Journal of Advanced Research in Intelligence Systems and Robotics

Exploring the Capabilities of ChatGPT in Replacing Human Interventions: An Analysis

Puneet Joshi — Thu, 21 Dec 2023 00:00:00 +0000

This paper presents an investigation into the potential of using ChatGPT, a large language model, to replace human intervention in various tasks. The study aimed to evaluate the effectiveness of ChatGPT in terms of regarding its capability to comprehend and respond to natural language inputs, as well as its accuracy and consistency in completing tasks. The study found that ChatGPT performed well in understanding and responding to natural language inputs, and demonstrated high levels of accuracy and consistency in completing tasks. The outcome of the research suggest that ChatGPT has the possibility to be a valuable tool for automating certain tasks and reducing the need for human intervention. However, the research emphasized the necessity for additional studies to overcome limitations and enhance the model's performance in specific areas

Insights into Automated Diagnosis of Cervical Cancer

Shivam Sharma — Thu, 21 Dec 2023 00:00:00 +0000

The cervix is the last part of the uterus where cervical cancer usually develops. Identification of the cervical cells can be very taxing owing to their complexity of morphological changes in the structural parts of the cell. The problem here is that the detection process is tardy and there is not much awareness about it. By means of this study, we attempt to automate this detection process using Computer Vision Techniques like Image Segmentation, Feature Matching, etc. Through this, the patient will be able to get the correct diagnosis of her cancer in a few minutes which otherwise would take a couple of days

Involuntary Human Motion Acknowledgement to Assist Future Robotic Skills

Rohit kumar — Thu, 21 Dec 2023 00:00:00 +0000

In the ever-evolving landscape of technology, robotics and artificial intelligence have emerged as transformative forces that continue to reshape our world. The rapid progress in these fields has given rise to increasingly sophisticated and capable robots. No longer relegated to the confines of controlled environments like factories, robots now find themselves coexisting with humans in a multitude of domains, ranging from healthcare and retail to our very own homes. This shift from isolation to integration has ushered in a new era of human-robot interaction, one that is defined by the challenges posed by the nuanced and intricate dance between humans and machines. Amid these intricate interactions lies a fundamental concern – the need to comprehend, recognize, and appropriately respond to the intricate realm of human behavior, including what we refer to as "Involuntary Human Motion Acknowledgment." As technology continues to evolve, it is imperative to explore how we can harness these advancements in robotics to improve the way humans and machines interact in shared spaces. Involuntary human motions are, by definition, natural, subconscious movements and behaviors that humans exhibit in their daily lives. These can encompass a vast array of actions, from subtle gestures to shifts in posture, even the subtleties of facial expressions. While these actions may often seem inconsequential, they play a pivotal role in effective human communication and collaboration, reflecting our intentions, emotions, and states of mind. Acknowledging and interpreting these involuntary human motions is emerging as a critical facet of human-robot interaction. By doing so, we empower robots with the capability to grasp human intentions and emotions more profoundly, facilitating more effective communication, cooperation, and mutual understanding. The significance of integrating Involuntary Human Motion Acknowledgment into the fabric of robotics cannot be overstated, as it holds the potential to revolutionize the way robots operate and coexist with humans. It can enhance the safety of shared environments, enable more fluid collaboration, offer personalized assistance, and even lead to more empathetic responses from robots, thereby making them better suited to cater to human needs and preferences. However, like any transformative technological development, this progression is not without its challenges, including ethical considerations and privacy concerns, which must be meticulously addressed to strike the right balance between improving robotic skills and respecting individual privacy and autonomy.

Psychological Detection Using Artificial Intelligence

Gourav Singh — Thu, 21 Dec 2023 00:00:00 +0000

In recent years, the attention to the study of emotional content of speech signal has been increased for the interaction of human with machines. New paradigm of human-computer interaction like emotion, gesture and force-feedback are emerging. In spite of that, one imperative component for natural interaction is still missing i.e., voice. This paper describes the classification of different emotional states by using voice quality information only. Emotions that are being observed in this paper are six basic emotions which includes happy, sad, angry, surprise, disgust and neutral. Different researchers inspected dataset of various voice parameters by doing multiple experiments for detecting psychological emotions by using different voice signals and achieved a reliable recognition rate. The best accuracy that is observed after analyzing all the experiments thoroughly is around 80% which is a comparable accuracy. Though the exact accuracy for voice analyzing is not still found and human-computer voice recognition can be improved, the authors are seeking to increase the dataset by adding more voice parameters.

Plant Disease Detection using Image Processing

Aman Minch — Thu, 21 Dec 2023 00:00:00 +0000

Agriculture, the bedrock of human civilization, has been pivotal in providing sustenance, livelihoods, and enabling societal progress for thousands of years. However, the modern agriculture sector faces numerous challenges, with effective plant disease management ranking among the most pressing. Pathogens such as bacteria, fungi, viruses, and pests can lead to substantial crop yield losses, imperiling global food security. To combat this persistent threat and fortify agricultural systems, innovative technologies are being harnessed, and one of the most promising solutions is the application of image processing for plant disease detection. Traditional approaches to plant disease detection typically rely on the expertise of agronomists and farmers through manual inspections. Nonetheless, this method is time-consuming, prone to human error, and reliant on subjective judgment. Furthermore, many plant diseases manifest at their early stages without distinct visible symptoms, intensifying the challenge of early detection. In this context, image processing technology emerges as a transformative solution to these long-standing issues. Plant disease detection via image processing leverages digital imagery, artificial intelligence, and computer vision. It encompasses the acquisition of high-resolution images of plant parts, such as leaves, stems, or fruits, followed by their analysis using advanced algorithms. The process commences with the capture of images through various means, including handheld cameras, drones, or other specialized imaging devices, forming the foundation for subsequent analysis. After acquiring images, they typically undergo a series of preprocessing steps to enhance their quality. These preprocessing steps encompass noise reduction, color correction, and other adjustments that enhance image clarity and consistency. The accuracy of disease detection is heavily reliant on the quality of the input data. Ethical and privacy concerns also come into play. Collecting, storing, and sharing agricultural images raise issues related to data privacy and ownership, necessitating attention to ethical and legal aspects, including consent and data protection. Scaling and adoption of image processing technologies require widespread awareness, education, and user-friendly tools and resources for farmers. Ensuring that the benefits of image-based disease detection reach a wide range of agricultural communities is a significant challenge.