Modeling and Optimizing of Strategic/Tactical Production Planning Problems

Satisfying customer demand at an optimal cost is the most important concern forthe high-level management of every company. This dissertation details i) thedevelopment of a strategic/tactical model for the distribution of productionresponsibilities to different sites/factories and ii) the design of an inter-area logistics flowensuring demand satisfaction, by consider the production capabilities of each site, whileminimizing the total costs of production output. A mixed integer program, which includesthe supply of raw materials and the distribution of finished products in the respectivemarkets, was proposed to manage this production problem. This concept encompassedtwo case studies: the first involved a scenario in which setup costs were identical (Case1); the second entailed setup costs that differed from product to product (Case 2) todetermine the optimal costs by understanding the role of the setup costs.This model also simultaneously automatically assigns a production job to aparticular factory and transports the finished goods among the sites, if the productioncosts at those sites are relatively higher than the transportation costs. CPLEX solver, usedfor the numerical analysis, determined that this proposed formulation could indeedmanage such a complex problem. These experiments were also used to predict the role ofFixed and Setup costs on the percentage of products transferred among the companies forpurposes of satisfying the demand

BORDER SURVEILLANCE USING FACE RECOGNITION, MOBILE OTP AND EMAIL

Expanding strains over Indian borders with illegal crossings and examining past assaults on the nation, it is clear that in a large portion of the cases, security powers are uninformed of the movement of these interlopers. For this reason, a framework is needed to manage the border issue that would be equipped for working in sloping landscapes where there is no power. This paper manages identification and situating of interlopers crossing the border utilizing PIR sensors and cameras. In the event of any undesirable crossing in the area, the sensor quickly detects it and the camera will stream pictures to the base station (BS). Relying upon the guidance originating from the BS, the sensor will either activate the camera for further streaming or turn it off. The objective of this paper is to give a framework that will help the Border Security Force (BSF) in controlling all sorts of illicit activities near the outskirt in a superior and precise manner. ABSTRAK: Merentas isu sempadan India dengan kegiatan pencerobohan sempadan dan dengan mengambil kira kutukan lepas terhadap bangsa kami, adalah jelas dalam banyak-banyak kes ini pegawai keselamatan tidak diberitahu tentang bahagian yang dicerobohi. Dalam keadaan ini, kita memerlukan rangka kerja bagi mengurus masalah sempadan di mana kelengkapan perlu dipasang di tebing landskap yang tidak mempunyai sumber tenaga. Kajian ini mengurus identiti pengenalan dan kedudukan kegiatan haram yang berleluasa di sempadan dengan mengguna pakai pengesan PIR dan kamera. Apabila terdapat perubahan pergerakan yang tidak diingini di sempadan, PIR akan mengesan pergerakan dengan cepat dan kamera akan menggaris arus gambar-gambar ke stesen utama (BS) dan bergantung kepada panduan pengkalan di BS, pengesan akan membuat kamera lebih bergaris arus atau berhenti merekod. Kajian ini pentng bagi menunjukkan rangka yang membantu Penguatkuasaan Keselamatan Sempadan (BSF) dalam mengawal semua kegiatan haram berhampiran sempadan dengan bermutu dan tepat

Deep Reinforcement Learning with Importance Weighted A3C for QoE enhancement in Video Delivery Services

Adaptive bitrate (ABR) algorithms are used to adapt the video bitrate based on the network conditions to improve the overall video quality of experience (QoE). Recently, reinforcement learning (RL) and asynchronous advantage actor-critic (A3C) methods have been used to generate adaptive bit rate algorithms and they have been shown to improve the overall QoE as compared to fixed rule ABR algorithms. However, a common issue in the A3C methods is the lag between behaviour policy and target policy. As a result, the behaviour and the target policies are no longer synchronized which results in suboptimal updates. In this work, we present ALISA: An Actor-Learner Architecture with Importance Sampling for efficient learning in ABR algorithms. ALISA incorporates importance sampling weights to give more weightage to relevant experience to address the lag issues with the existing A3C methods. We present the design and implementation of ALISA, and compare its performance to state-of-the-art video rate adaptation algorithms including vanilla A3C implemented in the Pensieve framework and other fixed-rule schedulers like BB, BOLA, and RB. Our results show that ALISA improves average QoE by up to 25%-48% higher average QoE than Pensieve, and even more when compared to fixed-rule schedulers.Comment: Number of pages: 10, Number of figures: 9, Conference name: 24th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM

Document clustering for knowledge synthesis and project portfolio funding decision in R&D organizations

The paper discusses a method of using document clustering for information/knowledge synthesis and decision facilitation in R&D organisations. The emerging methodologies of machine learning, artificial intelligence and data science in conjunction with fuzzy mathematics can be optimally exploited to catalyse development of information bank for research organisations. This knowledge ecosystem can be utilized by the proposed mechanism to accelerate and reinforce interdisciplinary research for R&D organisations and empower them to make efficacious information-driven decisions related to project portfolio selection and proposal funding

Analysis of Fog Computing for IOT Security

Distributed processing efficiency provided by fog computing, IoT needs an autonomic security approach. It is because IoT devices are deployed in both the managed and the unmanaged environments. The devices in unmanaged environment are more vulnerable to cyber-attacks. The new IoT produces large amounts of data from millions of connected devices which needs low latency analytics. Fog computing will fulfill this need. Fog nodes provide an abstraction layer that masks the heterogeneity between devices and offers a consistent, virtualization programmable interface

Impact of Principal Component Analysis on the Performance of Machine Learning Models for the Prediction of Length of Stay of Patients

Patient inflow, limited resources, criticality of diseases and service quality factors have made it essential for the hospital administration to predict the length of stay (LOS) for inpatients as well as outpatients. An efficient and effective LOS prediction tool can improve the patient care and minimize the cost of service by increasing the efficiency of the system through optimal allocation of available resources in the hospital. For predicting patient’s LOS, machine learning (ML) models can have encouraging results. In this paper, five ML algorithms, namely linear regression, k- nearest neighbours, decision trees, random forest, and gradient boosting regression, have been used to predict the LOS for the patients admitted to the hospital with some medical history, laboratory measurements, and vital signs collected before admission. Additionally, the impact of principal component analysis (PCA) has been analyzed on the predictive performance of all ML algorithms. A five-fold cross-validation technique has been used to validate the results of proposed ML model. The results concluded that the RF and GB model performs better with  score of 0.856 and 0.855 respectively among all the ML models without using PCA. However, the accuracy of all the models increased with the PCA except KNN and LR. The GB model when used with principal components has  score and MSE approximate to 0.908 and 0.49 respectively compared to the model that incorporates with the original data. Additionally, PCA has an advantageous effect on the DT, RF and GB models. Therefore, LOS for new patients can be predicted effectively using the proposed tree-based RF and GB model with using PCA

Pierre Robin syndrome: a case report

Pierre Robin syndrome is characterized by micrognathia, glossoptosis and palatal malformation. We report a case of a 6 day neonate who presented with complaints of feeding and respiratory difficulty and was later diagnosed as case of Pierre Robin syndrome

Klippel-Feil syndrome: a case report

Klippel-Feil Syndrome (KFS) is defined as congenital fusion of two or more cervical vertebrae. The most common signs are short neck, low hairline at the back of head and restricted mobility of neck. We report a case of a neonate who presented with complaint of respiratory difficulty and later diagnosed as case of Klippel-Feil syndrome

Formulation, Development and Evaluation of Montelukast Sodium Chewable Tablets.

The oral route of drug administration is the most important method of administering drug for systemic effects. Except in certain case the parental route is not routinely used for self administration, e.g. insulin. The topical route of administration has only recently been employed to deliver drugs to the body for systemic effect. The parental route of administration is important in treating medical emergencies in which the subject is comatose or cannot swallow. Nevertheless it is probable that at least 90% of all drugs used to provide systemic effect are administered by oral route. When a new drug is discovered one of the first question a pharmaceutical company asks is whether or not the drug can be effectively administered for its intended effect by oral route. Drugs that are administered orally, solid oral dosage forms represent the preferred class of product. Tablet and capsules represent unit dosage forms in which usual dose of drug has been accurately placed. It can be concluded that chewable tablets of Montelukast sodium can be prepared by wet granulation method using croscarmellose sodium as superdisintegrant. Chewable tablets of Montelukast sodium with 3% of croscarmellose sodium gave better drug release of 98% with minimum disintegration time with pleasant taste .This will improve patient compliance and increase in bioavailability