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Atharva Auti, Shrawani Pagar, Vivek Mishra, Jay Makwana, and Shwetambari Borade
IEEE
With the tremendous growth of cyber-attacks, the loss of private or sensitive data has risen to a peak. Honeypots are one of the most concerned topics in the field of cyber security currently. HoneyTrack is a honeypot that uses various technologies like Docker, Shell Scripts, Python, Elastic Search, Kibana, and Filebeats which protect an organization's database as well it backtracks the hacker when it intrudes the target network. Information about the attacker will be gathered by the bot in the honeypot which will help us reach the roots of the attacker. The target networks could be varied and cause a lot of damage when sensitive data will be lost/stolen. However, this honeypot is like a traditional honeypot but quite updated with new-generation technology and their needs related to cyber-security.
Shwetambari Borade, Dhananjay Kalbande, Kristen Pereira, Rushil Patel, and Sudhanshu Kulkarni
Seventh Sense Research Group Journals
Shwetambari Borade, Dhananjay Kalbande, Hemangi Jakaria, and Linesh Patil
IEEE
Dermatological illnesses are the most serious problem in the twenty-first century, owing to a lack of awareness and the high cost of diagnosis. To address the issue, we believe that automated methods-based applications will be extremely beneficial in the early stages of diagnosis. As a result, in this research, we describe an automated approach for identifying the kind of cosmetic skin and cosmetic skin condition from photographs. Our model is created utilizing machine learning methods. The model consists of three phases: data gathering and data augmentation, features extraction, and prediction.
Shwetambari Borade and Dhananjay Kalbande
IEEE
Prediction and detection of skin diseases have always been a challenging and critical task for health care professionals. There are many clinics and skin practitioners who are offering their services at exorbitant costs. At the same time, skin condition prevails in our country in every part. In the current scenario majority of the skincare practitioners are using traditional methods to diagnose the disease which may take a considerable amount of time. Skin Diseases are serious problems in recent times as it is a matter of environmental factors, socioeconomic factors, lack of full diet, etc. This paper is a comparative study about understanding various skin diseases related to normal skin issues as well as cosmetology. A comparison between oily, dry, and normal skin is also projected in this study. A survey of different papers is done on basis of technologies used, results with accuracy, ethical conduct, number of diseases diagnosed, datasets, etc are done.