Coordinates: 15°19′37″N 73°56′00″E / 15.32694°N 73.93333°E / 15.32694; 73.93333Padre Conceição College of Engineering (PCCE) is a private engineering college in Verna, Goa, India, established in 1997. The college is affiliated to Goa University, Taleigao, Goa, and the programmes are approved by All India Council for Technical Education (AICTE), New Delhi. The college is a part of Agnel Technical Education Complex, Verna, Goa and the college campus was designed by civil engineer Olavo Carvalho. PCCE was the first private engineering college in the state. The students of PCCE call themselves as Pacers.
Surveillance systems are being deployed and widely used in various areas such as traffic monitoring, airports, shopping centers and colleges. Although surveillance systems are active 24 × 7, detection of unusual activities is not possible by manual monitoring as it could be prone to errors. Hence, detection of unusual activity is a demanding area of research. This work aims to identify unusual activities happening in a campus setting such as fights and vandalism using videos from CCTV cameras. It proposes a smart system using a Long-Term Recurrent Convolution Network (LRCN). LRCN handles long video frame sequences and variations in lighting, making it suitable for real-world surveillance scenarios. The dataset used in this work is collected from college campuses and comprises of normal and unusual activities with the test and train split ration being 75
Sentiment analysis utilizes natural language processing to obtain and classify sentiment from data in the text form. The paper emphases on the classification of mobile product reviews using various algorithms. The experimentation is conducted on the two datasets obtained after cleaning where one dataset accounts for the negations in sentences whereas the other dataset does not. The experimentation process involves combining the feature extraction techniques with the different algorithms. Feature extraction is implemented uses a Count Vectorizer (CV) and Term Frequency – Inverse Document Frequency (TF-IDF). The classification is executed utilizing machine learning techniques like Naïve Bayes, Support Vector Machine, Random Forest, Logistic Regression, and deep learning techniques, including Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and a hybrid of BERT with Bi-Directional LSTM. The results show that among machine learning algorithms, RF performs the best with both extraction techniques giving accuracies of 0.9774 with TF-IDF and 0.972 with CV. Among deep learning models, the execution of BERT model by itself provides the best accuracy of 0.9863. It is observed that the dataset handling sentence negation improves the execution of all algorithms. This comprehensive evaluation highlights the effectiveness of both standard machine learning and advanced deep learning methods for analyzing sentiments.
This paper helps enhance the quality of life of the visually impaired through better mobility. These comprise of sound processing, LiDAR, ultrasonic real-time obstacle detection, and webcam-based environment capture. Voice control lets users use voice commands to rotate the wheelchair while audio guidance helps the operator. LiDAR and ultrasonic sensors can either avoid an object or let the user know that the object is present through the speakers. It captures facial expressions, communicates them to users through the sound system, and alerts them on interferences. The integration of voice control, intelligent sensing, and auditory feedback thus extends independence, safety, and quality of life, for users who are with visual impairments.
With the growth in architectural intricacy of indoor spaces, individuals encounter challenges in orienting themselves within such spaces which underscores the need for user-centric navigation solutions. Using this application, users can find their way around the grocery aisles to grab what they need or locate a store within an intricate market by providing the visual guidance through their smartphone. 2D visual markers are the core components used in this proposed system which is fueled by augmented reality. Multiple visual markers are scattered within the indoor space which upon scanning relocates the user and assists them using arrows on their screen to their desired destination.
An innovative approach for efficient and controlled drone-based building inspection has been introduced in this work. By integrating hardware and simulation, the system ensures efficient testing and validation using jMAVSim. Unlike conventional techniques that rely on onboard computing devices for real-time object detection and continuous wireless data transmission, posing bandwidth challenges and limited user control over the drone’s actions during detection, our approach utilizes long-range data transmission, eliminating onboard computing needs. The system establishes a networked drone damage detection system (DDS), offering real-time outputs and user control for efficient structural inspection, making it an efficient autonomous solution for structural inspection. The proposed system utilizes a ground station (Laptop) as a hardware platform, integrating YOLO-v3 for object detection, severity classification and action generation based on live video streamed from a drone’s long-range transmitter. The system’s efficacy is assessed using a dataset featuring diverse damage types. During a survey, if damage is detected, the ground station system employs severity classification to trigger MAVLink commands, pausing the mission. Based on detected damage severity, action decisions are made and transmitted to the drone through telemetry. Test outcomes demonstrate the ground station system’s capability to detect cracks and appropriately respond such as halting a survey when structural damage is identified. The proposed method achieves a mean average precision (mAP) of 74.67%, processing 9 to 11 frames per second with a batch size 34. This innovative approach optimizes building inspections by leveraging drone technology, offering enhanced precision, reduced risk and streamlined operations.
