Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset

Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset

Speech to text conversion and summarization for effective understanding and documentation

Speech, is the most powerful way of communication with which human beings express their thoughts and feelings through different languages. The features of speech differs with each language. However, even while communicating in the same language, the pace and the dialect varies with each person. This creates difficulty in understanding the conveyed message for some people. Sometimes lengthy speeches are also quite difficult to follow due to reasons such as different pronunciation, pace and so on.   Speech recognition which is an inter disciplinary field of computational linguistics aids in developing technologies that empowers the recognition and translation of speech into text. Text summarization extracts the utmost important information from a source which is a text and provides the adequate summary of the same. The research work presented in this paper describes an easy and effective method for speech recognition. The speech is converted to the corresponding text and produces summarized text. This has various applications like lecture notes creation, summarizing catalogues for lengthy documents and so on. Extensive experimentation is performed to validate the efficiency of the proposed metho

Dynamic Offloading Technique for Latency-Sensitive Internet of Things Applications using Fog Computing

Internet of Things (IoT) has evolved as a novel paradigm that provides com-putation power to different entities connected to it. IoT offers services to multiple sectors such as home automation, industrial automation, traffic management, healthcare sector, agriculture industry etc. IoT generally relies on cloud data centers for extended analytics, processing and storage support. The cloud offers highly scalable and robust platform for IoT applications. But latency sensitive IoT applications suffer delay issues as the cloud lies in remote location. Edge/fog computing was introduced to overcome the issues faced by delay-sensitive IoT applications. These platforms lie close to the IoT network, reducing the delay and response time. The fog nodes are usually distributed in nature. The data has to be properly offloaded to available fog nodes using efficient strategies to gain benefit from the integration. Differ-ent offloading schemes are available in the literature to overcome this prob-lem This paper proposes a novel offloading approach by combining two effi-cient metaheuristic algorithms, Honey Badger Algorithm (HBA) and Fla-mingo Search Algorithm (FSA) termed as HB-FS algorithm. The HB-FS is executed in an iterative manner optimizing the objective function in each it-eration. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the proposed work outperforms the existing algorithms in terms of latency, response time and execution time. The methodology also offers better degree of imbalance with proper load balancing under different conditions

COMPARATIVE EVALUATION OF INVITRO ANTIINFLAMMATORY ACTIVITY OF PSIDIUM GUAJAVA AND SYZYGIUM CUMINI LEAVES

Psidium guajava L. known as Guava is a medicinal plant belonging to the family Myrtaceae. Syzygium cumini Linn. known as Jamun is a tropical tree belonging to the family Myrtaceae. Recent evidence has demonstrated that combination therapy could provide greater therapeutic benefits to diseases such as AIDS, cancer, atherosclerosis and diabetes, all of which possess complex etiology and pathophysiology that make the treatment difficult with single drug target approach. The present study was to compare the invitro anti-inflammatory activity of two plants from Myrtaceae family as well as to investigate the anti-inflammatory activity of the combined extracts (1:1mixture) of those plants by estimating the inhibition of cyclooxygenase, 5-lipoxygenase, cellular nitrite levels, inducible nitric oxide synthase and myeloperoxidase using RAW 264.7 cells. Total ethanolic extracts of shade dried leaves were prepared and subjected to invitro anti-inflammatory study. The percentage inhibition of COX and 5 LOX by the combined extract, at 100 µg/ml was 55.67 and 48.02 respectively. The reduction in the cellular nitrite level (393.195 µg) and MPO level (0.00205U/ml) was comparable to that of standard. The results of the study showed that at 100 µg/ml, the combined extract (1:1 mixture) of the plants exhibited prominent anti-inflammatory activity than either of the individual plants in all the methods studied. On comparison of the anti-inflammatory activity, Syzygium cumini is found to be more active than Psidium guajava. Hence the combination of the two plants can be used to formulate drugs for various inflammatory disorders in traditional and modern medicine

EFFECT OF COMBINATION OF TWO PLANT EXTRACTS ON DIABETES MELLITUS

Objective: To investigate the anti-diabetic activity of combined ethanolic extracts (1:1mixture) of dry leaves of Syzygium cumini and Psidium guajava belonging to the family Myrtaceae as well as to compare the anti-diabetic activity of these plants by in vitro methods.Methods: In vitro glucose uptake assay was performed on cultured L6 cell lines (rat myoblast cell line) and estimated the glucose uptake using high sensitivity glucose oxidase kit. In vitro alpha amylase inhibitory assay was performed on porcine alpha amylase and the absorbance was measured at 540 nm using a microplate reader. Acarbose was used as the standard in both the methods.Results: At a concentration of 100µg/ml the percentage glucose uptake by the combined ethanolic extract (1:1 mixture) of Syzygium cumini and Psidium guajava leaves was 43.95 while for acarbose the corresponding value was 51.71. At 100 μg/ml the percentage of glucose uptake by Syzygium cumini and Psidium guajava was 27.62 and 22.17 respectively. The percentage inhibition of alpha amylase by the combined ethanolic extract (1:1 mixture) of Syzygium cumini and Psidium guajava leaves at a concentration of 1000 µg/ml was 36.51 and it was 29.26 for Syzygium cumini and 23.43 for Psidium guajava. For acarbose the percentage inhibition of alpha amylase was 73.82 at the concentration of 1000 µg/ml.Conclusion: The combined extract of the leaves of the plants selected was found to be more effective than individual plant extracts against diabetes. The percentage glucose uptake of the combined extract was found to be closer to that of the standard drug acarbose. On comparison of two plants Syzygium cumini was found to be more active against diabetes than Psidium guajava. As the 1:1 mixture of the ethanolic extract is found to be more active, the combination of the two plants can be used to formulate drugs for treating diabetes

PHARMACOLOGICAL PROFILE OF VATSANABHA: VISHA DRUG: AMRITA GUNA

Vatsanabha (Aconitum ferox) belongs to a group of potential drugs called Mahavishas, belonging to the genus Aconitum, Family Ranunculaceae. It is widely distributed in North Eastern Himalayan region. Earlier Aconite was more understood as a poison than a medicine. The roots of Aconitum ferox known under the common name Vatsanabha is extremely poisonous but after their detoxification process the drug is useful in the treatment of diseases such as rheumatoid arthritis, fever, sciatic neuritis, hypertension and also act as immunomodulators. Aconitum alkaloid, when ingested mainly concentrates in the liver, kidney and serum and is eliminated through urine and faeces. As stated by Acharya Sushruta, Vatsanabha precipitates harmful effects such as yellow discoloration of stool, urine and eyes and neck stiffness. The diterpene alkaloids such as hypaconitine, aconitine, and mesaconitine are poisonous components present in the root tubers, which is converted into less toxic alkaloids such as aconine, benzoylaconine, and pyroaconine by alkaline treatment, heating or through deacetylation and oxidation reaction. The textbook ‘Rasaratna Samucchaya’ explains 8 stages of toxic effect of Aconitum ferox. Shodhana process can be defined as the removal of unwanted part of drug and eradication of highly toxic ingredients. In addition to its detoxification properties, the efficacy and potency of the drug can be increased by Shodhana process. Thin layer chromatography studies have shown that the poisonous substances like psudoaconitine and aconitine is converted into less poisonous substance like veratroyl pseudoaconine and benzoyl aconine respectively only in traditional Ayurvedic shodhana

Drug-Induced Bullous Sweet Syndrome with Multiple Autoimmune Features

Sweet syndrome (SS) (Acute Febrile Neutrophilic Dermatosis) has been reported in association with autoimmune phenomena including relapsing polychondritis, drug-induced lupus, and the development of antineutrophil cytoplasmic antibodies (ANCAs). However, a combination of these autoimmune features has not been reported. Herein, we report a case of drug-induced bullous SS with ocular and mucosal involvement, glomerulonephritis, and multiple autoimmune features including clinical polychondritis with antitype II collagen antibodies, ANCAs, antinuclear (HEp-2), and antihistone antibodies in a patient on hydralazine and carbamazepine

Challenges in the Life Cycle Assessment of fibre reinforced polymers using the example of a composite aircraft interior shell

Lightweight structures made of fibre reinforced polymers (FRP) already play an important role in the reduction of fuel consumption in the aviation sector. Life Cycle Assessment (LCA) is a tool to support decision making by giving valuable information on different categories of potential environmental impacts for the whole life cycle of a product. While nowadays the aircraft use phase is by far predominant in the majority of the environmental impact categories considered in LCA, both production and end-of-life must not be disregarded in order to ensure the smallest possible ecological footprint. In this presentation, a preliminary LCA from a business class seat shell demonstrator in the SuCoHS* EU project will show potential reductions of environmental impacts by choice of design and materials compared to the state-of-the-art structure. Based on this example, todays challenges of LCA in the fields of FRP and aviation in general are addressed. *) SuCoHS (Sustainable and cost efficient high-performance composite structures demanding temperature and fire resistance). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769178

Expert curation of the human and mouse olfactory receptor gene repertoires identifies conserved coding regions split across two exons.

BACKGROUND: Olfactory receptor (OR) genes are the largest multi-gene family in the mammalian genome, with 874 in human and 1483 loci in mouse (including pseudogenes). The expansion of the OR gene repertoire has occurred through numerous duplication events followed by diversification, resulting in a large number of highly similar paralogous genes. These characteristics have made the annotation of the complete OR gene repertoire a complex task. Most OR genes have been predicted in silico and are typically annotated as intronless coding sequences. RESULTS: Here we have developed an expert curation pipeline to analyse and annotate every OR gene in the human and mouse reference genomes. By combining evidence from structural features, evolutionary conservation and experimental data, we have unified the annotation of these gene families, and have systematically determined the protein-coding potential of each locus. We have defined the non-coding regions of many OR genes, enabling us to generate full-length transcript models. We found that 13 human and 41 mouse OR loci have coding sequences that are split across two exons. These split OR genes are conserved across mammals, and are expressed at the same level as protein-coding OR genes with an intronless coding region. Our findings challenge the long-standing and widespread notion that the coding region of a vertebrate OR gene is contained within a single exon. CONCLUSIONS: This work provides the most comprehensive curation effort of the human and mouse OR gene repertoires to date. The complete annotation has been integrated into the GENCODE reference gene set, for immediate availability to the research community