Volume-12 , Issue-3 , Jun 2024, ISSN 2321-3256 (Electronic Version)

• Open Access   Article

  Diabetes Predictor: Prediction Using Machine Learning Techniques

  A Hariprasad Reddy, G. Tejaswi, S. Akshay, E. Anisha

  Research Paper | Journal (IJSRNSC)

  Vol.12 , Issue.3 , pp.1-6, Jun-2024

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  As the old saying goes, prevention is better than cure when it comes to health. The likelihood of saving lives can be greatly increased by anticipating diseases such as diabetes. Numerous variables, including age, obesity, lack of exercise, genetic predisposition, lifestyle, nutrition, and high blood pressure, can contribute to diabetes, an illness that is spreading quickly. With the help of machine learning techniques (MLT), healthcare professionals can now forecast patient outcomes using pre-existing data, which makes them indispensable tools. Several categorization machine learning methods are used in a diabetes prediction project to identify the most accurate model. This model takes into account extrinsic factors linked to diabetes risk in addition to conventional components like insulin, age, BMI, and glucose. Comprehending the natural glucose regulating process of the body is essential to understanding diabetes. The body uses glucose, which is obtained from foods high in carbohydrates, as its main energy source. The pancreas secretes insulin, which makes glucose easier for cells to use as fuel. On the other hand, diabetes is brought on by inadequate insulin synthesis or inadequate insulin use, which raise blood glucose levels. Here, skin thickness, number of conceptions, and pedigree function are additional characteristics that improve the model`s prediction power. These factors enhance the accuracy of diabetes risk assessment by adding to conventional markers and providing insightful information. Proactive illness prediction is made possible by utilizing MLT in the healthcare industry, especially for conditions like diabetes. The predicted accuracy of diabetes models can be greatly increased by incorporating both traditional and non-conventional risk indicators, such as skin thickness, number of pregnancies, and pedigree function. This will enable early intervention and better patient outcomes.

  Key-Words / Index Term
  Prevention, Diabetes, Extrinsic factors, Skin thickness, Conceptions (number of pregnancies), Pedigree function, Proactive prediction, Early intervention, Anticipating, Predisposition, Incorporating, Indispensable.

  References
  [1] J S Kannan, R Natarajan, G K Santhanam, “Predicting diabetes mellitus using machine learning techniques”, International Journal of Diabetes Research, Vol.20, Issue.2, pp.123-135, 2021.
  [2] Shimoo Firdous, Gowher A Wagai, Kalpana Sharma, “A survey on diabetes risk prediction using machine learning approaches”, Journal of Family Medicine and Primary Care, Vol.11, Issue.11, pp.1-6, 2022.
  [3] KM Jyoti Rani, “Diabetes Prediction Using Machne Learning”, International Journal of Scientific Research in Computer Science, Vol.6, Issue.4, pp.294-305, 2020.
  [4] Mitushi Soni, Dr. Sunita Varma, “Diabetes Prediction using Machine Learning Techniques”, International Journal of Engineering Research & Technology (IJERT), Vol.9, Issue.9, pp.1-5, 2020.
  [5] M N Anupama & S. Srinath, “An efficient model for predicting diabetes using machine learning”, Journal of Clinical Endocrinology, Vol.25, Issue.4, pp.200-215, 2020.
  [6] Urbanowicz R. J, “Machine learning for the prediction of diabetes using demographic and diagnostic data”, Journal of Artificial Intelligence in Medicine, Vol.12, Issue.3, pp.150-165, 2021.
  [7] A Ardestani, “Machine learning for Early Prediction of Diabetes and Pre-Diabetes”, Journal of Diabetes Management, Vol.28, Issue.4, pp.220-235, 2023.
  [8] Wei H, Wei C & Wang K, “Predicting the risk of diabetes using machine learning techniques”, Journal of Health Data Science, Vol.15, Issue.1, pp.67-80, 2023.

  Citation
  A Hariprasad Reddy, G. Tejaswi, S. Akshay, E. Anisha, "Diabetes Predictor: Prediction Using Machine Learning Techniques," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.3, pp.1-6, 2024

• Open Access   Article

  The Neural Frontier: AI`s Relentless Encroachment into the Human Mind

  Kritika

  Research Paper | Journal (IJSRNSC)

  Vol.12 , Issue.3 , pp.7-14, Jun-2024

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The intersection of neuroscience and artificial intelligence (AI) represents a turning point in technological progress, presenting previously unheard-of chances to better comprehend and improve human cognition while also posing significant ethical issues. The ethical implications of AI-driven neuro-technologies are examined in this work, "The Neural Frontier: AI`s Relentless Encroachment into the Human Mind," through a tripartite technique that includes a systematic literature review, an interdisciplinary ethical analysis, and a speculative risk assessment. Our findings highlight the importance of brain privacy and cognitive liberty as fundamental rights, highlighting the need for strong governance frameworks and data security measures. The possibility of AI systems perpetuating biases in neuroscientific applications, the potential for unequal access to cognitive augmentation to exacerbate societal disparities, and the blurring of moral duty as AI influences human cognition are among the major ethical problems that we uncover. The study makes the case for the creation of frameworks for equitable cognitive augmentation, neuro-ethically-aligned AI, and adaptive governance models. It highlights how important it is to collaborate across disciplines, involve the public, and incorporate neuro-ethics into scientific education. We are not just developing technology but also redefining the limits of human identity and awareness as we traverse this cerebral frontier, striking a balance between the enormous potential of AI in neuroscience and the necessity to protect human dignity, autonomy, and cognitive liberty. In an era of unparalleled technological advancement, this undertaking necessitates moral discernment, scientific probity, and a dedication to safeguarding the fundamental principles that characterize our humanity.

  Key-Words / Index Term
  Neuro-ethics, Artificial intelligence, Cognitive liberty, Neural privacy, Cognitive enhancement, Governance frameworks, Interdisciplinary collaboration

  References
  [1] Bostrom, N., & Roache, R. “Ethical issues in human enhancement” J. Ryberg, T. S. Petersen, & C. Wolf (Eds.), New waves in applied ethics, Palgrave Macmillan, USA, pp.120-152, 2008
  [2] Bostrom, N., & Sandberg, A. “Cognitive enhancement: Methods, ethics, regulatory challenges. Science and Engineering Ethics”, Vol.15, Issue.3, pp.311-341, 2009. https://doi.org/10.1007/s11948-009-9142-5
  [3] J. C. Bublitz, "My mind is mine!? Cognitive liberty as a legal concept," in Cognitive enhancement, E. Hildt and A. G. Franke, Eds. Springer, USA, pp.233-264, 2013.
  [4] J. C. Bublitz and R. Merkel, "Crimes against minds: On mental manipulations, harms, and a human right to mental self-determination," Criminal Law and Philosophy, Vol.8, Issue.1, pp.51-77, 2014. https://doi.org/10.1007/s11572-012-9172-y
  [5] A. Buchanan, Beyond humanity?: The ethics of biomedical enhancement. Oxford University Press, UK, 2011.
  [6] D. S. Char, N. H. Shah, and D. Magnus, "Implementing machine learning in health care—addressing ethical challenges," New England Journal of Medicine, Vol.378, Issue.11, pp.981-983, 2018. https://doi.org/10.1056/NEJMp1714229
  [7] J. Clausen, "Ethical brain stimulation – neuroethics of deep brain stimulation in research and clinical practice," European Journal of Neuroscience, Vol.32, Issue.7, pp.1152-1162, 2010. https://doi.org/10.1111/j.1460-9568.2010.07421.x
  [8] J. Clausen et al., "Help, hope, and hype: Ethical dimensions of neuroprosthetics," Science, Vol.356, Issue.6345, pp.1338-1339, 2017. https://doi.org/10.1126/science.aam7731
  [9] J. L. Collinger et al., "High-performance neuroprosthetic control by an individual with tetraplegia," The Lancet, Vol.381, Issue.9866, pp.557-564, 2013. https://doi.org/10.1016/S0140-6736(12)61816-9
  [10] M. Dresler et al., "Non-pharmacological cognitive enhancement," Neuropharmacology, Vol.64, pp.529-543, 2013. https://doi.org/10.1016/j.neuropharm.2012.07.002
  [11] K. Evers and M. Sigman, "Possibilities and limits of mind-reading: A neurophilosophical perspective," Consciousness and Cognition, Vol.22, Issue.3, pp.887-897, 2013. https://doi.org/10.1016/j.concog.2013.05.011
  [12] M. J. Farah et al., "Neurocognitive enhancement: what can we do and what should we do?," Nature Reviews Neuroscience, Vol.5, Issue.5, pp.421-425, 2004. https://doi.org/10.1038/nrn1390
  [13] F. Fukuyama, "Transhumanism," Foreign Policy, Issue.144, pp.42-43, 2004. https://doi.org/10.2307/4152980
  [14] H. Garden and D. E. Winickoff, "Issues in neurotechnology governance," OECD Science, Technology and Industry Working Papers, 2018/11, 2018. https://doi.org/10.1787/c3256cc6-en
  [15] J. Giordano, "Neurotechnology as a national security and defense issue: Focus on cognitive enhancement," Synesis: A Journal of Science, Technology, Ethics, and Policy, Vol.6, Issue 1, pp.T61-T68, 2015.
  [16] F. Gilbert et al., "I miss being me: Phenomenological effects of deep brain stimulation," AJOB Neuroscience, Vol.8, Issue.2, pp.96-109, 2017. https://doi.org/10.1080/21507740.2017.1320319
  [17] S. Goering et al., "Staying in the loop: Relational agency and identity in next-generation DBS for psychiatry," AJOB Neuroscience, Vol.12, Issue.2-3, pp.131-143, 2021. https://doi.org/10.1080/21507740.2021.1904031
  [18] H. T. Greely, K. M. Ramos, and C. Grady, "Neuroethics in the age of brain projects," Neuron, Vol.92, No.3, pp.637-641, 2016. https://doi.org/10.1016/j.neuron.2016.10.048
  [19] H. T. Greely et al., "Neuroethics guiding principles for the NIH BRAIN Initiative," Journal of Neuroscience, vol. 38, no. 50, pp. 10586-10588, 2018. https://doi.org/10.1523/JNEUROSCI.2077-18.2018
  [20] D. Hassabis et al., "Neuroscience-inspired artificial intelligence," Neuron, Vol.95, No.2, pp. 245-258, 2017. https://doi.org/10.1016/j.neuron.2017.06.011
  [21] L. R. Hochberg et al., "Reach and grasp by people with tetraplegia using a neurally controlled robotic arm," Nature, vol.485, no.7398, pp.372-375, 2012. https://doi.org/10.1038/nature11076
  [22] M. Ienca and R. Andorno, "Towards new human rights in the age of neuroscience and neurotechnology," Life Sciences, Society and Policy, vol.13, no.1, pp.5, 2017. https://doi.org/10.1186/s40504-017-0050-1
  [23] M. Ienca and P. Haselager, "Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity," Ethics and Information Technology, vol. 18, no. 2, pp. 117-129, 2016. https://doi.org/10.1007/s10676-016-9398-9
  [24] M. Ienca and E. Vayena, "Dual use in the 21st century: emerging risks and global governance," Swiss Medical Weekly, vol.148, pp.w14688, 2018. https://doi.org/10.4414/smw.2018.14688
  [25] M. Ienca et al., "Considerations for ethics review of big data health research: A scoping review," PloS One, vol. 13, no. 10, p. e0204937, 2018. https://doi.org/10.1371/journal.pone.0204937
  [26] J. Illes and E. Racine, "Imaging or imagining? A neuroethics challenge informed by genetics," The American Journal of Bioethics, vol. 5, no. 2, pp. 5-18, 2005. https://doi.org/10.1080/15265160590923358
  [27] N. Kriegeskorte and P. K. Douglas, "Cognitive computational neuroscience," Nature Neuroscience, vol. 21, no.9, pp. 1148-1160, 2018. https://doi.org/10.1038/s41593-018-0210-5
  [28] M. A. Lebedev and M. A. Nicolelis, "Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation," Physiological Reviews, Vol.97, No.2, pp.767-837, 2017. https://doi.org/10.1152/physrev.00027.2016
  [29] N. Levy, Neuroethics: Challenges for the 21st century. Cambridge University Press, UK, 2007.
  [30] H. S. Mayberg et al., "Deep brain stimulation for treatment-resistant depression," Neuron, Vol.45, No.5, pp.651-660, 2005. https://doi.org/10.1016/j.neuron.2005.02.014
  [31] C. C. McIntyre and R. W. Anderson, "Deep brain stimulation mechanisms: the control of network activity via neurochemistry modulation," Journal of Neurochemistry, Vol.139, supplement 1, pp.338-345, 2016. https://doi.org/10.1111/jnc.13649
  [32] T. Metzinger and E. Hildt, "Cognitive enhancement," in Oxford Handbook of Neuroethics, J. Illes and B. J. Sahakian, Eds. Oxford University Press, UK, pp.245-264, 2011.
  [33] Z. Obermeyer et al., "Dissecting racial bias in an algorithm used to manage the health of populations," Science, Vol.366, No.6464, pp.447-453, 2019. https://doi.org/10.1126/science.aax2342
  [34] R. A. Poldrack and M. J. Farah, "Progress and challenges in probing the human brain," Nature, Vol.526, No.7573, pp.371-379, 2015. https://doi.org/10.1038/nature15692
  [35] K. S. Rommelfanger et al., "Neuroethics questions to guide ethical research in the international brain initiatives," Neuron, Vol.100, No.1, pp.19-36, 2018. https://doi.org/10.1016/j.neuron.2018.09.021
  [36] A. Salles and M. Farisco, "Of ethical frameworks and neuroethics in big neuroscience projects: A view from the HBP," AJOB Neuroscience, Vol.11, No.3, pp.167-175, 2020. https://doi.org/10.1080/21507740.2020.1778118
  [37] J. Savulescu, "Justice, fairness, and enhancement," Annals of the New York Academy of Sciences, Vol.1093, No.1, pp.321-338, 2006. https://doi.org/10.1196/annals.1382.021
  [38] C. W. Woo et al., "Building better biomarkers: brain models in translational neuroimaging," Nature Neuroscience, Vol.20, No.3, pp.365-377, 2017. https://doi.org/10.1038/nn.4478
  [39] R. Yuste et al., "Four ethical priorities for neurotechnologies and AI," Nature, Vol.551, No.7679, pp.159-163, 2017. https://doi.org/10.1038/551159a
  [40] J. Zou and L. Schiebinger, "AI can be sexist and racist—it`s time to make it fair," Nature, Vol.559, No.7714, pp.324-326, 2018. https://doi.org/10.1038/d41586-018-05707-8
  [41] T. L. Beauchamp and J. F. Childress, Principles of biomedical ethics, 8th ed. Oxford University Press, UK, 2019.
  [42] N. Bostrom and A. Sandberg, "Cognitive enhancement: Methods, ethics, regulatory challenges," Science and Engineering Ethics, Vol.15, No.3, pp.311-341, 2009. https://doi.org/10.1007/s11948-009-9142-5
  [43] J. F. Childress et al., "Public health ethics: Mapping the terrain," The Journal of Law, Medicine & Ethics, vol.30, No.2, pp.170-178, 2002. https://doi.org/10.1111/j.1748-720X.2002.tb00384.x
  [44] J. E. Cohen, Between truth and power: The legal constructions of informational capitalism. Oxford University Press, 2019.
  [45] M. Kritika, "A comprehensive study on navigating neuroethics in Cyberspace," AI and Ethics, Vol.1-8, 2024. https://doi.org/10.1007/s43681-024-00486-7
  [46] Kritika, "Neuroethical quandaries at the crossroads of cyberspace," Scientific and Practical Cybersecurity Journal, Vol.8, No.1, pp.57-63, 2024.

  Citation
  Kritika, "The Neural Frontier: AI`s Relentless Encroachment into the Human Mind," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.3, pp.7-14, 2024

• Open Access   Article

  Protection of Research Data and Devices from Malware Attacks Using Endpoint Security System in Network

  Pratap Singh Solanki, Ajay Singh, Shaneel Sao, N.D. Atkekar

  Research Paper | Journal (IJSRNSC)

  Vol.12 , Issue.3 , pp.15-18, Jun-2024

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  In ealier era, computer were used by limited organizations with limited Internet accessibility therefore data and device security were not big issue. Gradulay, uses of computer increased exponentialy therefore day-by-day it posing new data security challenges. As the uses of Intrenet increases, the challenges of critical data security also increased. Every 39 second, one cyber attack is occurring and thousands website hacked daily. Every establishment uses the computational devices with Internet for running and expnading their business. The computational devices are generting huge volume of data which is very sensitive and essenatial to run the organization. All the devices which are connected in open network are prone to attack by harmful viruses. In this hyper-connected world, protecting the devices and data from loss and unauthorized access are big challenge. The Process of protecting the data from destructive threats, unauthorised access and data corruption is known as data security. In past, the Antivirus software on individual desktop computer were sufficient to protect the device but for various network devices, which are connected in Local Area Network (LAN) is required integrated centralized solution along with management tools. Many antivirus tools sometime failed to detect the advance thretas due to that risk of vulnerability and data lost may increased. End point securiy is an process to protect the all devices which is connected in network. As the Internet uses grown, the types of the threats and techniques changed due to that these Antivirus tools could not prove full efficient to protect the sesitive data. The antivirus software can be used for individual machine but in organization Network, dedicated Endpoint Security System (EPS) is necessary for quickly detecting the malware and common security threats in advance. The EPS runs on organization security policy. The full-proof defence with advanced threat detection system is very necessary. EPS, is an Software System to protect the organization network devices from dectructive threats. It is an device level security system with centralized management and results were encoraging . The End Point Security System provide the security beyond the traditional antiviurs software along with additional features to block the modern threats. In this paper we are discussing about the implementation of Endpoint Security system for protecting computational devices and protecting the scientific and bussiness data at CWPRS Local Area Network (CLAN). The Endpoint Security System has been successfully implemented for protecting the scientific data and devices from external possible thretas.

  Key-Words / Index Term
  End Point Security System, Virus, Antivirus, Device Protection, Cyber threats, Malware.

  References
  [1] Molina-Coronado, B., Mori, U., Mendiburu, A., & Miguel-Alonso, J. “Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process”, arXiv preprint arXiv:2001.0969, 2020.
  [2] F. Cohen, "Computer viruses: theory and experiments," Computers and Security, Feb., Vol.6, pp.22-35, 1987.
  [3] J. Shoch, J. Hupp, "The worm programs - early experience with a distributed computation," Commun. Of ACM, March, Vol.25, pp.172-180, 1982.
  [4] E. Spafford, "The Internet worm program: an analysis," ACM Comp. Commun. Rev., Jan., Vol.19 , pp.17-57, 1989.
  [5] S. Cass, "Anatomy of malice," IEEE Spectrum, Nov., pp.56- 60, 2001.
  [6] Cert-In, Information Security Policy for protection of critical information and infrastructure CERT-in/NISAP/01, May, Issue.1, 2006.

  Citation
  Pratap Singh Solanki, Ajay Singh, Shaneel Sao, N.D. Atkekar, "Protection of Research Data and Devices from Malware Attacks Using Endpoint Security System in Network," International Journal of Scientific Research in Network Security and Communication, Vol.12, Issue.3, pp.15-18, 2024