Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, focusing on the challenges of ensuring (...) safety, mitigating ethical concerns, and navigating evolving regulatory frameworks. We discuss key strategies for improving the transparency, fairness, and accountability of autonomous systems, as well as the role of machine learning in enabling safe decision-making. Additionally, we propose a roadmap for the future development of autonomous systems that incorporates robust safety measures, ethical guidelines, and regulatory compliance. (shrink)

Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, focusing on the challenges of ensuring (...) safety, mitigating ethical concerns, and navigating evolving regulatory frameworks. We discuss key strategies for improving the transparency, fairness, and accountability of autonomous systems, as well as the role of machine learning in enabling safe decision-making. Additionally, we propose a roadmap for the future development of autonomous systems that incorporates robust safety measures, ethical guidelines, and regulatory compliance. (shrink)

Amongst invertebrates, molluscs show great variability in their nervous system ranging from primitive arrangement in Chitons to the complex mass of fused ganglia forming the ‘brain’ of cephalopods. Most of the effector organs used for pharmacological or physiological experiments. The neurosecretory cells (NSCs) with their combination of neuronal and glandular capabilities are perfectly suited to translate a neuronal input into the hormonal output best suited to long-term process. In this capacity, the NSCs may produce hormones, which act directly upon the (...) peripheral target or it may exert its effect indirectly by influencing the activity of other non-neural, endocrine organs. Neurosecretory cells have been detected in the cerebral, pedal and visceral ganglia of Lamellidens corrianus. The distribution and biology of freshwater bivalve like Lamellidens corrianus is influenced by local ecological factors (like temperature, pH, inorganic salts, type of soil etc.), water flow system and presence of micro-organisms, teleost fishes and seasonal variations in these parameters. Nitrogenous excretory changes which include ammonia in these molluscs is also influenced by these local ecological factors in which temperature is plays very crucial role. It is well known fact that the high stress conditions during drought or severe winter conditions have been conquered by several lineage of gastropods and sand bivalves and their ability to enter the resistant or dormant stages (low food) protein catabolism increased. Present investigation deals with the oxygen consumption and ammonia excretion ratio of these freshwater pelecypode Lamellidens corrianus. Study on Lemellidens corrianus directed in understanding the different behavioral and physiological aspects after cerebral ganglia removal and injection of their extracts revealed significant changes during summer (April-May). (shrink)

Some variants of quantum theory theorize dogmatic "unimodal" states-of-being, and are based on hodge-podge classical-quantum language. They are based on ontic syntax, but pragmatic semantics. This error was termed semantic inconsistency [1]. Measurement seems to be central problem of these theories, and widely discussed in their interpretation. Copenhagen theory deviates from this prescription, which is modeled on experience. A complete quantum experiment is "bimodal". An experimenter creates the system-under-study in initial mode of experiment, and annihilates it in the final. The (...) experimental intervention lies beyond the theory. I theorize most rudimentary bimodal quantum experiments studied by Finkelstein [2], and deduce "bimodal probability density" P=|In><Fin| to represent complete quantum experiments. It resembles core insights of the Copenhagen theory. (shrink)

This essay intends to focus on the so-called 'Literates' people of our society.It labels them as a stigma.

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has led to significant innovations in the healthcare sector. One such development is AI-powered healthcare chatbots, which assist patients and medical professionals by providing medical guidance, symptom assessment, and appointment scheduling. This paper presents the design and implementation of an AI healthcare chatbot using machine learning techniques. The chatbot leverages natural language processing (NLP) and deep learning models to understand and respond to user queries effectively. Experimental results demonstrate the (...) chatbot's efficiency in delivering accurate and context-aware medical information. (shrink)

In the digital age, chatbots have emerged as essential tools for automating communication and improving user experiences across various sectors. This paper presents a Chatbot Assistant System powered by Natural Language Processing (NLP) to provide intelligent, context-aware, and real-time responses to user queries. The system incorporates NLP techniques, such as text preprocessing, intent recognition, and entity extraction, to facilitate effective interactions. We explore the architecture, working principles, and applications of the system, along with its performance evaluation in different domains.

In today's digital landscape, small-scale companies encounter significant challenges in safeguarding their sensitive data against threats due to limited financial resources. Traditional data loss prevention solutions often come with hefty price tags, rendering them inaccessible to such organizations. Our project addresses this issue by designing a user-friendly script that can be easily deployed and managed by non- experts, lowering the barrier to entry for enhancing data security. Our project proposes an innovative approach leveraging PowerShell and ADMX to develop a cost- (...) effective data loss prevention script. This script aims to fortify the security of employee systems without necessitating expensive licenses, offering small- scale companies a practical and affordable solution to enhance their data security posture. By implementing a preventative approach to data loss prevention, small- scale companies can mitigate risks more effectively and avoid the potentially catastrophic consequences of data breaches. This shift in mindset from damage control to risk prevention reflects a strategic investment in the long-term resilience and viability of small businesses in today's increasingly digitized economy. (shrink)

Nowadays, remote monitoring systems have developed gradually to respond for particular needs in healthcare sector, which is an essential pillar in the modern concept of smart living, we propose a smart health monitoring system to monitor patient health conditions, as a smart healthcare system based on the widely spread and evolved technologies. Statistics show that severe Falls, hypertensive heart disease and blood pressure are risk factors for high death rate. To decrease it a preventive measure should be applied providing a (...) real-time health monitoring system, to save patients life at acceptable time. The objectives of this project are to provide an effective system model that will track, trace, and monitor patient’s movement, location, vital readings like heartbeat pulses, body temperature in order to provide efficient medical services in time. In proposed system data will be captured using sensors and compared with a predefined threshold. After processing it will send the Message for support to Relatives/Caretakers which will contain the Location, Heartbeat Values and Body temperature. (shrink)

Quantum theory brought an irreducible lawlessness in physics. This is accompanied by lack of specification of state of a system. We can not measure states even though they ever existed. We can measure only transition from one state into another. We deduce this lack of determination of state mathematically, and thus provide formalism for maximum precision of determination of mixed states. However, the results thus obtained show consistency with Heisenberg's uncertainty relations.