Lokesh Kumar1, AnubhavSingh2, 1Senior Staff Engineer @Nagarro, Dehradun, India, 2Associate Data Scientist, @Mathco, Chandigarh, India
In the modern era, surveillance systems, notably Closed-Circuit Television (CCTV) technology, have undergone a significant evolution through the integration of computer vision algorithms and deep learning methodologies, enabling them to carry out a multitude of tasks and objectives. However, a considerable portion of these systems remains far from achieving the distinction of 'smart' CCTV. In response, we introduce an innovative approach that harnesses multi-model techniques to enhance the effectiveness of CCTV supervision across a wide array of scenarios. The omnipresence of CCTV systems, functioning around the clock, leads to the accumulation of vast volumes of data, a substantial portion of which eventually becomes redundant. The conventional approach requires human supervisors to painstakingly review captured frames and subsequently deduce information pertaining to the events within those frames. This process, apart from being labour-intensive, is also time-consuming. Our novel multi-model approach represents a transformative paradigm shift within this landscape. It not only enables the system to comprehend visual data but also provides real-time analysis, alerts, and action-based responses. This advancement carries the potential to revolutionise the utilisation of CCTV systems, optimising their role in surveillance and security operations. As the world continues to witness the widespread deployment of CCTV technology, our research highlights the paradigm shift brought about by multi-model approaches, offering a pathway towards the development of intelligent, efficient, and proactive surveillance systems. This work marks our initial step in exploring the multi-modal capabilities required to understand, reason, and formulate sequences of action to enhance the current state of CCTV technology. To this end, we first conduct experiments on 87 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, GPT-3.5, and GPT-4. These experiments show LLMs have a grasp of reasoning and can understand a scenario and act on it.
CCTV, Large Language Models, Surveillance, Alert Generation, Image to Text & Question and Answers on multiple images.
Wang Huifengl1 and Wu Jianfeng2, 1School of Electronic &Control Engineering Chang’an University, Xi’an 710064, Shaanxi, Chin, 2School of Information Engineering Chang’an University, Xi’an 710054, Shaanxi, China
Road traffic sign is a important facility to manage traffic and indicate the direction of traveling to ensure smooth road and driving safety. However, sign is usually small target, which is prone to the problem of missed detection and false detection. In addition, the deeplabv3+ model is a representative semantic segmentation network. It employs dilated convolution in the ASPP module, which is prone to lose small target information while expanding the sensory field. Aiming at above problem, an improved deepswin small target semantic segmentation based on deeplabv3+ is proposed. First of all, the ASPP module is replaced by swin-transformer block to enhance the signage feature extraction capability. Then, a channel and spatial fusion attention(CSFA) mechanism based on CBAM attention mechanism is utilized to enhance the extraction capability of channel and spatial features. The experimental results show that compared with the original network, the MIOU and mPA of the network proposed in this paper are improved by 4.0% and 4.9%, respectively.
Road traffic sign, deep learning, semantic segmentation, swin-transformer, attention mechanism.
Carlos Carrion R, Department of Data Science Technology, Andres Bello High Tech Institute, Quito, Ecuador
It is estimated that only 12% of financial/tax fraud and less than 30% of electoral fraud worldwide are detected with clear evidence, making it likely that intentional anomalies will increase aided by Artificial Intelligence and commercial access with quantum processors, since the amount of economic benefits and power is tempting, because it involves bigData, trying to clean tracks and eliminate evidence that certain systems and applications allow or do not consider taking care of. In Monitoring Audit of the normal operations behavior with help of Data Science and Technological advances, the variants of Benford's Law are available as a starting point at general level to know percentage of suspicion of values that do not comply with it; therefore, a technique is important to confirm these suspicions by more than 95% and specifically locate them through Dendrograms so that the data elements that produced suspicion are analyzed in depth, which is reason for this methodological proposal.
Types of Fraud, bigData, Artificial Intelligence, Benford Law, Dendrograms, Normal Operations Behavior.
Carlos Carrion R, Department of Data Science Technology, Andres Bello High Tech Institute, Quito, Ecuador
Millions of business and organizations users in the world use MS Excel spreadsheet every hour and have kept their records with calculations without considering there is any slight difference in results even if key decisions are made, such as in accounting area, when using 2 widely used conditional functions (SUMIF, COUNTIF) that in their main comparison parameter may have overflowing if it is floating point, which is used to save storage and processing due to the presence of large volume of data for the accounting action of business operations. Even more so today, with AI and social networks, millions of records are generated in business operations, it justify to abbreviate storage of business identification by country/city or IP addresses (IPv4/IPv6) where it can easily exceed 15 digits, to arrange them in floating point and use conditional functions in spreadsheet to group or differentiate them, which with this failure is sure to make wrong decisions.
Accounting Spreadsheet, Wrong Results, Artificial Intelligence, Business Operations, Software Failure.
Olga Fenina, Eurasian Nationality University by named L. Gumilev, Astana, Kazakhstan
As is known an Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, ANN has emerged over the years and has made remarkable contribution to the advancement of various fields of activities. In its turn cryptography is the one of the main categories of computer security that changes information from its normal form into an converted form. The purpose of this paper is to using neural networks on Cryptography. In this paper also, author has examined and analyzed the alternative encryption algorithms and criteria for its evaluating.
Artificial Neural Network, Cryptography, Alternative encryption algorithms, Evaluating, Criteria.
Ismat Saira Gillani1, Muhammad Rizwan Munawar2, Muhammad Talha3, 1Department of Computer Science, Columbus State University, USA, 2Department of Computer Science, COMSATS University, Pakistan, 3Department of Electrical Engineering, GC University Faisalabad, Pakistan
A recent study by the International Agency for Research on Cancer (IARC) predicts a 50% rise in yearly cancer cases by 2040, with one in five Americans facing a diagnosis in their lifetime. UV overexposure is the main cause of skin cancer, often hard to detect early due to its similarities. Recent tech advances aid detection, but challenges persist. Here, we introduce a deep learning model for skin cancer detection, outperforming prior methods like ResNet and AlexNet in accuracy and speed. Our model demands fewer features and achieves State of the Art results. Find the code at: https://github.com/RizwanMunawar/skin-cancer-binary-classification-computer-vision.
Deep learning, Object Detection, Feature Extraction, Network Architecture.
