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"Dinesh, M."
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Empowering artificial intelligence through machine learning : new advances and applications
\"This new volume, Empowering Artificial Intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next big-game changer in research and technology. The volume looks at how computing has enabled machines to learn, making machines and tools become smarter in many sectors, including science and engineering, healthcare, finance, education, gaming, security, and even agriculture, plus many more areas. Topics include techniques and methods in artificial intelligence for making machines intelligent, machine learning in healthcare, using machine learning for credit card fraud detection, using artificial intelligence in education using gaming and automatization with courses and outcomes mapping, and much more. The book will be valuable for professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology\"-- Provided by publisher.
Book
Diagnostic ability of deep learning in detection of pancreatic tumour
Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.
Journal Article
An amide to thioamide substitution improves the permeability and bioavailability of macrocyclic peptides
Solvent shielding of the amide hydrogen bond donor (NH groups) through chemical modification or conformational control has been successfully utilized to impart membrane permeability to macrocyclic peptides. We demonstrate that passive membrane permeability can also be conferred by masking the amide hydrogen bond acceptor (>C = O) through a thioamide substitution (>C = S). The membrane permeability is a consequence of the lower desolvation penalty of the macrocycle resulting from a concerted effect of conformational restriction, local desolvation of the thioamide bond, and solvent shielding of the amide NH groups. The enhanced permeability and metabolic stability on thioamidation improve the bioavailability of a macrocyclic peptide composed of hydrophobic amino acids when administered through the oral route in rats. Thioamidation of a bioactive macrocyclic peptide composed of polar amino acids results in analogs with longer duration of action in rats when delivered subcutaneously. These results highlight the potential of O to S substitution as a stable backbone modification in improving the pharmacological properties of peptide macrocycles.
Solvent shielding of the amide hydrogen bond donor through chemical modification or conformational control has been successfully utilized to impart membrane permeability to macrocyclic peptides. Here, the authors show that passive membrane permeability can also be conferred by masking the amide hydrogen bond acceptor through thioamide substitution, leading to improved pharmacological properties of peptide macrocycles.
Journal Article
An investigation and comparison of machine learning approaches for intrusion detection in IoMT network
Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient’s information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker’s attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.
Journal Article
Intranasal Nanoemulsions for Direct Nose-to-Brain Delivery of Actives for CNS Disorders
The treatment of various central nervous system (CNS) diseases has been challenging, despite the rapid development of several novel treatment approaches. The blood–brain barrier (BBB) is one of the major issues in the treatment of CNS diseases, having major role in the protection of the brain but simultaneously constituting the main limiting hurdle for drugs targeting the brain. Nasal drug delivery has gained significant interest for brain targeting over the past decades, wherein the drug is directly delivered to the brain by the trigeminal and olfactory pathway. Various novel and promising formulation approaches have been explored for drug targeting to the brain by nasal administration. Nanoemulsions have the potential to avoid problems, including low solubility, poor bioavailability, slow onset of action, and enzymatic degradation. The present review highlights research scenarios of nanoemulsions for nose-to-brain delivery for the management of CNS ailments classified on the basis of brain disorders and further identifies the areas that remain unexplored. The significance of the total dose delivered to the target region, biodistribution studies, and long-term toxicity studies have been identified as the key areas of future research.
Journal Article
Hybrid model for classifying Indo Aryan and Tamil texts from historic manuscripts
The ancient manuscripts, especially Indo-Aryan and Tamil texts have the complex linguistic structure in their manuscripts with historical differences. The present paper is a BERT-Li Scribe Hybrid Model of the historic Indo-Aryan and Tamil manuscript classification. The model combines the contextual embedding of BERT, which learns the semantic links in the text, and LiScribe, a specialized sequence model that learns features, and linguistic patterns of Indo-Aryan and Tamil scripts at a character level. The sample of 1,055 manuscripts of the Library of Congress and University of Hamburg is a perfect combination of Indo-Aryan and Tamil texts. The model that is offered provides the classification of two large groups, such as Indo-Aryan (Hindi, Bengali, Marathi, Gujarati) and Tamil, which guarantees the level of classification on a family level and specific scripts. Training is performed using categorical cross-entropy loss and Adam optimizer with learning rate scheduling with dropout layers used to avoid overfitting with noisy historical data. The model, which was coded in Python and deployed with the help of such libraries as TensorFlow and PyTorch, demonstrated a high overall classification accuracy of 97.61%, being able to distinguish between the Indo-Aryan and Tamil texts at the same time. The attention mechanism also increases the concentration on the important features by the model even in the degraded manuscripts. This mixed methodology proves the usefulness of the combination of deep learning and linguistic feature extraction to the correct classification of historical manuscripts.
Journal Article
Improvement of ternary fuel combustion with various injection pressure strategies in a toroidal re-entrant combustion chamber
The present experimental work focuses on the influence injection pressure and toroidal re-entrant combustion chamber in a single cylinder diesel engine fuelled with ternary fuel (diesel-biodiesel-ethanol) blend. Ternary fuel (TF) is prepared by blending 70% diesel, 20% biodiesel, and 10% ethanol blends and its fuel properties were investigated and compared with diesel fuel. Since the physic-chemical properties of TF are well behind the diesel fuel, it is proposed to be blended with 20 ppm alumina nano additives which act as an ignition enhancer and catalytic oxidizer. The resulting fuel mixture (TF + 20 ppm alumina additive) is named as high performance fuel (HPF). Experimentations were conducted on HPF subjected to various injection pressures of 18 MPa, 20 MPa, 22 MPa, and 24 MPa respectively and are operated in toroidal re-entrant chamber geometry (TG) at an injection timing of 22
o
bTDC. From experimentation, it was identified that, for TG-HPF, higher injection pressure of 22 MPa ensued highest BTE (Brake Thermal Efficiency) of 35.5% and lowest BSEC (Brake Specific Fuel Consumption) of 10.13 MJ/kWh owing to the pooled effect of higher swirl formation, improved atomization enhanced evaporation rate, and better air-fuel mixing. Emission wise TG-HPF operated at 22 MPa lowered the HC (hydrocarbon), CO (carbon monoxide), and smoke emissions by 18.88%, 7.19%, and 5.02%, but with marginally improved NOx (oxides of nitrogen) and CO
2
(carbon dioxide) emissions by 3.92% and 3.89% respectively. In combustion point of view, it is observed that injection pressure increased the cylinder pressure, heat release rate (HRR), and cumulative heat release rate (CHRR) by 5.35%, 5.08%, and 3.38% respectively indicating improved combustion rate as a result of enhanced atomization, evaporation, and high turbulence inducement. Overall, it is concluded that operating the ternary fuel at 22 MPa injection pressure at toroidal re-entrant combustion chamber results in improved performance and minimized emissions.
Journal Article
A comprehensive experimental study of eco-friendly hybrid polymer composites using pistachio shell powder and Aquilaria agallocha Roxb
This study investigates the effects of incorporating pistachio shell powder and a mixture of Aquilaria agallocha Roxb (AAR) resin with epoxy on the mechanical, dynamic mechanical, thermal, and biodegradability properties of an epoxy composite. Filler loadings ranged from 10 to 35% by volume, in 5% increments. Scanning electron microscopy (SEM) revealed a uniform distribution of the hybrid polymer materials, particularly at 30% natural resin content, enhancing the load-bearing capacity of the composites. The addition of pistachio shell powder and AAR resin significantly improved the flexural modulus and strength of the composites. At a filler volume of 35%, the hybrid polymer exhibited a maximum impact resistance of 2,718 J/m
2
, demonstrating increased energy absorption. Moreover, the hybrid system enhanced the damping factor by up to 30%, suggesting superior dynamic mechanical performance. Thermogravimetric analysis (TGA) indicated that the hybrid composites displayed better thermal stability compared to pure epoxy resin. These findings suggest that the combination of pistachio shell powder and AAR natural resin offers a sustainable approach to reinforcing epoxy-based composites, providing improved mechanical and thermal performance for potential industrial applications.
Journal Article
Prospects for 3D bioprinting of organoids
Three-dimensional (3D) organoids derived from pluripotent or adult tissue stem cells seem to possess excellent potential for studying development and disease mechanisms alongside having a myriad of applications in regenerative therapies. However, lack of precise architectures and large-scale tissue sizes are some of the key limitations of current organoid technologies. 3D bioprinting of organoids has recently emerged to address some of these impediments. In this review, we discuss 3D bioprinting with respect to the use of bioinks and bioprinting methods and highlight recent studies that have shown success in bioprinting of stem cells and organoids. We also summarize the use of several vascularization strategies for the bioprinted organoids, that are critical for a complex tissue organization. To fully realize the translational applications of organoids in disease modeling and regenerative medicine, these areas in 3D bioprinting need to be appropriately harnessed and channelized.
Journal Article
Primary Hepatocyte Isolation and Cultures: Technical Aspects, Challenges and Advancements
Hepatocytes are differentiated cells that account for 80% of the hepatic volume and perform all major functions of the liver. In vivo, after an acute insult, adult hepatocytes retain their ability to proliferate and participate in liver regeneration. However, in vitro, prolonged culture and proliferation of viable and functional primary hepatocytes have remained the major and the most challenging goal of hepatocyte-based cell therapies and liver tissue engineering. The first functional cultures of rat primary hepatocytes between two layers of collagen gel, also termed as the “sandwich cultures”, were reported in 1989. Since this study, several technical developments including choice of hydrogels, type of microenvironment, growth factors and culture conditions, mono or co-cultures of hepatocytes along with other supporting cell types have evolved for both rat and human primary hepatocytes in recent years. All these improvements have led to a substantial improvement in the number, life-span and hepatic functions of these cells in vitro for several downstream applications. In the current review, we highlight the details, limitations and prospects of different technical strategies being used in primary hepatocyte cultures. We discuss the use of newer biomaterials as scaffolds for efficient culture of primary hepatocytes. We also describe the derivation of mature hepatocytes from other cellular sources such as induced pluripotent stem cells, bone marrow stem cells and 3D liver organoids. Finally, we also explain the use of perfusion-based bioreactor systems and bioengineering strategies to support the long-term function of hepatocytes in 3D conditions.
Journal Article