Digital Transformation in Supply Chain Management: Artificial Intelligence (AI) and Machine Learning (ML) as Catalysts for Value Creation

In the rapidly evolving landscape of supply chain management (SCM), digital transformation has become a cornerstone for achieving competitive advantage. This paper explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) as catalysts in this transformation, driving significant value creation across various facets of SCM. Through a comprehensive literature review, including an analysis of 12 key papers, this study examines the integration of AI and ML in enhancing supply chain operations, from predictive analytics in demand forecasting to real-time decision-making in logistics and inventory management. The findings highlight the transformative impact of these technologies in optimizing efficiency, reducing costs, and improving overall supply chain resilience. The paper also addresses the challenges and ethical considerations inherent in implementing AI and ML, such as data privacy and workforce implications. Concluding with a look towards the future, this study underscores the growing importance of AI and ML in shaping the next generation of SCM practices. This research not only contributes to the academic discourse on digital supply chain transformation but also offers practical insights for industry professionals navigating this digital shift

Real Time Object Detection with Noisy Sensors Using Deep Learning

In this paper, we introduce a first of its kind, radio-signal based object detection system for controlled environments, which substitutes complex signal processing and expensive hardware with deep learning networks to detect patterns from low-quality, inexpensive sensors. Our system operates in the less crowded low- frequency range of 433 MHz in contrast to existing RF-based sensing methods and uses mini-Doppler maps generated from raw I/Q data, thereby allowing us to use cheap, off-the-shelf software defined radios. We demonstrate that our system is versatile enough to handle occlusions and is also sensitive to multiple objects; additionally, it does not use visual data and hence is not hampered by bad lighting. The core of our system is a VGG-16 based CNN architecture trained on the mini-Doppler maps. We achieve an accuracy of 0.96 on a binary classification task of detecting the presence or absence of an object in an enclosed space. Furthermore, we observe that our system shows promise for more complicated detection algorithms as it is able to successfully differentiate between the presence of a single object and two identical objects placed together. Our results indicate that convolutional networks can learn features important enough from spectrograms that enable it to distinguish the presence of objects, thereby eliminating the need of sophisticated signal processing methods to do the same

Assessment of Clinical Profile of epileptic patients

Background:Epilepsy is a common and diverse disorder with many different causes. The present study was conducted to assess clinical profile of patients with epilepsy.Materials & Methods:80 patients diagnosed with epilepsy of both genders were clinically evaluated. Reason of epilepsy, clinical features etc. was recorded.Results: Out of 80 patients, males were 50 and females were 30. Etiology was infectious in 20, vascular in 11, metabolic in 7, alcoholic in 8, idiopathic in 14, neoplastic in 15 and arachnoid cyst in 5 cases. The difference was significant (P< 0.05). Age at first seizure was 30.4 years, frequency of seizure per year was 62.2, duration of seizure before starting treatment was 4.1 years and family history of epilepsy was observed in 36. Conclusion:Most common etiology of epilepsy was infectious followed by vascular, metabolic, alcoholic, idiopathic, neoplastic and arachnoid cyst

Assessment of the cases of Myocardial infarction- A clinical study

Background:Myocardial injury is common in patients without acute coronary syndrome, and international guidelines recommend patients with myocardial infarction are classified by aetiology. The present study was conducted to assess the cases of Myocardial infarction.Materials & Methods:102 cases of Myocardial infarction of both genders were included. Recording of education level, family history, residence, tobacco history, co- morbidities etc. was recorded.Results: Out of 102, males were 62 and females were 40. Common risk factors were smoking in 45, alcoholism in 50, hypertension in 72, hyperlipidemia in 80 and lack of exercise in 51. The difference was significant (P< 0.05).Conclusion: Common risk factors were smoking, alcoholism, hypertension, hyperlipidemia and lack of exercise