Detecting deception in an increasingly digital world is both a critical and challenging task.In this study, we present a comprehensive evaluation of the automated deception detection capabilities of Large Language Models (LLMs) and Large Multimodal Models (LMMs) across diverse domains.We assess the performance of both open-source and proprietary LLMs on three distinct datasets-real-life trial interviews (RLTD), instructed deception in interpersonal scenarios (MU3D), and deceptive reviews (OpSpam).We systematically analyze the effectiveness of different experimental setups for deception detection, including zeroshot and few-shot approaches with random or similarity-based in-context example selection.Our findings indicate that fine-tuned LLMs achieve state-of-the-art performance on textual deception detection, whereas LMMs struggle to fully leverage multimodal cues, particularly in real-world settings.Additionally, we analyze the impact of auxiliary features, such as non-verbal gestures, video summaries, and evaluate the effectiveness of different prompting strategies, such as direct label generation and post-hoc reasoning generation.Experiments unfold that reasoning-based predictions do not consistently improve performance over direct classification, contrary to the expectations.

Non-biodegradable product usage and ignorance towards proper disposal are creating the problem of ever-growing trash stacks. An autonomous mobile trash collector which collects trash lying on ground in a trash-container attached to it can be a feasible solution to the problem. In this procedure trash detection is done via deep learning algorithm. An ultrasonic sonar sensor on the robot detects object along the path and a camera module sends pictures of the object to raspberry pi for classification into trash or not trash. The prototype robot is of low-cost and can detect a wide range of trash with high accuracy. Therefore has good environmental as well as economic impact.

This paper proposes a system for converting handwritten words and numbers into a text file. Our system uses A CNN based method to identify letter and digits. This Automatic system requires image preprocessing, classifying into letters and digits and saving the letter into a text file. The system consists of a touchscreen as a user interface, an Arduino board (microcontroller ATmega 2560) and MATLAB. Any type of handwriting is tested with the classifying process; we got 89% accuracy using our own dataset. Both letter and digits can be recognized and converted into text file using this process. The proposed system will lessen the labor of creating electronic documents and provide easy preservation of data.