RECIPE SUGGESTION TOOL

ABSTRACTThere is currently a great need for a tool to search cooking recipes based on ingredients. Current search engines do not provide this feature. Most of the recipe search results in current websites are not efficiently clustered based on relevance or categories resulting in a user getting lost in the huge search results presented.Clustering in information retrieval is used for higher efficiency and better presentation of information to the user. Clustering puts similar documents in the same cluster. If a document is relevant to a query, then the documents in the same cluster are also relevant.The goal of this project is to implement clustering on recipes. The user can search for recipes based on ingredient

Integration of feature distributions for colour texture segmentation

This paper proposes a new framework for colour texture segmentation and determines the contribution of colour and texture. The distributions of colour and texture features provides the discrimination between different colour textured regions in an image. The proposed method was tested using different mosaic and natural images. From the results, it is evident that the incorporation of colour information enhanced the colour texture segmentation and the developed framework is effective

In Vitro Anti Inflammatory, Anti-Oxidant and Anti-Cancer Cell Line Studies on Mollugo Cerviana (L.) Ser

Our traditional treatises on medicine have shown references to number of rare medicinal herbs prescribed to treat various ailments in human. An herbal plant preferred to treat fever, inflammation, rheumatic pain and wounds prevalently in the villages of the delta districts of Tamilnadu by name Mollugo cerviana was chosen for this research. The Ethanol extract of Mollugo cerviana was analysed for its in vitro anti-inflammatory, antioxidant and anticancer activity through standard procedures. The anti-inflammatory activity of the ethanol ethanolic extracts of varying concentrations were evaluated under HRBC (Human Red Blood Cell) Membrane Stabilization and Protein denaturation studies. Diclofenac sodium a nonsterioidal anti-inflammatory drug was the reference standard. The results revealed a concentration dependent increase in the percentage of membrane stabilization activity with increase in concentration of the test extract and a concentration dependent inhibition of protein (albumin) denaturation activity. Antioxidant studies revealed a significant free radical scavenging activity by the extract upon evaluation by DPPH free radical scavenging assay, nitric oxide assay and hydrogen peroxide radical scavenging activity assay. The studies on liver cancer cell lines using MTT assay revealed the anticancer activity exhibited by the ethanol extract of Mollugo cerviana. The studies as a whole reveals the anti-inflammatory, antioxidant and anticancer activity possessed by the herbal extract of Mollugo cerviana which invites attention to proceed with further research towards development of novel herbal drugs from Mollugo cerviana to treat inflammation and cancer

Efficient Dense 3D Reconstruction Using Image Pairs

The 3D reconstruction of a scene from 2D images is an important topic in the _x000C_eld of Computer Vision due to the high demand in various applications such as gaming, animations, face recognition, parts inspections, etc. The accuracy of a 3D reconstruction is highly dependent on the accuracy of the correspondence matching between the images. For the purpose of high accuracy of 3D reconstruction system using just two images of the scene, it is important to _x000C_nd accurate correspondence between the image pairs. In this thesis, we implement an accurate 3D reconstruction system from two images of the scene at di_x000B_erent orientation using a normal digital camera. We use epipolar geometry to improvise the performance of the initial coarse correspondence matches between the images. Finally we calculate the reprojection error of the 3D reconstruction system before and after re_x000C_ning the correspondence matches using the epipolar geometry and compare the performance between them. Even though many feature-based correspondence matching techniques provide robust matching required for 3D reconstruction, it gives only coarse correspondence matching between the images. This is not su_x000E_cient to reconstruct the detailed 3D structure of the objects. Therefore we use our improvised image matching to calculate the camera parameters and implement dense image matching using thin-plate spline interpolation, which interpolates the surface based on the initial control points obtained from coarse correspondence matches. Since the thin-plate spline interpolates highly dense points from a very few control points, the correspondence mapping between the images are not accurate. We propose a new method to improve the performance of the dense image matching using epipolar geometry and intensity based thin-plate spline interpolation. We apply the proposed method for 3D reconstruction using two images. Finally, we develop systematic evaluation for our dense 3D reconstruction system and discuss the results

Angles

Measurement of line segments and the concept of length arise naturally in students. However, measurement of a turn or an angle formed between two rays is more complex to understand. Comprehending the concept of a degree as a measure of a turn and developing the skill of using a protractor takes time

GLOBAL CHALLENGES IN ACCESSING MENTAL HEALTH SERVICES AND ADDRESSING THE IMPACT OF ALZHEIMER\u27S DISEASE AND DEPRESSION

This research project focuses on developing a quantum sensing system that can detect biomarkers associated with health disorders, like Alzheimer’s and depression. Our goal is to create a sensitive and highly selective quantum sensing device using a diamond nitrogen vacancy (NV) center. To train and test our quantum machine learning algorithms we will preprocess data from the available Human Connectome Project dataset. This dataset forms the basis of our quantum-based methods. The core of our project revolves around developing quantum machine learning algorithms that utilize techniques such as Support Vector Machines and neural networks to diagnose health disorders using data from quantum sensors. The integrated quantum computing resources in our system will efficiently handle the volumes of generated data. We will tailor the quantum algorithms and software for platforms like IBM Qiskit ensuring they are well trained, optimized, precise and efficient in diagnosing these disorders. To evaluate their performance, we will compare them against AI and ML techniques using the Human Connectome Project dataset. In collaboration with health professionals and stakeholders we aim to explore applications while addressing implementation challenges and strategies, for translating our research into clinical practice. Our research project serves as a connection, between quantum technology, machine learning and mental health with the goal of enhancing precision and transforming the way we treat Alzheimer’s disease and depression. This interdisciplinary approach holds promise in improving the level of care and overall results, for individuals grappling with these health conditions

Numerical Studies on Flexural Behaviour of Carbon Fiber Reinforced Polymer-Wrapped Reinforced Concrete Beam

Construction faults and poor maintenance of concrete buildings become crucial as a result of heavy loading. Fibre reinforced polymer increases load bearing capacity, improves ductility, and decreases degradation damages. The researchers are looking for new and novel ways to strengthen beams since traditional techniques of reinforcement have limits that must be addressed. Due to its better qualities, the technology of wrapping a Reinforced Concrete (RC) beam with composite material has become popular and widely employed in structural applications. FRP has a lower labour cost and is a simple technique to reinforce buildings for a more effective solution. The experimental and analytical work was done for both conventional and CFRP strengthening with various intervals, such as CFRP wrapped with a 100mm interval, CFRP wrapped with a 200mm interval, and CFRP wrapped without an interval, with improved results in deformation and stress analysis.  All of the experimental data were compared to the calculated analytical values. When CFRP is used on a beam, it enhances its strength, load bearing capability, and ductility

Study on the Behavior of Cold-formed Steel Angle Tension Members

Cold-formed steel tension members with bolted end connections are frequently used in a variety of structures such as trusses, transmission towers etc. Among all the shapes, angles are widely used. When angle sections are connected with gusset plates and eccentrically loaded, their ultimate load- carrying capacity is influenced by the effect of shear lag. This paper presents the details of an experimental and numerical investigation with a primary objective of studying the effect of shear lag on cold-formed steel single and double angles subjected to tension. Seventy-two single plain and lipped angles made from thicknesses 2,3 and 4 mm connected to gusset plates at their ends by ordinary black bolts were tested. Forty-eight double angles of 3 and 4 mm thicknesses connected to the opposite side of gusset plate and to the same side of the gusset plate at their ends by black bolts were also tested. All the one hundred and twenty specimens were tested in an Universal Testing machine subjected to eccentric tensile load. From the test results, load vs deflection behaviour and the failure modes were studied. The actual load carried by the specimen was compared with the theoretical load carrying capacity predicted by International codal provisions and with the load carrying capacity predicted by numerical investigation by ANSYS. An empirical equation is proposed to determine the load-carrying capacity of the cold-formed steel angles and the predicted values agree with the experimental results

Optimization of SSF parameters for natural red pigment production from Penicillium purpurogenum using cassava waste by central composite design

Pencillium purpurogenum 8904.12, a red pigment producer, was isolated from soil screened and selected based on the pigment production. The pigment production by P.purpurogenum was optimizedby using factorial design and Response Surface Methodology (RSM) in SSF. Cassava waste is a low cost and nutrient rich substrate used in this study as a substrate. RSM based central composite design was employed to obtain best combination of substrate concentration, inoculum volume, incubation time, initial moisture and initial pH. By the point prediction tool of Design-Expert 8.0, the optimum values of the factors for maximum red pigment production were determined. Under the optimized conditions (substrate concentration 10 g, inoculum volume 5 ml, 15 days incubation time, 50 % initial moisture and initial pH of 6), the red pigment yield was 28.33 colour value units / g of dry fermented substrate which agreed closely with the predicted yield. The model showed that the value of R2 (0.9936) was high and pvalue of interaction of variance was <0.0001. Hence the model can be said to be of highly significant. A significant Increase in red pigment production was achieved using RSM. Thus, utilization of cassava waste for red pigment production in this study could provide the most effective use of cassava resource, and lead to technology of development for its further utilization and value addition