Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub

Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub

Agonistic Association of Lepidoptera and Fungus in the Development of Leaf-spot Disease in High Altitude Mango and its Control

Plants are common prey for pests, though plants at high altitudes are less prone to diseases. However, our sample proved to be an exception, as disease in plants have become a major problem in North India, especially in old, crowded orchards where there is excessive shade .Mango, our test plant, is well adapted to tropical and subtropical climate. Here we considered the mechanism of disease initiation in the mango leaves by the entry of a fungal pathogen- Cercospora mangiferae, and its possible agonistic association with an insect of the Lepidoptera group, Procontarinia sp . Our aim is to suggest a pesticide to avert the entry and reduce the occurrence of the disease. The specimen, collected from a place called Jorolle (NH 88) near Sundernagar, is 10 kms away from the Beas-Sutlej confluence in the state of Himachal Pradesh, during the months of January-February, the temperature recorded was between 7-14°C. The environment in the vicinity of the mango orchard was dry, windy, and grimy and plagued by vehicular emissions. There were predominantly 2 kinds of leaf spots-a white and a brown spot. The spread of the disease started from the lower mature leaves to the upper younger leaves. Enormity of the infection was much greater in leaves having galls along their margins. The gall formation results due to the mechanical damage caused by the infection due to a midge fly (Procontarinia sp). The average diameter of galls ranged between 3-4mm. As affirmed earlier, the leaves with large number of galls are the primary  home for the fungus- Cercospora mangiferae where they reside in larger numbers. Although the mechanism of an agonistic association is obscure but the possibility of such an association cannot be ruled out completely; where the primary infection caused by the midge insect paves the way for secondary infection by the fungus. Our sole intention was to prevent occurrence of such an association, by inhibiting both the infections from occurring individually. Our test pesticide belonged to the Malathione group. Its main component is monocrotophos which interferes not only with the nerve impulse transmission of the insect but also damages the cell wall of the fungal pathogen thereby attending both the problems. The experiment was performed with different concentrations of pesticide and it was observed that at 43.5%w/w it was effective enough to prevent 100% germination. Our studies provide a conclusive result which suggests that if the pesticide, at the effective concentration is sprayed till run-off, the young tender leaves of Mangifera indica will be protected from both the midge insect as well as the fungal pathogen.Key words: Chausa, Langra, Dashehari, Leaf spot, Cercospora mangiferae, White spot, Brown Spot, Gall, Procontarinia sp., pesticide, Malathione, Hilcron, Monocrotophos Arup Kumar Mitra et al. Agonistic Association of Lepidoptera and Fungus in the Development of Leaf-spot Disease in High Altitude Mango and its Control.  J Phytol 2/7 (2010) 28-36

Graphene and amyloid peptide binding and its implications in Alzheimer's disease

A poster discussing the relation of graphene and amyloid peptide binding to Alzheimer's disease

Mechanisms of Phosphine Toxicity

Fumigation with phosphine gas is by far the most widely used treatment for the protection of stored grain against insect pests. The development of high-level resistance in insects now threatens its continued use. As there is no suitable chemical to replace phosphine, it is essential to understand the mechanisms of phosphine toxicity to increase the effectiveness of resistance management. Because phosphine is such a simple molecule (PH3), the chemistry of phosphorus is central to its toxicity. The elements above and below phosphorus in the periodic table are nitrogen (N) and arsenic (As), which also produce toxic hydrides, namely, NH3 and AsH3. The three hydrides cause related symptoms and similar changes to cellular and organismal physiology, including disruption of the sympathetic nervous system, suppressed energy metabolism and toxic changes to the redox state of the cell. We propose that these three effects are interdependent contributors to phosphine toxicity

Graphene & Graphene Oxide and amyloid peptide binding and its implications in Alzheimer’s disease

Alzheimer’s disease is a neurodegenerative disease caused by the incorrect cleaving of the transmembrane Amyloid Precursor Protein into the neurotoxic Aβ40 and Aβ42 fragments2. These fragments are soluble oligomers with a random coil conformation that can impair synapses or neurotransmission; they may also aggregate into parallel and antiparallel beta sheets to form amyloid plaques, which can block or distort signaling between neuronal pathways7. Aβ fibrils self-assemble into parallel and antiparallel beta sheets on hydrophobic graphite, but not on hydrophilic mica5,6. Aβ fibrils also assemble on graphene, which irreversibly captures fibrils3, suggesting grapheme might have a role in the study of Alzheimer’s amyloid plaque. These studies characterize binding between amyloid beta peptide fibrils and graphene using Raman spectroscopy, scanning electron microscopy (SEM), and circular dichroism (CD). The goal is to provide evidence that graphene can attract free floating Aβ fibrils and Aβ plaque. Both studies currently use diphenylalanine peptide, a self-assembling model peptide for Aβ fibrils

Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior

Shannon, in his seminal paper introducing information theory, divided the communication into three levels: technical, semantic, and effectivenss. While the technical level is concerned with accurate reconstruction of transmitted symbols, the semantic and effectiveness levels deal with the inferred meaning and its effect on the receiver. Thanks to telecommunications, the first level problem has produced great advances like the internet. Large Language Models (LLMs) make some progress towards the second goal, but the third level still remains largely untouched. The third problem deals with predicting and optimizing communication for desired receiver behavior. LLMs, while showing wide generalization capabilities across a wide range of tasks, are unable to solve for this. One reason for the underperformance could be a lack of "behavior tokens" in LLMs' training corpora. Behavior tokens define receiver behavior over a communication, such as shares, likes, clicks, purchases, retweets, etc. While preprocessing data for LLM training, behavior tokens are often removed from the corpora as noise. Therefore, in this paper, we make some initial progress towards reintroducing behavior tokens in LLM training. The trained models, other than showing similar performance to LLMs on content understanding tasks, show generalization capabilities on behavior simulation, content simulation, behavior understanding, and behavior domain adaptation. Using a wide range of tasks on two corpora, we show results on all these capabilities. We call these models Large Content and Behavior Models (LCBMs). Further, to spur more research on LCBMs, we release our new Content Behavior Corpus (CBC), a repository containing communicator, message, and corresponding receiver behavior

Microalgae: An Exquisite Oil Producer

With the influx in population and shortage of conventional energy-sources, an exponential-rise of the microalgal oil-production has been observed in the past two decades. The algal bio-oil is used in various industries viz. food, pharmaceutical, cosmetic and biodiesel plants. The present study is focused towards the production of oil from oleaginous microalgae in photo-bioreactors and open water systems. Moreover, microalgae can thrive in non-cultivable waters like seawater, salt water and even wastewater which make the algal technology more attractive in terms of soil and water preservation. Using sunlight and nutrients like salts of magnesium, potassium, sodium etc. the autotrophic microalgae can grow in large quantities in indoor photo-bioreactors and in open ponds. Microalgae are able to produce approximately 10,000 gallons of oil per acre as compared to the higher plants that produces only 50 gallons per acre (soy), 110 to 145 gallons per acre (rapeseed), 175 gallons per acre (Jatropha), 650 gallons per acre (palm). The biomass productivity is 10 times higher than that of the phytoplanktons and 20–30% higher than that of the terrestrial biomass. In terms of the fatty acid composition, the microalgal oil can well match with the plant-derived oil, mainly C16 and C18 fatty acids. Some microalgae are also rich in valuable polyunsaturated-fatty-acids, which have multiple health benefits