Over the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. There are lots of factors that affect agricultural production, with seed quality topping the list. Seed classification can provide additional knowledge about quality production, seed quality control and impurity identification. The process of categorising seeds has been traditionally done based on characteristics like colour, shape and texture. Generally, this is performed by specialists by visually inspecting each sample, which is a very tedious and time-consuming task. This procedure can be easily automated, providing a significantly more efficient method for seed sorting than having them be inspected using human labour. In related areas, computer vision technology based on machine learning (ML), symmetry and, more particularly, convolutional neural networks (CNNs) have been generously applied, often resulting in increased work efficiency. Considering the success of the computational intelligence methods in other image classification problems, this research proposes a classification system for seeds by employing CNN and transfer learning. The proposed system contains a model that classifies 14 commonly known seeds with the implication of advanced deep learning techniques. The techniques applied in this research include decayed learning rate, model checkpointing and hybrid weight adjustment. This research applies symmetry when sampling the images of the seeds during data formation. The application of symmetry generates homogeneity with regards to resizing and labelling the images to extract their features. This resulted in 99% classification accuracy during the training set. The proposed model produced results with an accuracy of 99% for the test set, which contained 234 images. These results were much higher than the results reported in related research.

Milk oxidoreduction potential was modified using gases during the production of a model dairy product and its effect on gel setting was studied. Acidification by glucono-delta-lactone was used to examine the physicochemistry of gelation and to avoid variations due to microorganisms sensitive to oxidoreduction potential. Four conditions of oxidoreduction potential were applied to milk: milk was gassed with air, nongassed, gassed with N(2), or gassed with N(2)H(2). The rheological properties and microstructure of these gels were determined using viscoelasticimetry, measurement of whey separation, and confocal laser scanning microscopy. It appeared that a reducing environment led to less-aggregated proteins within the matrix and consequently decreased whey separation significantly. The use of gas to modify oxidoreduction potential is a possible way to improve the quality of dairy products.

With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework"s security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments; latest on the scene are Machine Learning approaches. Machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.

To cite this version:Lucile Sautot, Bruno Faivre, Ludovic Journaux, Paul Molin. The Hierarchical Agglomerative Clustering with Gower index: a methodology for automatic design of OLAP cube in ecological data processing context. Ecological Informatics, Elsevier, 2015, 2 (26), pp.❝❡s ✇✐t❤ ❛ •❛r❣❡ ❞❛t❛s❡t✳ ❚❤❡r❡❜② t❤❡ •♦✇❡r ✐♥❞❡① ✐♥ ❛ ❤✐❡r❛r❝❤✐❝❛• ❛❣❣•♦♠❡r❛t✐✈❡ ❝•✉st❡r✐♥❣ ♣❡r♠✐ts t❤❡ ♠❛♥❛❣❡♠❡♥t ♦❢ ❤❡t❡r♦❣❡♥❡♦✉s ❞❛t❛s❡t ✇✐t❤ ♠✐ss✐♥❣ ✈❛•✉❡s ✐♥ ❛ ❝♦♥t❡①t ♦❢ ❛✉t♦♠❛t✐❝ ❜✉✐•❞✐♥❣ ♦❢ ❖▲❆P ❝✉❜❡✳ ❲✐t❤ t❤✐s ♠❡t❤♦❞♦•♦❣②✱ ✇❡ ❝❛♥ ❜✉✐•❞ ♥❡✇ ❞✐♠❡♥s✐♦♥s ❜❛s❡❞ ♦♥ ❤✐❡r❛r❝❤✐❡s ✐♥ t❤❡ ❞❛t❛✱ ✇❤✐❝❤ ❛r❡ ♥♦t ❡✈✐❞❡♥t✳ ❚❤❡ ❞❛t❛ ♠✐♥✐♥❣ ♠❡t❤♦❞s ❝❛♥ ❝♦♠♣•❡t❡ t❤❡ ❡①♣❡rt ❦♥♦✇•❡❞❣❡ ❞✉r✐♥❣ t❤❡ ❞❡s✐❣♥ ♦❢ ❛♥ ❖▲❆P ❝✉❜❡✱ ❜❡❝❛✉s❡ t❤❡s❡ ♠❡t❤♦❞s ❝❛♥ ❡①♣•❛✐♥ t❤❡ ✐♥❤❡r❡♥t str✉❝t✉r❡ ♦❢ t❤❡ ❞❛t❛✳ ❑❡②✇♦r❞s✿ ❖▲❆P❀ ❍✐❡r❛r❝❤✐❝❛• ❆❣❣•♦♠❡r❛t✐✈❡ ❈•✉st❡r✐♥❣❀ ❇✐r❞ P♦♣✉•❛t✐♦♥❀ ❆✉t♦♠❛t✐❝ ❉❡s✐❣♥ ■♥tr♦❞✉❝t✐♦♥✿ ✉s❡ ❞❛t❛ ♠✐♥✐♥❣ ❢♦r ❖▲❆P ❝✉❜❡ ❞❡s✐❣♥ ❙✐♥❝❡ ✶✾✾✸✱ ❖▲❆P ✭❖♥ ▲✐♥❡ ❆♥❛•②t✐❝❛• Pr♦❝❡ss✐♥❣✮ s②st❡♠s ❤❛✈❡ ❜❡❡♥ ♣r♦♣♦s❡❞ t♦ ✐♠♣r♦✈❡ ❞❡❝✐s✐♦♥ ♠❛❦✐♥❣ ♣r♦❝❡ss ❞✉❡ t♦ ❛♥❛•②s✐s ♦❢ •❛r❣❡ ❞❛t❛s❡ts ✭❈♦❞❞ ❡t ❛•✳✱ ✶✾✾✸✮✳ ❚❤✐s ❦✐♥❞ ♦❢ s♦❢t✇❛r❡ ✐s ❞❡s✐❣♥❡❞ t♦ ❡①♣•♦r❡ ❡❛s✐•② ❛♥❞ q✉✐❝❦•② ♠✉•t✐❞✐♠❡♥s✐♦♥❛• ❞❛t❛ ✭|✐✈❡st ❡t ❛•✳✱ ✷✵✵✺✮✳ ❚❤❡ ✇♦r❞ ❖▲❆P ❝❛♥ ❜❡ ❛ss♦❝✐❛t❡❞ ✇✐t❤ ❛ ♣r♦❝❡ss✱ ❛ ❦✐♥❞ ♦❢ s②st❡♠ ♦r ❛ ❦✐♥❞ ♦❢ ❞❛t❛ ✭❏❡r❜✐ ❡t ❛•✳✱ ✷✵✵✾✮✳ ❆ ❜❛s✐❝ |❡•❛t✐♦♥❛• ❖▲❆P ✭|❖▲❆P✮ s②st❡♠ ❛r❝❤✐t❡❝t✉r❡ ❝♦♥s✐sts ♦❢ ✭✐✮ ❛ r❡•❛t✐♦♥❛• ❉❛t❛ ❇❛s❡ ▼❛♥❛❣❡♠❡♥t ❙②st❡♠ ✭❉❇▼❙✮✱ t❤❛t st♦r❡s ❞❛t❛ ✐♥ ❛❝❝♦r❞❛♥❝❡ ✇✐t❤ ❞❛t❛ ✇❛r❡❤♦✉s✐♥❣ ♣❛r❛❞✐❣♠❀ ✭✐✐✮ ❛♥ ❖▲❆P s❡r✈❡r t❤❛t ✐♠♣•❡♠❡♥ts t❤❡ ♠✉•t✐❞✐♠❡♥s✐♦♥❛• ♠♦❞❡• ❛♥❞ ❖▲❆P ♦♣❡r❛t♦rs ♦♥ t♦♣ ♦❢ t❤❡ ❉❇▼❙❀ ✭✐✐✐✮ ❛♥ ❖▲❆P ❝•✐❡♥t✱ t❤❛t ❝♦♠❜✐♥❡s ❛♥❞ s②♥❝❤r♦♥✐③❡s t❛❜✉•❛r ❛♥❞ ❣r❛♣❤✐❝❛• ❞✐s♣•❛②s ❛♥❞ ❛••♦✇s q✉❡r② ❜✉✐•❞✐♥❣❀ ✭✐✈✮ ❛♥ ❊❚▲ t♦♦• t❤❛t ❡①tr❛❝ts ❞❛t❛ ❢r♦♠ ❤❡t❡r♦❣❡♥❡♦✉s s♦✉r❝❡s✱ tr❛♥s❢♦r♠s t❤❡♠ ❛♥❞ •♦❛❞s t❤❡♠ ✐♥t♦ ❛ ❞❛t❛ ✇❛r❡❤♦✉s❡✳ ■♥ t❤✐s ♣❛♣❡r✱ ✇❡ ❛r❡ ❢♦❝✉s❡❞ ♦♥ ❞❡s✐❣♥ ♦❢ ❖▲❆P s❝❤❡♠❛✱ ✇❤✐❝❤ ✐s ❞❡✜♥❡ ❜② ❯s♠❛♥ ❛s ❛ ❝♦••❡❝t✐♦♥ ♦❢ ❞❛t❛❜❛s❡ ♦❜•❡❝ts✱ ✐♥❝•✉❞✐♥❣ t❛❜•❡s✱ ✈✐❡✇s✱ ✐♥❞❡①❡s ❛♥❞ s②♥♦♥②♠s ✭❯s♠❛♥ ❡t ❛•✳✱ ✷✵✶✵✮✳ ❙❡✈❡r❛• r❡s❡❛r❝❤ ✇♦r❦s s✉❣❣❡st ♠♦❞❡•✐♥❣ ❢♦r ❖▲❆P s❝❤❡♠❛✱ t❤❛t ❡✐t❤❡r r❡•② ♦♥ ❡①✐st✐♥❣ ♠♦❞❡•s ✭❊♥t✐t②✴|❡•❛t✐♦♥s❤✐♣✱ ❖❜•❡❝t✲❖r✐❡♥t❡❞✱ ✳✳✳✮ ♦r s✉❣❣❡st ♥❡✇ ♠♦❞❡•s ✭▲❡❤♥❡r✱ ✶✾✾✽ ❀ ◆❣✉②❡♥ ❛♥❞ ❚•♦❛✱ ✷✵✵✵ ❀ P❡❞❡rs❡♥ ❛♥❞ ❏❡♥s❡♥✱ ✶✾✾✽ ❀ ❚s♦✐s ❡t ❛•✳✱ ✷✵✵✶✮✳ |❡❣❛r❞•❡ss ♦❢ t❤❡ ♠❡t❤♦❞s ❝❤♦s❡♥ ❜② t❤❡ ❛✉t❤♦rs t♦ ❞❡✜♥❡ t❤❡ r✉•❡s ♦❢ t❤❡✐r ♠♦❞❡•s✱ t❤❡s❡ ♠♦❞❡•s ❛r❡ ❜❛s❡❞ ♦♥ t❤r❡❡ ❝♦♥❝❡♣t ♦❢ ♠✉•t✐❞✐♠❡♥s✐♦♥❛• ♠♦❞❡•✐♥❣ ✿ ♠❡❛s✉r❡s✱ ❞✐♠❡♥s✐♦♥s ❛♥❞ ❤✐❡r❛r❝❤✐❡s ✭❏❡r❜✐ ❡t ❛•✳✱ ✷✵✵✾✮✳ * ❈♦rr❡s♣♦♥❞✐♥❣ ❛✉t❤♦r✳ ❊♠❛✐• ❛❞❞r❡ss ✿ •✳s❛✉t♦t❅❛❣r♦s✉♣❞✐•♦♥✳❢r Pr❡♣r✐♥t s✉❜♠✐tt❡❞ t♦ ❊•s❡✈✐❡r ✷✷ •✉✐♥ ✷✵✶✹

This paper describes the design of a 3D image acquisition system dedicated to natural complex scenes composed of randomly distributed objects with spatial discontinuities. In agronomic sciences, the 3D acquisition of natural scene is difficult due to the complex nature of the scenes. Our system is based on the Shape from Focus technique initially used in the microscopic domain. We propose to adapt this technique to the macroscopic domain and we detail the system as well as the image processing used to perform such technique. The Shape from Focus technique is a monocular and passive 3D acquisition method that resolves the occlusion problem affecting the multi-cameras systems. Indeed, this problem occurs frequently in natural complex scenes like agronomic scenes. The depth information is obtained by acting on optical parameters and mainly the depth of field. A focus measure is applied on a 2D image stack previously acquired by the system. When this focus measure is performed, we can create the depth map of the scene.