The term “supply chain” refers to a network of facilities that includes a variety of companies. To minimize the entire cost of the supply chain, these entities must collaborate. This research focuses on the use of artificial intelligence techniques in supply chain management. It includes supply chain management examples like demand forecasting and supply forecasting, text analytics, pricing planning, and more to help companies improve their processes, lower costs and risk, and boost revenue. It gives us a quick rundown of all the key principles of economics and how to comprehend and use them effectively.

After the coronavirus pandemic struck in 2020, administrative agencies around the globe imposed strictlockdowns in every sector. The biggest of them was imposed in India. The only things allowed were the essentialservices like medical and grocery. But due to the sudden panic of the unknown disease, there was a huge rush tobuy and stockpile items. Long queues and crowds were developed, which may have led to rapid transmission of thevirus. We, as a team, tried to find a solution by putting their shops online so that they could take orders remotely anddistribute them without the risk of long queues and infection. Gromore Shoppe was developed to make these smalllocal shops online. Retail owners can now catalogue their products on the web and take orders from the locality.They can process them quickly and distribute them with minimal risk of contact. This solution will first introducethem to the web and its benefits and make them sufficient enough so that they can even expand easily and effectively

The term “supply chain” refers to a network of facilities that includes a variety of companies. To minimize the entire cost of the supply chain, these entities must collaborate. This research focuses on the use of artificial intelligence techniques in supply chain management. It includes supply chain management examples like demand forecasting and supply forecasting, text analytics, pricing planning, and more to help companies improve their processes, lower costs and risk, and boost revenue. It gives us a quick rundown of all the key principles of economics and how to comprehend and use them effectively.

Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient’sneurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, and laborious process.Because of the limits of human sleep stage scoring, there is a greater need for creating automatic sleep stageclassification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleepand is an important step in assisting physicians in the diagnosis and treatment of associated sleep disorders. In thisresearch, we offer a unique method and a practical strategy to predict early onset of sleep disorders, such as restlessleg syndrome and insomnia, using the twin convolutional model FTC2, based on an algorithm composed of twomodules. To provide localized time-frequency information, 30-second-long epochs of electroencephalogram (EEG)recordings are subjected to a fast Fourier transform, and a deep convolutional long short-term networks neuralnetwork is trained for sleep stage categorization. Automating sleep stage detection from EEG data offers a greatpotential to tackle sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification isproposed, which combines the best of signal processing and statistics. In this study, we used the PhysioNet SleepEuropean Data Format (EDF) database. The code evaluation showed impressive results, reaching an accuracy of90.43, precision of 77.76, recall of 93,32, F1 score of 89.12, and the final mean false error loss of 0.09. All the sourcecode is available at https://github.com/timothy102/eeg.

This study aims to determine if autonomous vehicles (AV) for last-mile deliveries are sustainable from threeperspectives: social, environmental, and economic. Because of the relevance of AV applications for last-miledelivery, safety was only addressed for the sake of societal sustainability. According to this study, it is rather safe todeploy AVs for delivery since current society’s speed restrictions and decent road conditions give the foundationfor AVs to run safely for last-mile delivery in metropolitan areas. Furthermore, AV has a distinct advantage in theevent of a pandemic. The biggest worry for environmental sustainability is the emission problem. It is establishedthat AV has a substantial benefit in terms of emission reduction in terms of a series of emissions. This is mostly dueto the differences in driving behavior between autonomous cars and human vehicles. Because of AV’s commercialcharacter, this research used a quantitative approach to highlight the economic sustainability of AV. The studydemonstrates the economic benefit of AV for various carrying capacities.

The project sought to provide a paradigm for improving African education systems through the use ofthe Internet of Things (IoT). The created IoT model for Africa will enable African countries, notably Namibia, toexchange educational content and resources with other African countries. The objective behind the IoT paradigmin Africa’s education sector is to provide open access to knowledge and information. The study revealed that thereare no recognized platforms in African education systems that are utilized by African governments to interact,communicate, and share educational materials directly with African institutions. As a result, the current researchdeveloped a model for transforming African education systems using the IoT in the Namibian context, which willserve as a centralized online platform for self-study, new skill acquisition, and self-improvement using materialsprovided by African institutions of higher learning. Everyone is welcome to use the platform, including students,instructors, and members of the general public

Accounting for people is the first step for every manpower-based organization in today’s world. Hence, it takes asignificant amount of energy and value in the form of money from respective organizations for both implementinga suitable system for manpower management as well as maintaining that same system. Although this amount ofexpenditure for big organizations is near to nothing and rather just a formality, it does not hold as much truth forsmall organizations, such as schools, colleges, and even universities, to a certain degree. This is the first point.The second point for discussion is that much work has been done to solve this issue. Various technologies, likebiometrics, RFID, Bluetooth, GPS, and QR Code, have been used to tackle the issues of attendance collection.This study paves the path for researchers by reviewing practical methods and technologies used for existingattendance systems..

A fire accident is a mishap that could be either man-made or natural. Fire accidents occur frequently and can becontrolled but may, at times, result in severe loss of life and property. Many a time, firefighters struggle to sort outthe exact source of the fire as it is continuously flammable and spreads all over the area. For this concern, we havedesigned an Internet of things based device which sorts out the exact source of fire through software and hardwaredevices, and also allows the complete detail of an area to be visualized by firefighters, which is pre-installed in thesoftware itself. Because of our system’s intelligence in decision-making during firefighting, the proposed systemis claimed as a “smart firefighting system.” The implementation of the smart firefighting system can make thefirefighters analyze the current situation immediately and make the decision more quickly in an effective manner.Because of this system, fire losses in a building can be greatly reduced and many lives can be rescued immediately.Furthermore, fire spread can also be restricted.

Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human-computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.

A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spreadof non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, andsensory changes. This research explores two main categories of brain tumors: benign and malignant. Benignspreads steadily, and malignant express growth makes it dangerous. Early identification of brain tumors is a crucialfactor for the survival of patients. This research provides a state-of-the-art approach to the early identification oftumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for three-dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the diceloss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got adice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.

This article focuses on the results of self-funded quantitative research conducted by social workers working in the “refugee” crisis and social services in Greece (1). The research, among other findings, argues that front-line professionals possess specific characteristics regarding their working profile. Statistical methods in the research performed significance tests to validate the initial hypotheses concerning the correlation between dataset variables. On the contrary of this concept, in this work, we present an alternative approach for validating initial hypotheses through the exploitation of clustering algorithms. Toward that goal, we evaluated several frequently used clustering algorithms regarding their efficiency in feature selection processes, and we finally propose a modified k-Modes algorithm for efficient feature subset selection.