman's The Genius of Birds had a massive impact on me and the way I thought about intelligence (human and animal) when I was like, 14, and I was a big fan of the half of the sequel The Bird Way I managed to get through in 2020. I've always been a fan of making the self-deprecating joke "I call myself Owl online because I appear to be wise but am actually very stupid," and this book seems like it's trying to swing the pendulum back in the other direction - so, of course, I must read it.

Are We Smart Enough To Know How Smart Animals Are? (Franz de Waal, 2016) - relatedly, I've read a lot of cognitive archaeology over the course of my degree, much of which plays on the same things I found interesting about The Genius of Birds, but it's obviously hominid-focused, and where it isn't the lens is mostly on other primates and occasionally some cetaceans and corvids, and it's prone to making sweeping statements about what animals can/can't do. This one came up in a book I read last year, which was also about conceptions of 'intelligence,' and we had a copy lying around, so it's on the list.

Selfish Genes to Social Beings: A Cooperative History of Life (Jonathan Silvertown) - relatedly relatedly, I know a guy who does social cognition and philosophy of nature, and this one partially derives from trying to pick up on those threads, as well as get to grips with the "cooperation versus competition" arena of the philosophy/science of evolution. I'm reading it right now and while I have some nitpicks about the way it talks about human cooperation (of the "reliance on modelling leaves it stripped down and apolitical" variety), the science is very clearly presented and the author is pretty funny.

Assume also that whatever godforsaken iteration of the Skulduggery Pleasant threequels drops next is on this list, as well as a variety of other books I have yet to acquire a copy of. Most of these are recent to very old Christmas/birthday presents, so I'm prioritising the things I have a physical copy of for now.

#i'll put this under #asks

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tutor850 · 11 months ago

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distinguishedfifty · 2 years ago

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Thank you @its-always-silly-season for this academic au idea. This is so fun. I did leave some riders blank bc I’m just unsure. Feel free (anyone) to make additions or what you think past or present riders would be doing

Marc M: statistics professor. Some people think stats is too different to math, but he’s found his way and has convinced many of his undergrad students to consider stats

Alex M: has his MBA, working in the industry as a consultant, but Marc is trying to get him back to academia (Álex doesn’t like the idea of going back to school for 5 years and then fighting it out in academia)

Alex R: art history professor. He thought he found funding for a post doctorate at a uni, but it fell through (they’re cutting the arts). Is now here teaching

Joan: philosophy professor, but is in his 1st year of teaching this university. He came from another uni bc they defunded his department and is trying to find his footing

Taka: architecture professor. Usually around in the architecture building helping his students and trying to tell them to not pull all nighters getting their designs finished

Bezz: Got his bachelors in math and currently on a PhD track. However, is debating on testing out and leaving with his masters

Pecco: in his last year of his PhD, and getting ready to defend his dissertation where he focuses more on differential equations. He’s great at memorizing

Franky: got his phd in philosophy and Vale is trying to get him a professor position in his department. He actually has his undergrad in literature, but loves writing even more

Luca: got his bachelors in computer science and is getting his masters in computer science. Debating on getting his phd in computer science since he likes the idea of working in the industry

Jack: marine biologist and is known to take his students on wild boat rides. He will take over campus tours when they’re in the marine biology building

Brad: a professor in the medical school. He used to be an ER doctor and volunteers his summers with Doctors Without Borders. Makes sure every professor gets their flu shot or else he will be visiting them in their office hours

Pol: music professor that focuses on piano. Will take any student and go on a tangent on how special the piano is

Augusto:

Aleix: works in the law school, but has a heavy hand in telling prelaw students to get their law degree here too. Additionally, he is on the faculty senate. One day he will get everyone to unionize

Maverick: contrary to popular belief, Maverick is actually an air traffic controller professor and not an aviation professor. He used to be in the industry, but it’s a very stressful and underemployed industry. Figured he’d make more change in trying to get more air traffic controllers to fix this

Miguel: languages professor that was the first to bring the Portuguese track to this uni

Raúl:

Fabio: getting his phd in statistics. Actually getting ready to make the leap from phd student to PhD candidate

Diggia:

Jorge M: is getting his phd in gender and queer studies. He’s writing his dissertation on gender and queer identities in sports. He petitioned for a safe space to be created on campus and is working on getting a section of the undergrad dorms to be just for students that identify as queer

Johann: music professor and focuses on the guitar. He also is the choir professor

Dani: languages department chair. Since he is the dept chair, he doesn’t teach much, but students would do questionable things to get into his class. Ensures enough funding and resources are available to keep Catalan studies

Jorge L: business department chair. He has his undergrad in psychology, but went on to get his MBA and PhD in international business. Will use a weekend to travel to a different country and observe their business practices

Vale: math department chair. Theoretical math bc he’s insane. You should hear him talk about the 4th dimension

Dovi: psychology professor. Has his masters in counseling and his psyD. He makes sure his students know how taxing it can be to become a therapist. He may or may not still have a practice, but his fellow professors come to him anyways. One day he found a couch in his office and he never asked for it to be moved

#clearly some are more fleshed out than others #but I do my best #tried to also include social sciences and the arts bc duh #also did my best to be a bit specific #academic au #rpf

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religion-is-a-mental-illness · 1 year ago

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By: Aaron Sibarium

Published: Jan 30, 2024

It's not just Claudine Gay. Harvard University's chief diversity and inclusion officer, Sherri Ann Charleston, appears to have plagiarized extensively in her academic work, lifting large portions of text without quotation marks and even taking credit for a study done by another scholar—her own husband—according to a complaint filed with the university on Monday and a Washington Free Beacon analysis.

The complaint makes 40 allegations of plagiarism that span the entirety of Charleston's thin publication record. In her 2009 dissertation, submitted to the University of Michigan, Charleston quotes or paraphrases nearly a dozen scholars without proper attribution, the complaint alleges. And in her sole peer-reviewed journal article—coauthored with her husband, LaVar Charleston, in 2014—the couple recycle much of a 2012 study published by LaVar Charleston, the deputy vice chancellor for diversity and inclusion at the University of Wisconsin-Madison, framing the old material as new research.

Through that sleight of hand, Sherri Ann Charleston effectively took credit for her husband's work. The 2014 paper, which was also coauthored with Jerlando Jackson, now the dean of Michigan State University's College of Education, and appeared in the Journal of Negro Education, has the same methods, findings, and description of survey subjects as the 2012 study, which involved interviews with black computer science students and was first published by the Journal of Diversity in Higher Education.

The two papers even report identical interview responses from those students. The overlap suggests that the authors did not conduct new interviews for the 2014 study but instead relied on LaVar Charleston's interviews from 2012—a severe breach of research ethics, according to experts who reviewed the allegations.

"The 2014 paper appears to be entirely counterfeit," said Peter Wood, the head of the National Association of Scholars and a former associate provost at Boston University, where he ran several academic integrity probes. "This is research fraud pure and simple."

Sherri Ann Charleston was the chief affirmative action officer at the University of Wisconsin-Madison before she joined Harvard in August 2020 as its first-ever chief diversity officer. In that capacity, Charleston served on the staff advisory committee that helped guide the university's presidential search process that resulted in the selection of former Harvard president Claudine Gay in December 2022, according to the Harvard Crimson.

A historian and attorney by training, Charleston has taught courses on gender studies at the University of Wisconsin, according to her Harvard bio, which describes her as "one of the nation's leading experts in diversity." The site says that her work involves "translating diversity and inclusion research into practice for students, staff, researchers, postdoctoral fellows and faculty of color."

Experts who reviewed the allegations against Charleston said that they ranged from minor plagiarism to possible data fraud and warrant an investigation. Some also argued that Charleston had committed a more serious scholarly sin than Gay, Harvard's former president, who resigned in January after she was accused of lifting long passages from other authors without proper attribution.

Papers that omit a few citations or quotation marks rarely receive more than a correction, experts said. But when scholars recycle large chunks of a previous study—especially its data or conclusions—without attribution, the duplicate paper is often retracted and can even violate copyright law.

That offense, known as duplicate publication, is typically a form of self-plagiarism in which authors republish old work in a bid to pad their résumés. Here, though, the duplicate paper added two new authors, Sherri Ann Charleston and Jerlando Jackson, who had no involvement in the original, letting them claim credit for the research and making them party to the con.

"Sherri Charleston appears to have used somebody else's research without proper attribution," said Steve McGuire, a former political theory professor at Villanova University, who reviewed both the 2012 and 2014 papers.

One-fifth of the 2014 paper, including two-thirds of its "findings" section, was published in the 2012 study, according to the complaint, and three interview responses are identical in both articles, suggesting they come from the same survey.

According to Lee Jussim, a social psychologist at Rutgers University, "it is essentially impossible for two different people in two different studies to produce the same quote." At best, he said, the authors got their wires crossed and mixed up interviews from two separate surveys, both of which just happened to involve 37 participants with the exact same demographic profile. At worst, the authors committed data fraud by framing old survey responses as new ones—a separate and more serious offense.

The Journal of Negro Education did not respond to a request for comment. Sherri Ann Charleston, LaVar Charleston, and Jerlando Jackson did not respond to requests for comment.

Monday's complaint, which was filed anonymously, comes as Harvard is facing questions about the integrity of its research affiliates and the ideology of its diversity bureaucrats, most of whom report to the sprawling office that Sherri Ann Charleston oversees.

The Dana-Farber Cancer Institute, one of Harvard Medical School's three teaching hospitals, announced in January that it would retract six papers and correct dozens more after some of its top executives were accused of data manipulation. That news came on the heels of a viral essay in which Carole Hooven, a Harvard biologist, described how she had been hounded out of a teaching role by her department's diversity committee after she said in an interview that there are only two sexes.

The school is also facing an ongoing congressional probe over its handling of anti-Semitism and its response to the plagiarism allegations against Gay, which Harvard initially sought to suppress with legal saber-rattling. Half of Gay's published work contained plagiarized material, ranging from single sentences to entire paragraphs, with some of the most severe lifts coming in her dissertation. Though Gay stepped down as president on January 2, she remains a tenured faculty member drawing a $900,000 annual salary.

Some of Charleston's offenses are similar to Gay's. In her 2009 dissertation, for example, Charleston borrows a sentence from Eric Arnesen's 1991 book Waterfront Workers of New Orleans: Race, Class, and Politics, 1863-1923, without quotation marks and without citing Arnesen's work in a footnote.

She also lifts full paragraphs from her thesis adviser, Rebecca Scott, while making minimal semantic tweaks.

"There's simply not enough difference to consider them original words," said Jonathan Bailey, the founder of the website Plagiarism Today. "Though the sources in those examples are cited"—Charleston includes a footnote to Scott at the end of each passage—"the text either needed to be quoted or properly paraphrased."

Bailey added that the plagiarism of Scott alone merited an investigation—ideally, he said, "by a neutral party with no ties to either the school or the school's critics."

Harvard did not respond to a request for comment. Scott and Arnesen did not respond to requests for comment.

Charleston also lifted language from Louis Pérez, an historian at the University of North Carolina-Chapel Hill; Alejandro de la Fuente, an historian at Harvard; and Ada Ferrer, an historian at New York University, among other scholars.

Charleston cites each source in a footnote but omits quotation marks around language copied verbatim. The omissions violate Harvard's Guide to Using Sources, a document produced for incoming students, which states that quotation marks are required when "you copy language word for word."

Pérez, de la Fuente, and Ferrer did not respond to requests for comment.

The range of examples presented in the complaint, which was also filed to the University of Michigan and the University of Wisconsin-Madison, highlights how plagiarism can shade into more severe forms of misconduct when it involves interviews or other data.

In fact, some experts said the term "plagiarism" didn't quite capture the dishonesty of duplicate publication, which is sometimes categorized as a separate offense and accounts for 14 percent of all paper retractions in the life sciences.

"You cannot just republish an old paper as if it is a new paper," Jussim, the Rutgers psychologist, said. "If you do, that is not exactly plagiarism; it's more like fraud."

Wood said the case was really a combination of the two offenses. "Because the second paper, on which Sherri Ann Charleston is one of the three co-authors, recycles so much of the text of the original paper by LaVar J. Charleston, this does have the earmarks of plagiarism, but the plagiarism is compounded by an even larger effort to deceive," he said. "The universities and journals need to investigate."

While scholars can reuse data across multiple papers, they must make clear when they are doing so and provide appropriate attribution to earlier studies, per guidelines from the Office of Research Integrity and the editorial policies of top academic journals, including Nature and Cell.

But the 2014 paper never indicates that it is reusing research from 2012. Instead, it claims to present new data that fill a "gap" in the literature and "corroborate" the 2012 study, among others, and on two occasions refers to survey subjects as "participants in this study."

Those participants appear to be the same people whom LaVar Charleston interviewed in 2012. Both surveys involved the same number of undergraduates, graduate students, Ph.D.s, and students at historically black colleges—all drawn from the same computer science conference—a similarity that experts said was a red flag.

"It is curious that the proportions are identical," said Debora Weber-Wulff, a German computer scientist who researches plagiarism and other forms of academic misconduct. "This would be grounds for the universities in question to request the data and investigate."

Jussim agreed. "This seems sufficiently improbable that, absent something saying they are re-reporting an already-published study, it would be fraud," he said.

LaVar Charleston did not respond to a request for comment about whether the two studies used the same interviews. The University of Michigan said it was "committed to fostering and upholding the highest ethical standards in research and scholarship," but declined to comment on the complaint. The University of Wisconsin-Madison told the Free Beacon it had "initiated an assessment in response to the allegations."

The main difference between the papers is a long section in the 2014 article about "culturally responsive pedagogy theory," which the authors say their findings support. Both articles are littered with the tropes of progressive scholarship, including a disclaimer about "positionality"—the authors assure readers that they reflected on their own "racial, gender, and socioeconomic status"—and a lament that computer science is a "White male-dominated field."

Both also criticize the idea that "computing sciences is for nerds, only for White people, [and] only for geniuses."

Such language is typical of the diversity initiatives Charleston oversees. Since 2020, her office has pumped out a stream of materials that bemoan the "weaponization of whiteness," discuss the ins and outs of "white fragility," and urge students to "call out" their peers for "harmful words." One message, signed by Charleston herself, was titled "A Call to Dismantle Intersecting Oppressions."

"We must continue to work against systematic oppression in all its forms—racism, sexism, homophobia, ableism, and more," she wrote.

Her office also curates resources for students seeking to become fluent in progressive patois, including a "glossary of diversity, inclusion and belonging (DIB) terms" that provides examples of "gaslighting."

Tactics can include "shooting down the target's ideas," the entry reads—or "taking credit for them."

Here we go again...

If you haven't already figured it out, the DEI-related faux-"disciplines" - the "Studies": Ethnic Studies, Women's Studies, Gender Studies, Postcolonial Studies, Media Studies, etc - are the most corrupt, the most ideological with the absolute lowest academic standards of all. All they care about is echoing back the "correct" opinions, not valid scholarship.

And yet, somehow these lunatics and fanatics end up the most powerful people in the asylum.

Harvard needs to fumigate the house, top to bottom.

#Aaron Sibarium #Sherri Ann Charleston #Claudine Gay #Harvard #Harvard University #academic corruption #DEI #DEI bureaucracy #diversity officer #chief diversity officer #diversity equity and inclusion #diversity #equity #inclusion #plagiarism #academic fraud

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ahecworld · 2 years ago

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tutorsindia152 · 18 days ago

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honorarydoctorate · 23 days ago

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How to Pursue a PhD in Chemistry: A Complete Guide

Pursuing a PhD in Chemistry is a major academic and professional milestone. Whether your goal is to contribute to cutting-edge research, enter academia, or advance in the pharmaceutical or chemical industries, a doctorate equips you with the skills and credibility to thrive. Here's a complete guide to help you understand the process from start to finish.

Understand What a PhD in Chemistry Entails

A PhD in Chemistry is a research-focused degree that typically takes 4–6 years to complete. It involves advanced coursework, comprehensive exams, and a major original research project culminating in a dissertation. Specializations can include:

Organic Chemistry

Inorganic Chemistry

Physical Chemistry

Analytical Chemistry

Biochemistry

Materials Science

Theoretical/Computational Chemistry

You will work under the guidance of a supervisor, contributing new knowledge to your field and publishing in peer-reviewed journals.

2. Meet the Basic Eligibility Criteria

Most PhD programs require:

A Master’s degree (M.Sc.) in Chemistry or a closely related field

Strong academic performance (typically a GPA of 3.0/4.0 or equivalent)

Letters of recommendation

Statement of purpose (SOP) outlining research interests

Research experience (preferred, but not always required)

Some universities may accept exceptional candidates with just a Bachelor’s degree, especially in the U.S.

3. Choose Your Area of Research Interest

Before applying, identify the area of chemistry you're most passionate about. This helps you:

Narrow down suitable programs

Find potential supervisors whose research aligns with your interests

Write a compelling SOP tailored to specific departments

Review recent papers, attend webinars, or speak with current PhD students to gain clarity.

4. Prepare for Entrance Exams

Depending on your location and target universities, you may need to take standardized tests:

GRE (Graduate Record Examination) – Required by some U.S. universities

TOEFL/IELTS – For non-native English speakers applying to international programs

CSIR-NET/GATE – Required for admission or funding in India

Check individual university requirements carefully.

5. Apply to the Right Universities

Shortlist institutions based on:

Research output

Faculty expertise

Funding opportunities

Lab facilities

Industry connections

Applications generally include academic transcripts, test scores, SOP, resume/CV, and letters of recommendation. Most deadlines fall between November and February for Fall admission.

6. Secure Funding

PhD programs often offer financial support through:

Teaching assistantships (TA)

Research assistantships (RA)

Fellowships or scholarships

Make sure to apply for external funding as well (e.g., UGC-NET JRF in India, NSF fellowships in the U.S., or DAAD in Germany).

7. Interview and Acceptance

Some programs may conduct interviews to assess your fit with the department and potential advisor. Be prepared to discuss:

Your academic background

Research interests

Long-term goals

Why you chose their program

Once accepted, you'll receive an offer letter outlining funding, expectations, and start date.

8. Begin the Program

Your PhD journey typically involves:

Coursework (1–2 years): Advanced classes in chemistry and research methods

Comprehensive exams: To assess your readiness for research

Research proposal: Defining the scope and methodology of your dissertation

Lab work: Intensive experiments, data collection, and analysis

Dissertation writing: Documenting your findings in a scholarly format

Defense: Presenting and defending your research before a committee

9. Career Opportunities After a PhD in Chemistry

With a PhD, you can pursue:

Academia (Postdoctoral research, Professorship)

R&D in pharmaceuticals, biotechnology, and materials science

Chemical manufacturing and process industries

Government or defense research labs

Scientific writing, patent law, or consulting

Final Tips:

Stay curious and resilient – research is a marathon, not a sprint.

Network with peers, attend conferences, and seek mentorship.

Keep publishing and updating your skills (e.g., in data analysis or AI in chemistry).

A PhD in Chemistry is demanding but deeply rewarding. With the right preparation and mindset, you can carve out a meaningful and impactful career.

#PhD in Chemistry salary in India #Phd in chemistry eligibility #PhD in Chemistry Syllabus

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studentsideas1 · 1 month ago

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tutor850 · 9 months ago

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nursingwriter · 2 months ago

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Using deep learning and medical data classification for predicting bacterial skin infection Fundamentals of Information (Trường Đại học Cần Thơ) Scan to open on Studocu Studocu is not sponsored or endorsed by any college or university Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 T.C. ALTINBAS UNIVERSITY Institute of Graduate Studies Information Technologies USING DEEP LEARNING AND MEDICAL DATA CLASSIFICATION FOR PREDICTING BACTERIAL SKIN INFECTION Sura Abdulameer Nayyef AL-DABBAGH Master of Science Supervisor Asst. Prof. Dr. Abdullahi Abdu IBRAHIM Istanbul, 2021 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 USING DEEP LEARNING AND MEDICAL DATA CLASSIFICATION FOR PREDICTING BACTERIAL SKIN INFECTION by Sura Abdulameer Nayyef AL-DABBAGH Information Technologies Submitted to the Institute of Graduate Studies in partial fulfillment of the requirements for the degree of Master of Science ALTINBAŞ UNIVERSITY 2021 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 iii The thesis titled “USING DEEP LEARNING AND MEDICAL DATA CLASSIFICATION FOR PREDICTING BACTERIAL SKIN INFECTION” prepared and presented by Sura Abdulameer Nayyef AL-DABBAGH was accepted as a Master of Science Thesis in Information Technologies. Asst. Prof. Dr. Abdullahi Abdu IBRAHIM Supervisor Thesis Defense Jury Members: Asst. Prof. Dr. Abdullahi Abdu IBRAHIM School of Engineering and Natural Sciences, Altinbas University __________________ Asst. Prof. Dr. Muhammad Ilyas School of Engineering and Natural Sciences, Altinbas University __________________ Asst. Prof. Dr. Tareq Abed MOHAMMED Computer Science , IMAM Jaafar AL-Sadiq University __________________ I certify that this thesis satisfies all the requirements as a thesis for the degree of Master of Science. Approval Date of Institute of Graduate Studies: ____/____/____ Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 iv All information in this document was collected and provided in compliance with academic rules and ethical behavior. I also declare that, as required by these rules and behavior, I have properly cited and referenced all non-original material and performance.. Sura Abdulameer Nayyef AL-DABBAGH Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 v DEDICATION I devote and pledge this research work to my supervisor who is salient for guiding me through whole research work as well as my family for always assisting me in my hard time. Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 vi ACKNOWLEDGEMENTS First and foremost, I would like to thank my supervisor Asst. Prof. Dr. Abdullahi Abdu IBRAHIM for guiding and helping me along the way in writing this dissertation. Discussing my progress, problems, and ideas with my supervisor Asst. Prof. Dr. Abdullahi Abdu IBRAHIM a couple of times every week helped me tremendously in understanding the logic behind the research. It made me better realize the technical need for this research work. He helped me by looking outside of all the machine learning and network-based information, and considering the cryptocurrency-based knowledge from a more holistic perspective than just from the productivity point of view. He patiently explained to me a great number of technical details with regards to block-chain and cryptocurrency, for example how to deal with solving problems and to provide insight into the reliability of those results. Without his time and help none of this research would have been possible, and I’m grateful to him for giving me the opportunity to work on this interesting subject. I am also very thankful to my parents as well as my friends who always supported me through whole of this journey. Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 vii ABSTRACT USING DEEP LEARNING AND MEDICAL DATA CLASSIFICATION FOR PREDICTING BACTERIAL SKIN INFECTION AL-DABBAGH, Sura Abdulameer Nayyef, M.Sc., Information Technologies, Altınbaş University, Supervisor: Asst. Prof. Dr. Abdullahi Abdu IBRAHIM Date: July /2021 Pages: 63 Infectious infectious diseases are one of the most common signs of disease in pharmacy. It's usually diagnosed clinically, with a clinical evaluation followed by a dermo copy images examination, a therapeutic or transdermal drug, and a histological examination.. In this study, I'll look at how deep learning techniques like CNN and health information detection can be used to forecast microbial skin disease. Big data is constantly being used to verify the effectiveness of identifying specific skin conditions. Many new methods have been developed to speed up the process while maintaining the quality of precision. In this paper, we introduced a method for predicting the likelihood of bacterial infections using machine learning and artificial neural networks, both of which are components of artificial intelligence's deep learning framework.. The data comprises nearly 3000 photographs of individuals with skin infections that are divided into two categories: malignant or benign. We used CNN and its 7 distinct frameworks to verify the relevance of photos of surface infectious diseases and conducted a comparative study to identify the best framework for this issue.. For the training, testing and validation of our dataset a wellknown MATLAB R2019a software was used for this purpose. We had the great outcome using Neural Networks and Health Information Analysis, with an efficiency rate of nearly 91.303 percent in detecting skin problems.. Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 viii Keywords: Bacterial Contaminants, Learning Techniques, Computer Vision, and Deep Neural Networks are all terms that can be used to describe infectious diseases. Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 ix TABLE OF CONTENTS Pages ABSTRACT ................................................................................................................................. vii LIST OF TABLES ....................................................................................................................... xi LIST OF FIGURES .................................................................................................................... xii LIST OF ABBREVIATIONS .................................................................................................... xv 1. NTRODUCTION ................................................................................................................. 1 1.1 INTRODUCTION ............................................................................................................. 1 1.2 MOTIVATION .................................................................................................................. 3 1.3 MAIN CONTRIBUTIONS ............................................................................................... 4 1.4 THESIS ORGANIZATION .............................................................................................. 5 2. LITERATURE REVIEW ................................................................................................... 6 2.1 CONVOLUTIONAL NEURAL NETWORK .................................................................. 6 2.1.1 Input layer ..................................................................................................................... 7 2.1.2 Dense Layer .................................................................................................................. 8 2.1.3 Convolution Layer ........................................................................................................ 9 2.1.4 Rectified Linear Unit Layer ........................................................................................ 10 2.1.5 Pooling Layer ............................................................................................................. 11 2.1.6 Normalization Layer ................................................................................................... 12 2.1.7 Fully Connected Layer ............................................................................................... 12 2.1.8 Output Layer ............................................................................................................... 13 2.2 NEURAL NETWORKS .................................................................................................. 14 2.3 FROM THE VISUAL HIERARCHY PATHWAY TO CNN’S ..................................... 16 2.4 OBJECT RECOGNITION IN THE VENTRAL VISUAL PATHWAY ........................ 18 2.5 AUTO-ENCODERS ........................................................................................................ 21 2.5.1 Deep auto-encoders .................................................................................................... 22 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 x 2.6 ROLE OF DEEP CONVOLUTIONAL NEURAL NETWORK TO PREDICT SKIN BACTERIAL INFECTIONS ................................................................................................... 23 3. METHODOLOGY ............................................................................................................ 25 3.1 DATASET DESCRIPTION ............................................................................................ 27 3.2 IMAGE PRE-PROCESSING IN MATLAB ................................................................... 27 3.3 POOLING LAYER SELECTION .................................................................................. 28 3.3.1 Dropout Selection ....................................................................................................... 29 4. RESULTS ........................................................................................................................... 32 4.1 BASIC CNN WITH CROSS VALIDATION ................................................................. 33 4.2 K-FOLD .......................................................................................................................... 33 4.3 APPLYING CNN WITH CROSS-VALIDATION ......................................................... 34 4.4 APPLYING VGG16 IN OUR DATASET ...................................................................... 38 4.5 VGG19 ............................................................................................................................ 41 4.5.1 Applying VGG19 in Our Dataset ............................................................................... 41 4.6 XCEPTION COCEPT ..................................................................................................... 44 4.6.1 Applying Xception in Our Dataset ............................................................................. 46 5. DISCUSSION ..................................................................................................................... 49 6. CONCLUSION .................................................................................................................. 50 REFERENCES ............................................................................................................................ 52 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 xi LIST OF TABLES Pages Table 4.1: Outputs per 5 epochs in CNN Architecture ................................................................. 35 Table 4.2: Outputs per 5 epochs in VGG16 Architecture. ............................................................ 38 Table 4.3: Outputs per 5 epochs in VGG19 Architecture ............................................................. 42 Table 4.4: Outputs per 5 epochs in Xception Architecture ........................................................... 46 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 xii LIST OF FIGURES Pages Figure 2.1: Architectures of Convolutional Neural Network .................................................. 7 Figure 2.2: Input Layer ............................................................................................................ 8 Figure 2.3: Dense Layer .......................................................................................................... 9 Figure 2.4: Convolution Layer .............................................................................................. 10 Figure 2.5: Rectified Linear Unit Layer ................................................................................ 11 Figure 2.6: Pooling Layer ...................................................................................................... 11 Figure 2.7: Normalization Layer ........................................................................................... 12 Figure 2.8: Fully Connected Layer ....................................................................................... 13 Figure 2.9: Output Layer ....................................................................................................... 14 Figure 2.10: Structure of a neuron with a detailed view of synaptic transmission . .............. 16 Figure 2.11: Simplified depiction of the two visual streams ................................................. 17 Figure 2.12: Comparative illustration of the organization of the two visual pathways and the stacking of S- and C-cells ...................................................................................................... 18 Figure 2.13: General architecture of the neocognitron ......................................................... 20 Figure 2.14: Architecture of an undercomplete auto-encoder building block. The fully-connected autoencoder contains a single hidden layer of lower dimensionality than its input layer ..... 21 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 xiii Figure 2.15: Architecture of an overcomplete auto-encoder building block ........................ 22 Figure 2.16: Examples of correctly and incorrectly classifies diseases. (a) Correctly classified ACROS in DermNet (b) Correctly Classified NAIL in DermNet (c) Correctly Classified SEB in DermNet (d) Correctly Classified VASC in ISIC (e) CEL Misclassified as ACROS in DermNet (f) Correctly Classified AKIEC in ISIC (g) BKL Miscalssified as MEL in ISIC (h) NV Misclassified as VASC in ISIC ............................................................................................. 24 Figure 3.1: Flowchart of the Proposed System ............................................................................. 26 Figure 3.2: Sample Images from our Dataset ............................................................................... 27 Figure 3.3: Sigmoid Function ....................................................................................................... 30 Figure 3.4: Relu Function. ............................................................................................................ 31 Figure 4.1: CNN Model ................................................................................................................ 32 Figure 4.2: An example of Dividing Dataset for Cross Validation .............................................. 33 Figure 4.3: K-Fold Mechanism ..................................................................................................... 34 Figure 4.4: Build Architecture of CNN model ............................................................................. 35 Figure 4.5: Loss Vs epochs. .......................................................................................................... 37 Figure 4.6: VGG16 ....................................................................................................................... 38 Figure 4.7: VGG16 Model Accuracy (accuracy vs epochs) and Model Loss (loss vs epochs) .... 40 Figure 4.8: VGG19 ....................................................................................................................... 41 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 xiv Figure 4.9: VGG19 Model Accuracy (accuracy vs epochs) Model Loss(loss vs epochs) ........... 43 Figure 4.10: Original Xception ..................................................................................................... 45 Figure 4.11: Modified Xeption ..................................................................................................... 46 Figure 4.12: Xception Model Accuracy(accuracy vs epochs) Model Loss(loss vs epochs) ........ 47 Figure 4.13: Output of original test data ....................................................................................... 48 Figure 4.14: Xception prediction on the same test data ................................................................ 48 Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 xv LIST OF ABBREVIATIONS CNN : Conventional Neural Network DNA : Deoxyribonucleic Acid ROS : Reactive Oxygen Species SVM : Support Vector Machine GPU : Graphic Processing Unit Downloaded by James Mcknight ([email protected]) lOMoARcPSD|28323056 1 1. NTRODUCTION 1.1 INTRODUCTION Psoriasis is an infectious fungal infection that lasts for years. Psoriasis Vulgaris has been the most widespread form of the disease, which is characterized by bright, furred inscriptions. The disorder causes skin irritation, nails, and joints on occasion . It is most commonly found on the elbows, knees, and scalp. Psoriasis causes chronic itchiness due to just an elevated number of lymphocytes and blood circulation, also known as inflammation. While psoriasis induces a greater range of blood vessel produces and resulting in an imperfect stratum granulosum, increased flow of blood, and lymphocytes, the precise mechanism of rheumatoid arthritis are unclear. Psoriasis was found to be present in 1.5 to 3% of people in a Nordic study. In a 30-year obey randomized trial in Lewin conducted in 2007-2008, 11.8 percent of people aged 20 to 79 had rosacea, relative to 4.8 percent in 1979-1980 . About 1970 and 2000, a set of inhabitants research revealed that the incidence of untreated rheumatoid arthritis in adults over the age of 18 had increased. It has been suggested that there is a considerable rise in the epidemic, that has been reported at least in the United States and Belgium . The problem with these experiments is that they depend on personality data. A medical decision by a certified clinical doctor will be the accepted norm.. The doctor will identify particular features of the outer layer of skin during the psoriatic arthritis clinical examination: swelling, rosacea, stiffness, and intensity region. A medical indicator of Psoriasis intensity is included in the treatment process. The PASI (Psoriasis Area Severity Index) is a measure that ranks the intensity of rheumatoid arthritis on a range of 0-72. Anticipated systematic reviews, its been suggested, should look into the incidence of rheumatoid arthritis with a more systematic research design and use proven and medically confirmed psoriatic arthritis medical testing done by a doctor. Rheumatoid arthritis supporting facts obviously varies among research, but there could be stratified people aged going up around 20 and 39 and 50 and 69 years. . This may be attributed to a variety of factors, including weather . The disorder is influenced by the environment, with rising interest rates recorded in certain nations at high elevations. A research from Michigan found that the weather has an effect on rheumatoid arthritis, with 68 percent of cases reported and during winter seasons.. Read the full article

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tutorsindia152 · 25 days ago

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honorarydoctorate · 3 months ago

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PhD Full Form in India: Everything You Need to Know

What is the Full Form of PhD?

PhD stands for Doctor of Philosophy. It is the highest academic degree awarded in various disciplines, signifying expertise and significant research contributions.

Overview of PhD in India

In India, a PhD is pursued after completing a master’s degree (such as MA, MSc, MTech, MBA, or MPhil). It involves in-depth research in a chosen subject, leading to the submission of a thesis or dissertation.

Eligibility Criteria for PhD in India

To pursue a PhD in India, candidates must meet specific eligibility requirements:

Educational Qualification: A postgraduate degree (Master’s or MPhil) with at least 55% marks (50% for reserved categories).

Entrance Exam: Most universities require a PhD entrance test (PET) or accept national-level exams like UGC NET, CSIR NET, GATE, or JRF.

Interview: After clearing the entrance, candidates must pass a personal interview to discuss their research proposal.

PhD Admission Process in India

The admission process generally involves:

Application Submission – Candidates apply to universities offering PhD programs.

Entrance Exam – Most institutions conduct an entrance test (some accept NET/GATE scores).

Interview & Research Proposal – Shortlisted candidates present their research ideas before a panel.

Final Selection & Enrollment – Selected candidates enroll as PhD scholars and start their research work.

Popular PhD Entrance Exams in India

Here are some common PhD entrance exams:

UGC-NET – For research fellowships and university teaching positions.

CSIR-NET – For science and engineering fields.

GATE – Required for PhD admissions in IITs, NITs, and other technical institutes.

ICAR AICE-JRF/SRF – For PhD in agricultural sciences.

Duration of PhD in India

The duration of a PhD in India varies:

Minimum Duration: 3 years

Maximum Duration: 5-6 years (as per UGC guidelines)

Types of PhD Programs in India

PhD programs in India are offered in different modes:

Full-Time PhD – Scholars engage in full-time research with stipends or fellowships.

Part-Time PhD – Designed for working professionals who want to pursue research along with their jobs.

Online/Distance PhD – Some universities offer flexible research programs with minimal campus visits.

Top PhD Specializations in India

PhD programs are available in various disciplines, including:

Engineering & Technology (Computer Science, Mechanical, Civil, etc.)

Management & Business Studies (Marketing, Finance, HR, etc.)

Science (Physics, Chemistry, Biology, Mathematics, etc.)

Social Sciences & Humanities (History, Economics, Sociology, Psychology, etc.)

Law & Legal Studies

Medicine & Healthcare

Top Universities for PhD in India

Some of the best universities offering PhD programs in India include:

IITs & IISc (for Science & Technology)

AIIMS (for Medical Sciences)

IIMs (for Management Studies)

Jawaharlal Nehru University (JNU)

Delhi University (DU)

Banaras Hindu University (BHU)

Funding & Scholarships for PhD in India

Several scholarships and fellowships are available for PhD students in India:

UGC Junior Research Fellowship (JRF)

CSIR NET Fellowship

INSPIRE Fellowship (for Science students)

ICSSR Doctoral Fellowships (for Social Sciences)

IIT/NIT Research Assistantships

Career Opportunities After PhD in India

A PhD opens up numerous career paths, such as:

Academia & Teaching – Become a professor or researcher in universities.

Research & Development (R&D) – Work in government or private research labs.

Corporate Sector – Many companies hire PhD holders for specialized roles.

Entrepreneurship & Consultancy – Start your own research-based venture.

Government Jobs – Join organizations like ISRO, DRDO, ICAR, or CSIR.

Conclusion

A PhD is a prestigious qualification that enhances knowledge and career prospects. If you have a passion for research and innovation, pursuing a PhD in India can be a rewarding journey.

Got more questions? Drop them in the comments! 🚀

Hope this helps! Click on the headings for more details. Let me know if you need any modifications. 😊

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