Astrological Journal on the Topic of Mars Dosha and Marital Life as per Vedic Astrology

Karthik. R, Dr. Thangavel Murugan

Mars Dosha; Manglik Dosha; Kuja Dosha, Marital Life; Marriage Compatibility; Horoscope Matching; Planet Mars; Marital Harmony & Delay in Marriage

Mars Dosha, commonly known as Manglik Dosha or Kuja Dosha, occupies an important place in the study of Vedic Astrology. It is considered one of the most discussed astrological combinations affecting marriage, relationships, domestic harmony, and emotional compatibility. According to classical Vedic texts, the placement of Mars in specific houses of the natal horoscope creates energetic imbalances that may influence marital life. The aggressive, fiery, and forceful nature of Mars can create tension, impulsiveness, misunderstandings, conflicts, and delays in marriage if not harmoniously balanced.This journal explores the concept of Mars Dosha in detail from the perspective of Vedic Astrology. It discusses the astronomical and astrological significance of Mars, the houses responsible for the formation of Mars Dosha, the classical interpretations, psychological implications, effects on marital harmony, cancellation conditions, remedies, and modern interpretations. The study also highlights how Mars Dosha should not be interpreted with fear but with astrological wisdom, balance, and proper guidance.

[1] Parāśara Maharshi, Brihat Parashara Hora Shastra, translated and edited by R. Santhanam, 2 vols. New Delhi, India: Ranjan Publications, 2008.
[2] Mantreswara, Phaladeepika: A Unique Classic on Hindu Predictive Astrology, translated and commented by G. S. Kapoor. New Delhi, India: Ranjan Publications, 2014.
[3] Vaidyanatha Dikshita, Jataka Parijata, translated and annotated by V. Subrahmanya Sastri, 3 vols. New Delhi, India: Ranjan Publications, 2004.
[4] B. V. Raman, Predictive Astrology of the Hindus. Bangalore, India: Raman Publications, 2002.
[5] B. V. Raman, Hindu Astrology. Bangalore, India: UBS Publishers Distributors Ltd., 2000.
[6] B. V. Raman, Three Hundred Important Combinations. Bangalore, India: Raman Publications, 1991.
[6] F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, Vol. 12, 2011, pp. 2825–2830.
[7] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, Vol. 16, 2002, pp. 321–357.
[8] S. M. Lundberg and S. I. Lee, “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems, Vol. 30, 2017, pp. 4765–4774.
[9] A. L. Buczak and E. Guven, “A Survey of Data Mining and Machine Learning Methods for Cybersecurity Intrusion Detection,” IEEE Communications Surveys and Tutorials, Vol. 18, No. 2, 2016, pp. 1153–1176.
[10] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, Massachusetts, 2016.
[11] W. McKinney, “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, 2010, pp. 56–61.
[12] G. Kim, H. Lim, and Y. Lee, “An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic,” Electronics, Vol. 9, No. 10, 2020, p. 1583.
[13] K.K. Rakesh, Dr. A. S. Aneeshkumar, “Optimization of fuzzy logic-based Genetic Algorithm Technique in wireless sensor network Protocols”, International Journal of Intelligent Systems and Applications in Engineering, Vol.12(14), 2024, pp. 548–556.
[14] X. Guo, “Deep Learning-Based Network Intrusion Detection Using NSL-KDD Dataset,” Proceedings of the International Conference on Computing and Data Science, 2021, pp. 112–118.
[15] C. Zhang, M. Renz, and C. Lim, “Deep Learning Intrusion Detection Model Based on Optimized Feature Selection,” Applied Sciences, Vol. 9, No. 16, 2019, p. 3381.
[16] G. V. Rossum and F. L. Drake, Python 3 Reference Manual, CreateSpace, Scotts Valley, California, 2009.