Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection model.The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands.Additionally,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate assistance.This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader audience.The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI.Notably,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual impairments.The modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these advancements.Crucially,the solution places the user at its core.Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

Convolutional Neural Network Model for Fire Detection in Real-Time Environment

Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing massive fiscal and human life casualties.However,Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco.The authors have proposed an early fire detection system uses machine and/or deep learning algorithms.The article presents an Intelligent Industrial Monitoring System(IIMS)and introduces an Industrial Smart Social Agent(ISSA)in the Industrial SIoT(ISIoT)paradigm.The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices.Every Industrial IoT(IIoT)entity gets authorization from the ISSA to interact and work together to improve surveillance in any industrial context.The ISSA uses machine and deep learning algorithms for fire-related incident detection in the industrial environment.The authors have modeled a Convolutional Neural Network(CNN)and compared it with the four existing models named,FireNet,Deep FireNet,Deep FireNet V2,and Efficient Net for identifying the fire.To train our model,we used fire images and smoke sensor datasets.The image dataset contains fire,smoke,and no fire images.For evaluation,the proposed and existing models have been tested on the same.According to the comparative analysis,our CNN model outperforms other state-of-the-art models significantly.

Cogent and Energy Efficient Authentication Protocol for WSN in IoT

Given the accelerating development of Internet of things(IoT),a secure and robust authentication mechanism is urgently required as a critical architectural component.The IoT has improved the quality of everyday life for numerous people in many ways.Owing to the predominantly wireless nature of the IoT,connected devices are more vulnerable to security threats compared to wired networks.User authentication is thus of utmost importance in terms of security on the IoT.Several authentication protocols have been proposed in recent years,but most prior schemes do not provide sufficient security for these wireless networks.To overcome the limitations of previous schemes,we propose an efficient and lightweight authentication scheme called the Cogent Biometric-Based Authentication Scheme(COBBAS).The proposed scheme is based on biometric data,and uses lightweight operations to enhance the efficiency of the network in terms of time,storage,and battery consumption.A formal security analysis of COBBAS using Burrows–Abadi–Needham logic proves that the proposed protocol provides secure mutual authentication.Formal security verification using the Automated Validation of Internet Security Protocols and Applications tool shows that the proposed protocol is safe against man-in-the-middle and replay attacks.Informal security analysis further shows that COBBAS protects wireless sensor networks against several security attacks such as password guessing,impersonation,stolen verifier attacks,denial-of-service attacks,and errors in biometric recognition.This protocol also provides user anonymity,confidentiality,integrity,and biometric recovery in acceptable time with reasonable computational cost.

Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition

This work presents a spoken dialog summariza- tion system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of au- tomatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possi- bility and potential of the proposed system.