Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emotions. In the experiment of classifying five types of emotions: Anger, Sad, Surprise, Happy, and Neutral. As result the overall classification rate as high as 95% is achieved

A New Design for Alignment-Free Chaffed Cancelable Iris Key Binding Scheme

Iris has been found to be unique and consistent over time despite its random nature. Unprotected biometric (iris) template raises concerns in security and privacy, as numerous large-scale iris recognition projects have been deployed worldwide—for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. Template protection schemes from biometric cryptosystems and cancelable biometrics are expected to restore the confidence in biometrics regarding data privacy, given the great advancement in recent years. However, a majority of the biometric template protection schemes have uncertainties in guaranteeing criteria such as unlinkability, irreversibility, and revocability, while maintaining significant performance. Fuzzy commitment, a theoretically secure biometric key binding scheme, is vulnerable due to the inherent dependency of the biometric features and its reliance on error correction code (ECC). In this paper, an alignment-free and cancelable iris key binding scheme without ECC is proposed. The proposed system protects the binary biometric data, i.e., IrisCodes, from security and privacy attacks through a strong and size varying non-invertible cancelable transform. The proposed scheme provides flexibility in system storage and authentication speed via controllable hashed code length. We also proposed a fast key regeneration without either re-enrollment or constant storage of seeds. The experimental results and security analysis show the validity of the proposed scheme

Customer Relationship Management Dashboard with Descriptive Analytics for Effective Recommendation

Businesses looking to understand customer behaviour and take advantage of competitive advantages can benefit greatly from the Customer Relationship Management (CRM) dashboard with analytics capabilities. Organisations can effectively track and analyse key data, such as client interactions, purchase histories, and demographics, thanks to this userfriendly dashboard. The CRM dashboard provides data in a clear and straightforward manner by utilising a variety of visualisation techniques including bar charts, line charts, and heatmaps, allowing organisations to learn more about customer journeys, patterns, and behavioural trends. Additionally, the CRM dashboard's integrated recommender system is crucial. This system makes personalised product or service suggestions to clients based on their prior interactions and purchasing behaviour, improving engagement, and eventually boosting revenues. The CRM dashboard with analytics capabilities offers a complete solution for firms looking to manage and analyse customer data and interactions thanks to its user-friendly interface, visualisations, and recommender system. The CRM dashboard improved with suggestion elements is reviewed in this article along with a framework that combines analytics and visualisation capabilities

Advancing Retail Operations: A Customizable IoT-Based Smart Inventory System

The retail sector has encountered formidable challenges in recent years, particularly concerning food sustainability and the need for reduced manpower, which have been further exacerbated by the COVID-19 pandemic. The inventory management process involves critical tasks such as environment checking, product inspection, and stock arrangement, all of which are essential for maintaining product quality. Price label management is another crucial aspect of retail operations, providing key information to potential customers. However, the labor-intensive process of installing and replacing price labels, as well as adapting to market trends, poses efficiency and sustainability concerns. To address the aforementioned challenges, we have proposed an IoT-based inventory management system that consists of three interconnected components: a smart shelf that keeps real-time track of humidity, temperature, and air index; electronic shelf labeling that allows for easy updating of product information from mobile/PC devices; and RFID-based stock sorting to track product in/out and location. Hence, the proposed integrated solution significantly enhances operational efficiency, reduces overall workload, optimizes inventory management tasks, streamlines operations, and mitigates financial losses associated with inefficient processes

Cashierless Checkout Vision System for Smart Retail using Deep Learning

As Corona Virus Disease (COVID-19) pandemic strikes the world, retail industry has been severely impacted by staff shortage and high risk of virus outbreak. However, most of existing smart retail solutions is associated with high deployment and maintenance cost that are infeasible for small retail stores. As an effort to mitigate the issue, a computer vision-powered smart cashierless checkout system is proposed based on You Only Look Once (YOLO) v5 and MobileNet V3 for product recognition along with 3-stage image synthesis framework that includes crop and paste algorithm, GAN-based shadow synthesis and light variation algorithm. By using 3000 images generated from the framework, proposed model was trained and optimized with TensorRT. Experimental result shows that the lightweight model can be deployed on affordable edge devices like Jetson Nano while achieving high Mean Average Precision (mAP) of 98.2%, Checkout Accuracy (cAcc) of 89.17% with only 0.142s of inference time