International Conference Control and Robots 

About: International Conference Control and Robots is an academic conference. The conference publishes majorly in the area(s): Robot & Robot kinematics. Over the lifetime, 288 publications have been published by the conference receiving 367 citations.

ASSMA-SLM: Autonomous System for Smart Motor-Vehicles integrating Artificial and Soft Learning Mechanisms

Abstract: As the world's population and development increase, the demand for motor-vehicles increases, adversely impacting the environment and smart cities by increasing traffic congestion. An unsystematic traffic management in smart cities may cause serious concerns, including pollution, loss of time, traffic irritation, and stress. An autonomous system for smart motor-vehicle is an important research domain of recent times worldwide that is gaining importance, disrupting the automotive industry in a big way. In the coming decades, as transportations evolve into autonomous motor-vehicles and on-demand services, the results may be a major paradigm shift in how motor-vehicles move people and goods around the physical world. Driverless motor-vehicles may change the perception of time, space, how they communicate to work, where they exist and how they work. Artificial and soft learning/computing mechanisms are efficient approaches that may provide a platform for developing an autonomous system for smart motor-vehicles to facilitate the citizens and smart city administrators while routing the traffic smoothly and intelligently. In this research work, an Autonomous System for Smart Motor-Vehicles integrating Artificial and Soft Learning Mechanisms (ASSMA-SLM) is proposed that proves helpful in monitoring, routing, and predicting smart city mobility in a smooth as well as efficient way. After the simulation, it is clearly shown that the proposed system results are better as compared to the previously published approaches in terms of 90.28% accuracy and 9.72% miss-rate.

Intelligence Skin Cancer Detection using IoT with a Fuzzy Expert System

Abstract: Skin cancer is a widespread and fatal disease in developing countries nowadays. Hence initial phase medication is possible. Skin cancer arose due to the aberrant expansion of skin cells. There could be many other feasible causes for skin cancer. It is so difficult to detect skin cancer. Melanoma and non-melanoma are frequent categories of skin cancer. It is anticipated that virtually 2000 people die due to non-melanoma lesions. In this paper proposed skin cancer detection technique is applied using a fuzzy expert system established on asymmetry, color, border, evolving, and diameter. Simulation results show better results than earlier published results.

Smartphone Security Hardening: Threats to Organizational Security and Risk Mitigation

Abstract: This article aims to identify some of the most significant security risks an organization may face if it does not consider the inherent risks associated with personal devices in the workplace. Also, mention some security measures, such as secure configurations (hardening) and tools, to mitigate these risks and strengthen the current working environment's security baseline. A security review of personal mobile devices raises awareness among employees and employers so that sensitive information is handled securely for both parties.

Classification of Skin Cancer empowered with convolutional neural network

Abstract: Cancer is a major cause of death for many people around the world. There are a number of types of cancers and they are curable; only if it is detected at its early stages. Skin cancer has increasing victims all around the world. Many Computers based diagnoses have been developed to deal with this disease and help the physician to classify and detect the occurrence of the cancer. In this study, a reliable methodology has been proposed to deal with the lung's cancer classification based upon the data of 3600 pictures (224 x 224). There are two classes of images: Malignant and benign. Each class contains 1800 images. A reliable system is developed using a Convolutional Neural Network and fully connected layers. The proposed model improved the accuracy to 86.23% with efficient computations.

Severity Monitoring Device for COVID-19 Positive Patients

Abstract: In this paper we have designed an intelligent wearable device and developed the corresponding algorithm, for COVID-19 positive patients that is capable of predicting and notifying the increase in severity of the virus. The device uses ESP 32: Node MCU, MAX 30102: Pulse Oximeter and Heart rate sensor, LM35: Temperature sensor and a vibration sensor. This device will monitor the patient's body condition such as heart pulse rate, oxygen saturation level, body temperature, hand movements due to restlessness and process this information simultaneously. Consequently, when the virus is predicted to advance to its next stage, an alert will be sent to the person taking care of the patient. Hence, this device will inform when the patient is advancing from mild to the moderate or serve condition of COVID-19. The paper gives a deep understanding on the use of this device.