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Recommendation-based systems are utilized for various purposes such as suggesting web pages, books, restaurants, TV shows, movies, and so on. One of the primary objectives of movie recommendation systems is to provide movie suggestions to users based on their interests, saving them time in scouring the internet for suitable movies from a vast pool of options. Content-based recommendation systems analyze item descriptions and predict movies that a user may prefer, based on the features present in previously selected movies. These systems may utilize one or more features to recommend movies, such as the movie genre, director, or actors. In this paper, we propose a recommendation system that incorporates the features of cast, keywords, crew, and genres. We integrate a column outlining all four features that make it a powerful piece of a movie recommendation system top of Form.

Investigations of the In(III)porphyrin complexes with substituted salicylates(X) as axial ligands have been carried out using electronic and biological studies. Electronic spectra of the complexes are accompanied by shifting of wavelength towards hypsochromic/blue-shift or bathochromic/red-shift along with 'ƒ' values decided by the nature of the functional groups attached to the salicylate ligand. IR frequencies appear at 550cm-1–400cm−1 for In–N(Por) and at 650cm−1–700cm−1 for In–OSA. 1HNMR spectra reveal the merging of the salicylate ring with that of the protons of the macrocyclic ring. The 13CNMR studies confirm the resonance of the meso-carbon tetraphenyl porphyrin ring between 130ppm to 160ppm and the salicylate ring carbons in the region of 110ppm to 165ppm. Thermal analysis confirms the presence of indium nitride (In-N) in an argon atmosphere from 0oC to 900oC. Cyclic voltammetry(CV) revealed the reduced or oxidized properties of these complexes lead to the generation of π-anion or cation radicals by two one-electron transfer reactions. In(III)porphyrin complexes with substituted salicylates were also screened for in-vitro antifungal activity(%inhibition) against the microbe "Acremonium fusidoides spp." using the PDA method, which shows that the percentage inhibition is inverse to the diameter of the colony. The freeradical scavenging or antiradical activity (%RSA) by the DPPH method reveals that these complexes were absorbance and concentration-dependent.

For the concurrent energy researches around the world, the question of energy availability and the effects of global warming have taken on particular importance. This situation creates room for prospective, more modern and sustainable alternative energy sources. In the industrial, automotive and even as a fuel source for power plants, biodiesel can be used as a sustainable alternative energy. This study offers a detailed overview of the use and advancement of bio-diesel as a fuel for the production of renewable energy from material sources. The creation of various bio-diesel mixture combinations and their use in diverse sectors is a key topic covered in this study. In order to use different types of biodiesel in different parts of the world, its physical and chemical properties have been researched. Although the use of biodiesel reduces emissions and pollutants, a specific approach is required to maximise efficiency and effectiveness, both technically and non-technically. However, the use of biodiesel together with the optimization of its properties and parameters demonstrates that this alternative energy is practical and might be used as a sustainable fuel source for the auto industry and electricity generation in the future.

The present work involves the formulation of colon targeted matrix tablet of Ibuprofen by using direct compression method. Literatures regarding, Ibuprofen tablet dosage form preparation, excipients selection, manufacturing method etc., has been collected and reviewed. In this work, selection of excipients was done based on a literature review. Excipients include Eudragit S100, Ethyl cellulose, Lactose, Talc, Magnesium stearate. Quantities of the excipients were selected performing FT-IR method which is an HIS of Fourrts India Laboratory. Preformulation studies have also been performed to study the nature of API and compatibility of API with excipients by physical observation and FT-IR studies. The result showed that API was compatible with all the excipients selected. The tablets were formulated by direct compression method using the selected excipient quantities. The formulated tablets were tested for both pre-compression parameters and post compression parameters as per requirements of standards. Pre-compression parameters such asbulk density, tapped density, compressibility index, Hausner’s ratio and compressibility index. The results obtained indicate that it has good flow property for direct compression. The formulated Ibuprofen matrix tablets were coated with enteric polymer Eudragit FS30D by pan coating method. The prepared tablets were evaluated for weight variation, hardness, thickness, friability, drug content, and disintegration time and in-vitro dissolution studies. All these parameters were found to be within the standard limits. Comparative studies of coated Ibuprofen tablets and uncoated Ibuprofen tablets are evaluated for the hardness, thickness, in-vitro dissolution studies and disintegration time. Out of six formulations, the formulation F6 showed 98.51% drug release at 24 hrs. Since it provide greater protection to the core under acidic condition while at the same time show the fastest drug release under intestinal pH. So the formulation F6 was considered as the confirmatory trial and it was subjected for stability studies up to three months of accelerated stability 400C ± 2C0, 75 %± 5 % RH and found to be within limits.

Introduction: Trypanosomiasis often caused by trypanosome is common among wildlife and domestic animals. Aim and Objectives: This study assessed the antitrypanosomal and anti-anemic activities of bitter leaf ethanol extract (BLEE) against Trypanosoma congolense. Methodology: Forty (40) healthy wistar rats of both sexes weighing 120-150 g were used at the beginning of the experiment. They were randomly divided into six groups (A, B, C, D, E and F). In A, B, D, E and F (n = 5) but group C was started with fifteen (15) rats till death reduced the population to five (5). Group A rats were uninfected and untreated; group B rats were uninfected but treated (500 mg/kgbwt BLEE); group C rats were infected but not treated; groups D, E and F rats were infected and treated (100, 300 and 500 mg/kgbwt BLEE, respectively). Infection of the rats was carried out by injecting 0.1 ml of blood containing approximately 1 × 103 trypanosomes intraperitoneally into each rat in the infected groups. The effects of BLEE on the infected rats were determined. Changes in haemoglobin level and packed cell volume (PCV) were determined prior, during and post infection. Results: The results obtained revealed that the percentage values for haemoglobin level and PCV of infected rats in this study were remarkably reduced compared with the uninfected rats. A marked association between the prevalence of Trypanosoma congolense, haemoglobin level and PCV was observed in this study. The extract immobilized the trypanosome in the treated groups as compared to the infected but untreated group. The BLEE was able to cause a remarkable reduction of parasitaemia level, improved haemoglobin level and PCV as compared to the infected-untreated rats. Conclusion: Our findings thus has given an indication of in vitro and in vivo antitrypanosomal and anti-anemic activities of Vernonia amygdalina against Trypanosoma congolense.

The negative impact of type 2 diabetes mellitus (T2DM) on individuals, families, health system and the economic development as a whole, is an important justification for research in this field. The present study is a hospital-based case-control type to ascertain the genetic susceptibility of T2DM among a sample population of various ethnic groups resident in Bayelsa State, Nigeria. Also evaluated was the relationship between transcription factor 7-like 2 (TCFL2) rs12255372 and rs290487 genetic polymorphisms with development of T2DM. Genotyping of TCFL2 rs12255372 and rs290487 were carried out using the polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) method. Among the indigenous groups, the allelic frequencies determined for the abnormal (CC) and the normal (TT) genotypes were 94.4 and 5.6 (%), respectively; whereas, the values 91.7 and 8.3 (%), in that other, were determined for the non-indigenous groups. A convincing significant difference was found between subjects with T2DMne and their counterpart controls within indigenous people of Bayelsa State with respect to confounders, including age (t=8.046, p

At present, driver fatigue has emerged as a predominant root cause of vehicular accidents on the roads. The phenomenon of drowsiness occurring while driving, particularly during twilight hours, can pose a significant risk to road safety, potentially leading to unforeseen collisions resulting in grave injuries or fatalities. The insufficiency of sleep negatively impacts an individual's capacity to carry out tasks efficiently, thereby resulting in prolonged reaction durations, weakened memory, and impaired decision- making abilities. This augmentation accentuates the likelihood of inflicting harm upon fellow riders and pedestrians on the thoroughfare. In response to the aforementioned difficulty, our proposed resolution leverages a Deep Learning methodology employing a convolutional neural network (CNN) framework, with a focus on treating the detection of drowsiness as an object recognition undertaking aimed at discerning and pinpointing the occurrence of opened and closed eyelids. The object detection process in our proposed methodology utilizes the Inception V3 convolutional neural network architecture

The most important challenge to sustainable mobility is persistent congestions of differing strength and duration in the dense transport networks. The standard Adaptive Traffic Signal Control cannot properly address this kind of congestion. Deep learning-based mechanisms have proved their significance to anticipate in adjective outcomes to improve the decision making on the predictions of traffic length. The deep learning models have long been used in many application domains which needed the identification and prioritization of adverse factors for a simplifying human life. Several methods are being popularly used to handle real time problems occurring from traffic congestion. This study demonstrates the capability of DL models to overcome the traffic congestion by simply allowing the vehicles through a signal depending on the length of vehicles. Our proposed method integrates a numeral of approach, intended to advance the cooperativeness of the explore operation. In this work, we implement the application to detect the number of vehicles in the images from the user and gives vehicles counts. To detect the vehicles count here we are using the YOLO pretrained weights.

This study was aimed to evaluate the Aqua program in calibration and validity and its use in simulation to study the productive and water characteristics of maize crop.. The experiment was conducted in two irrigation methods, sprinkler irrigation and surface irrigation, and two cultivation methods are borders (lines) and furrows (lines and then furrows) and for two cultivars of maize, a hybrid (unlocal) and a local variety. The results that sprinkler irrigation reduced water consumption compared to surface irrigation, as it reached (558.38) mm for sprinkler irrigation, and (668.79) mm for surface irrigation. While the method of furrows after borders outperformed the borders only in improving growth characteristics and increasing productivity, and the hybrid (unlocal) cultivar outperformed the local cultivar. The various experimental parameters were also calibrated using Aquacrop program, and it was found that the program gave a good convergence between the simulated and field values, as it gave R2 values that ranged between (0.71-0.92) and RMSE between (0.12-23.76) for all studied traits.

In today's society, video surveillance for observing a particular area which includes hospitals, establishments, public parks, and buildings has become a need, where in each person desires to keep their belongings safe and relaxed. The growth in the urban population has ended in an increase in crime. For citizens, video surveillance has had a tremendous influence. Closed-circuit television (CCTV) is the most extensively used machine, but it's miles extra expensive and makes use of more strength and a garage. To address this trouble, we developed a complicated wise video surveillance device for places where human presence is abnormal. It isn't important to constantly screen the area with cameras in such situations. This consumes both the electricity and the storage space required for the footage. Open CV, a device gaining knowledge of software library, is used to put in force this gadget. The proposed gadget operates in such a manner that it captures video then processes frame by frame and starts recording whilst it detects human presence. The surveillance device can be activated if the cameras hit upon any movement. The counseled system gathers information and stores it in a neighborhood database. The video that becomes captured and stored can be utilized to identify the intruder and aid in his seizure. It could be useful in places where human presence is irregular, which includes bank vaults and houses

The medicine recommendation system is intended to suggest alternative medicines based on the cosine similarity between a patient's symptoms and the effects of various medications. The system makes use of a database of medications and their indications, as well as a list of symptoms that a patient may exhibit. It vectorizes the data, applies filters, and makes suggestions. Medicines with a higher cosine similarity are considered more relevant and recommended to patients. In the scenario of a medical emergency, when physicians or prescribed medications are unavailable, this recommender serves as a valuable resource. The proposed medicine recommendation system has the potential to help healthcare professionals and patients make educated decisions about alternative medications. The system can reduce the risk of adverse drug reactions and improve patient outcomes by suggesting alternative medicines that are more effective and have fewer side effects. Overall, the proposed medicine recommendation system has the potential to significantly improve patient care by making effective recommendations for alternative medications. It can also reduce healthcare professionals’ workload by automating the process of identifying.

Penelitian ini bertujuan untuk mengembangkan Modul IPA berbantuan Augmented Reality pada Materi Getaran, Gelombang dan Bunyi yang valid, praktis dan terbaca. Jenis penelitian ini adalah Research dan Development (R&D) dan menggunakan model pengembangan ADDIE. Prosedur penelitian ini meliputi (1) tahap analisis (analysis), (2) tahap perancangan (design), (3) tahap pengembangan (development). Instrumen pengumpulan data yang digunakan adalah angket kebutuhan, angket validasi ahli materi dan media, angket kepraktisan guru, dan angket keterbacaan siswa. Subjek dalam penelitian ini meliputi subjek uji validasi yang terdiri atas 1 orang ahli materi dan 1 orang ahli media, uji kepraktisan terdiri atas 5 orang guru IPA dan uji keterbacaan melibatkan 16 orang siswa. Pelaksanaan uji kepraktisan dan uji keterbacaan dilaksanakan di SMP Negeri 6 Singaraja Tahun Ajaran 2020/2021. Hasil penelitian menunjukan bahwa: 1) berdasarkan hasil uji validasi oleh ahli materi memperoleh skor penilaian 4,7 dengan kualifikasi sangat valid, (2) berdasarkan hasil uji validasi oleh ahli media memperoleh skor penilaian 4,2 dengan kualifikasi sangat valid, (3) berdasarkan hasil uji kepraktisan oleh praktisi/guru memperoleh skor rata-rata 4,74 dengan kualifikasi sangat praktis, (4) berdasarkan hasil uji keterbacaan oleh siswa memperoleh skor rata-rata 4,5 dengan kualifikasi sangat terbaca. Berdasarkan hasil uji validasi, kepraktisan dan keterbacaan maka Modul IPA berbantuan Augmented Reality pada Materi Getaran, Gelombang dan Bunyi dinyatakan valid, praktis dan terbaca untuk digunakan sebagai bahan ajar dalam proses pembelajaran.

This study aims to determine the effect of foreign direct investment (FDI) on economic growth and employment. Method, for this purpose, secondary data was collected in the form of annual data from the Central Bureau of Statistics and the Investment and One-Stop Integrated Service (PTSP) Office of Maluku Province. The data were analyzed using simple linear regression. The analysis model used adopts the Cobb Douglas function, namely Q = f (A Kα Lβ) with the assumption that Q is economic growth, K is capital and L is labor and A is technological progress. In the case of this study, it is assumed that economic growth is a function of capital, namely foreign investment (FDI) so that by modifying the Cobb-Douglas production function. The results showed that foreign investment (FDI) has a positive and significant effect on economic growth and employment. The effect of foreign investment (FDI) has a positive and significant effect on economic growth in Maluku Province. Policies that make it difficult for investors need to be reduced. Investment also has an influence on employment. Realized investment can expand production capacity and that will require additional labor.

Water makes up about 70% of the earth’s floor and is one of the most vital sources essential to maintaining life. Speedy urbanization and industrialization have led to a deterioration of water best at an alarming fee, ensuing in harrowing illnesses. Water high- quality has been conventionally estimated through expensive and time-ingesting lab and statistical analyses, which render the cutting-edge belief of real-time tracking moot. The alarming results of bad water nice necessitate an alternative approach that is quicker and inexpensive. With this motivation, this study explores a sequence of supervised device mastering algorithms to estimate the water high-quality. The proposed methodology achieves affordable accuracy the use of a minimum number of parameters to validate the possibility of its use in real time water first-class detection systems. It demonstrates the overall maintenance and management of the water quality and quantity inside a plant using modern technologies. It based on the systematic working process to reduce the man power and cost maintenance for the industrialization development.

The main idea behind this project is to develop a unobtrusive system which can detect fatigue of any human and can issue a timely warning. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough. According to the expert’s studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drnk-driving. This system will monitor the driver eyes using a camera and by developing an algorithm we can detect symptoms of driver fatigue early enough to avoid the person from sleeping. So, this project will be helpful in detecting driver fatigue in advance and will give warning output in form of alarm and pop-ups. Moreover, the warning will be deactivated manually rather than automatically. For this purpose, a de-activation dialog will be generated which will contain some simple mathematical operation which when answered correctly will dismiss the warning. Moresssover, if driver feels drowsy there is possibility of incorrect response to the dialog. We can judge this by plotting a graph in time domain. If all the three input variables show a possibility off fatigue at one moment, then a Warning signal is given in form of sound. This will directly give an indication of drowsiness/fatigue which can be further used as record of driver performance.

There is a great saying that a good book is a good friend. Reading books can help human to improve one’s intelligence and is also a good exercise to the brain. So reading book habits have many advantages. The traditional method of reading books has its own advantages, but it consumes not only a lot of time and money but also a lot of space. They require more time to complete a book. Unlike the traditional methods, there are also audio podcasts available in the market which take only a few hours to complete a book but are also very distracting. The users of audio podcasts can be easily distracted by anything and hence they may lose track of the actual message in the book. Thus, our idea is to provide users with a new era of digital library where multiple options are available to them. They can choose to read a book, listen to its audio, or select the read and listen option where our system reads the book for them with highlighted text, so they pay full attention to the book and the audio playing. The users can read a book as a pdf or listen to the audio of the books or can even select an option such as read and listen where our system reads the book for the user with highlighted text so that the user pay full attention on the book and the audio playing. This requires users to pay full attention to the book, and keeps them concentrated. The chances of distraction in this type of reading are very few, and hence users can get the full benefit from the book. This method can also help users complete more books in less time. Our goal is to provide every book lover with a new learning experience and give them a satisfactory experience in reading a book.

This proposes the development of a price negotiating chatbot on e-commerce websites using Natural Language Processing (NLP) technology. The chatbot aims to assist customers in negotiating prices with the sellers, enhancing their shopping experience. The proposed chatbot will be built using the Python programming language and the TensorFlow library for Natural Language Processing. The chatbot's architecture consists of several components, including a user interface, an NLP module, a price prediction module, and a negotiation module. The NLP module will enable the chatbot to understand and interpret the customer's messages, while the price prediction module will predict the minimum price that the seller is willing to accept. The negotiation module will use reinforcement learning algorithms to negotiate with the seller and come up with the best possible price for the customer. The proposed chatbot will provide customers with a convenient way to negotiate prices with sellers, enhancing their shopping experience. The chatbot's architecture, which includes a user interface, an NLP module, a price prediction module, and a negotiation module, will enable the chatbot to understand and interpret customer messages and negotiate prices with sellers on behalf of the customer. The chatbot will be trained and assessed on a substantial dataset of negotiation conversations, and its performance will be compared to other contemporary negotiation models. The results will demonstrate that the proposed chatbot significantly improves the shopping experience of customers, attaining higher success rates, shorter negotiation times, and increased customer.

Goal three of the UN’s Sustainable Development Goal is focused on promoting good health and well-being, with a specific emphasis on addressing the challenges posed by non-communicable diseases. One of the objectives for this goal is to reduce premature mortality from non-communicable diseases by a third by the year 2030. Chronic kidney disease (CKD) is a major contributor to morbidity and mortality from non-communicable diseases, affecting between 10 and 15% of the global population. Early and accurate detection of the stages of CKD is considered vital in order to minimize the impact of the associated health complications, such as hypertension, anemia, mineral bone disorder, poor nutritional health, acid base abnormalities, and neurological complications. To this end, machine learning techniques have been used in various research studies to detect CKD at an early stage. However, previous research has not focused on specific stage prediction. In this study, both binary and multi-classification for stage prediction were carried out using Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) prediction models. Analysis of variance and recursive feature elimination were applied for feature selection, and tenfold cross-validation was used to evaluate the models. The results showed that RF based on recursive feature elimination with cross-validation had better performance than SVM and DT for stage prediction of CKD. This research has the potential to lead to earlier detection and intervention, ultimately reducing premature mortality from non-communicable diseases as outlined in the UN's Sustainable Development Goal of good health and well-being.

It is crucial to create systems that lessen our reliance on non-renewable resources in a world with declining energy reserves and escalating economic crises. Smart Home Energy Management Systems, which employ smart switches to actively monitor energy use by each load, available power, and other parameters to transition between renewable resources and the primary power grid, are becoming more and more popular. The results of several methodologies were combined and analysed after a thorough literature review. The approach described is a synthesis of all the most well-liked approaches. The suggested methodology aims to implement demand response management simultaneously on grid electricity and solar power. The issue was divided into two scheduling scenarios: one assuming only solar power is available, the other assuming just grid power. In order to use the resources available as effectively as possible, the two halves are then combined.

The aluminum bronze coating is a type of soft solid lubricant film with excellent performance. In this work, to improve the wear resistance and tribological behavior of the aluminum alloy, the aluminum bronze coatings were prepared on aluminum alloy substrates using the High- Velocity Oxygen Fuel (HVOF) system. Fretting wear tests were performed on the coatings at 200 °C with different frequencies to analyze the wear behavior. The worn surface was characterized by scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS). The results showed that as the frequency increased from 5 Hz to 15 Hz, the coefficient of friction increased from 0.653 to 0.929. It then decreased to 0.890 at 20 Hz. There was an overall trend of the coefficient of friction increasing and then decreasing. The worn surface’s morphology changed from pits to bulges. At a low frequency, the wear mechanism was abrasive wear with delamination. As the frequency increased, adhesive wear was observed and gradually became the dominant wear form. Oxidation occurred on the surface of the worn tracks caused by the high temperature and thermal effect of high-frequency micromotion. Due to the intensification of the oxidation phenomenon, oxidation films formed on the worn surface. The wear rate of the coating was significantly reduced. The oxide was identified as mainly CuO and Cu2O. The presented results confirmed that the aluminum bronze coating fabricated via HVOF exhibited a great resistance performance even at a high frequency.

In recent years, the interest in using wireless communication technologies and mobile devices in the healthcare environment has increased. However, despite increased attention to the security of electronic health records, patient privacy is still at risk for data breaches. Thus, it is quite a challenge to involve an access control system especially if the patient’s medical data are accessible by users who have diverse privileges in different situations. Blockchain is a new technology that can be adopted for decentralized access control management issues. In this work, blockchain based frame work Electronic Medical Records (EMR) is applied. The proposed frame work aims at providing interoperable, secure, and efficient access to EMRs by health providers, patients and third parties while maintaining the patient’s privacy. We propose a timed-based smart contracts whose design meet the demands of EMRs. These contracts are employed in the blockchain for governing the transactions, monitoring the computations performed on the EMRs through the enforcement of the acceptable usage policies and managing the use of data after transmission. This work employs the blockchain technology with a collection of encryption techniques and hash functions. Sensitive information that are placed on the blockchain are encrypted to decrease the possibility of being accessed by unauthorized entity. Advanced cryptographic techniques are also adopted by the proposed framework for providing further security. The use of proxy re-encryption technique is employed to solve the problem of transferring encrypted messages among nodes with no need to share symmetric key. This adopts the distributed ElGamal re-encryption schema with distributed blinding technique. Our proposed framework employs the hashing method SHA-256, to ensure data integrity. The proposed system employs a new incentive mechanism integrated with the Proof of Authority (PoA) consensus algorithm for crating, validation, and appending new block. Now security and access control are maintained by the adoption of advanced encryption and authentication techniques throughout the blockchain. Interoperability, auditability, and accessibility are provided by the use of comprehensive logs. Our proposal gives efficient security and accessibility for medical records in an effective manner.

Firmware attacks on IoT devices have become a growing concern in recent years. These attacks exploit vulnerabilities in the firmware, which is the low-level software that controls the hardware of the device, to gain access to sensitive data or control the device remotely. Firmware attacks are particularly dangerous because they can be difficult to detect and can allow attackers to maintain access to a device for an extended period of time. In this paper, we explore the silent threat of firmware attacks on connected devices and the potential consequences of a successful attack. We discuss the methods that attackers use to exploit firmware vulnerabilities and the impact these attacks can have on the security and privacy of users. We also provide recommendations for protecting against firmware attacks, including keeping firmware up to date, using strong passwords, and monitoring for suspicious activity on the network. By understanding the risks and taking proactive steps to protect against firmware attacks, users can help to ensure the security and integrity of their connected devices.

Data structures are the foundation of computing, providing efficient ways to store and manipulate data. They are essential for designing and implementing algorithms that can handle large amounts of information quickly and accurately. In this paper, we explore the world of data structures, examining their basic principles, properties, and uses. We start by introducing the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs, and discuss their advantages and limitations.

As the need for machine learning models continues to grow, concerns about data privacy and security become increasingly important. Federated learning, a decentralized machine learning approach, has emerged as a promising solution that allows multiple parties to collaborate and build models without sharing sensitive data. In this comprehensive survey, we explore the principles, techniques, and applications of federated learning, with a focus on its privacy-preserving aspects

The Internet of Things (IoT) is rapidly growing and becoming an integral part of our daily lives. However, the increasing use of IoT devices also raises significant security concerns. One of the most pressing threats to IoT security is the blackhole attack, where an attacker can selectively drop or discard packets to disrupt communication between IoT devices. In this paper, we conduct a comprehensive survey on blackhole attacks in IoT networks. We explore the types of blackhole attacks, the methods attackers use to exploit vulnerabilities in IoT devices, and the potential impact of these attacks. We also review existing solutions and strategies for mitigating the effects of blackhole attacks in IoT networks. Through our survey, we provide a deeper understanding of the blackhole attack's nature and the potential implications for the security and reliability of IoT networks. Ultimately, our findings highlight the need for increased awareness of this type of attack and the implementation of robust security measures to protect IoT devices and networks.

With the increasing volume of data being generated every day, the need for data security has become more crucial than ever. Encryption is one of the most effective techniques for protecting sensitive data from unauthorized access or theft. This comparative survey aims to provide a comprehensive analysis of different data encryption methods in the context of big data. The survey covers both traditional encryption techniques and newer, more advanced methods such as homomorphic encryption and quantum cryptography. The effectiveness, advantages, and limitations of each technique are examined, and a comparison of their performance in terms of speed, scalability, and security is presented. The study also explores the challenges and issues associated with implementing data encryption in big data environments. The findings of this survey will be useful for organizations seeking to secure their big data assets and for researchers interested in the latest developments in data encryption techniques.

Social media makes it easier for people to communicate with one another online. Social media encompasses a wide range of applications and platforms, including Facebook for entertainment, Instagram for youth, Twitter for social and political, and YouTube, that let users share information, communicate online, and create communities. More than 4.7 billion individuals, or nearly 60% of the world's population, utilise social media. Twitter is a popular social media platform where users may express their feelings and opinions. In order to determine user sentiments, this Twitter sentiment analysis study uses sentiment analysis to data from tweets on the social media site. A whole new set of problems, such as the usage of slang and acronyms, are brought about by the relatively small size of the tweet format. Our objective is to carry out research on Twitter sentiment analysis while outlining the methodology, models, and generalised Python-based approach that was employed.

Abstract This study takesup” Dimensions of Gender Equality in India.”While examining the dimensions the researchers compares the gender development index reports of states in India Gender equality-a driver for economic growth, matters in its own right,and as as pre requisite for the health and development of families and economies. The term gender equality has been on the corpet for the past two decades. The millennium development goals set gender equality and women empowerment at 3 rd place. Earlier women empowerment had been assessed interns of access to resources and later on moved to control over resources. The resources include physical resources. Fiscal resopurces, financial resources and intellectual resources. The term empowerment has been pronounced with the term empowerment after the millennium year. The post 2015 framework present a unique opportunity to build on the achievement of the millennium development goal, while also addressing that lag behind. The sustainable development goal also set Gender equality at fifth place. The reason is that Gender inequality persists everywhere and stagnates the progress of a country. The un equal treatment or perceptions of the individuals on the basis of gender have been witnessed not only in developing countries like India, but also in developed countries aswell. Gender related inequality in human well being is one of the most crucial issues in many states in India.The female dis advantage in survival is the important dimension that has drawn the attention of policy makers. InIndia the situation is not even among the states; Kearela has lesser discrimination while Odissa has higher discrimination. The sex ratio is also high in kerela which shows positive symptoms of women empowerment. Maharastra introduced property rights for women. Its percapita income is high compared to kerela but GDI is low. Female Education growth in kerela promotes gender equality.Has education alone dis appear inequalities is the million dollar question? Per capita income is not so high in kerela as compared to other southern states. The study examines gender equality on the basis of Gender Development Index, which measures gender gap in human development by accounting disparities between men and women in three dimensions of human develoipme3nt, a long and healthy life, knowledge and a decent standard of living. The global gender gap report places India at 135 out of 146 countries. India has moved from 140 to 135 from last year. Though India is moving towards one of the developed nations in the world, Gender inequality pulling its growth. Which factor is hindering the SGDs 5 the goal gender equality in India? The issue of gender inequality had been in the lime light in all academic conferences, seminars and symposiums. Why gender inequality prevails in higher degree in some states of India? What are the policy measures have to be pinned up? What are the measures to be added in those states? Any other indicators to be included along with HDI indicators? These are the research questions to be addressed. This paper is an attempt made by the researchers to address these issues

The Internet of Things (IoT) has brought about significant changes to various domains such as healthcare, transportation, and manufacturing. However, security remains a critical challenge in IoT due to the large number of connected devices with varying processing capabilities and memory constraints. Traditional cryptographic algorithms are not well-suited for IoT devices due to their high computational and memory requirements. Lightweight cryptography algorithms have emerged as a promising solution for securing IoT devices with limited resources. In this abstract, we provide an overview of lightweight cryptography algorithms for IoT, including their design principles, security properties, and performance evaluation.

Datafication has emerged as a key driver of the digital economy, enabling businesses, governments, and individuals to extract value from the growing flood of data. In this comprehensive survey, we explore the various dimensions of datafication, including the technologies, practices, and challenges involved in turning information into structured data for analysis and decision-making. We begin by providing an overview of the historical context and the rise of big data, and then delve into the latest developments in artificial intelligence and machine learning. We examine the key drivers of datafication across industries and sectors, and explore the ethical, legal, and social implications of the data revolution. Finally, we consider the challenges and opportunities presented by datafication, including issues of data privacy and security, the need for new skills and competencies, and the potential for data to drive innovation and social change. Overall, this survey provides a comprehensive and up-to-date overview of the datafication landscape, helping readers to better understand and navigate the rapidly-evolving world of data.

The convergence of the Internet of Things (IoT) and blockchain technology is revolutionizing the field of security. IoT devices are becoming increasingly prevalent in our daily lives, from smart homes to wearable technology, but they also pose a significant risk to cybersecurity. Blockchain technology offers a decentralized, tamper-proof network that can enhance the security of IoT devices. By creating secure identities, communication channels, and transactions, blockchain can protect IoT devices from potential cyber-attacks. This paper explores the advantages of using blockchain to enhance IoT security and highlights the potential of this technology to create a safer and more secure IoT ecosystem

As cities around the world are growing at an unprecedented rate, there is a pressing need for smarter, more sustainable, and efficient urban infrastructure. In recent years, blockchain technology has emerged as a potential solution for various challenges faced by smart cities. Blockchain's unique features such as decentralization, immutability, and transparency offer new opportunities for secure and efficient management of city systems. This paper explores the potential of blockchain technology in smart city development, highlighting its applications in areas such as energy management, transportation, governance, and public services. By analyzing various use cases and examples, the paper demonstrates how blockchain can enhance the reliability, efficiency, and transparency of smart city infrastructure. Additionally, the paper discusses the challenges and limitations of blockchain adoption in smart cities and suggests potential solutions for overcoming these obstacles. Overall, this paper argues that blockchain technology has the potential to transform the future of smart city development, enabling cities to become more resilient, sustainable, and citizen-centric

"This article considers the formation of research skills in students and pupils. The first part of this article displays the main aspects of the formation of research skills among students, why it is important, what skills a student or student acquires during the research, how effective this approach is in learning a foreign language, and how much it helps to form a student’s interest in the subject. In the course of writing the article, it was revealed that this approach is effective and helps to diversify the conservative methods of teaching English, also increases the level of interest in the subject being studied, forms the skill of working in a team, and also forms the skill of conducting research. Within the framework of this study, a review was made of a study conducted by students of grade 6 on the topic of non-verbal communication methods in the Kazakh and English languages. The similarities and differences in the use of these methods in both languages ​​were identified, and a comparative analysis of the etymology of the origin of certain means of transmission of non-verbal communication methods was also carried out. "

PCOS, commonly known as hyperandrogenic anovulation, is a condition that affects women's ovaries. A frequent endocrine system illness that affects women of reproductive age is polycystic ovarian syndrome (PCOS), also known as hyperandrogenic anovulation (HA), or Stein-Leventhal syndrome (Evans and Riley, 1958). According to Stein and Leventhal (1935), it is a condition that develops on one or both ovaries when an estimated 10 tiny cysts with a diameter ranging from 2 to 9 mm and/or an ovarian volume greater than 10 ml are present. According to a systematic examination of women using National Institutes of Health (NIH) diagnostic criteria, 4–10% of women of reproductive age have PCOS. Recent research reveals that PCOS is a lifelong syndrome that first manifests during pregnancy, despite the fact that it was traditionally thought to be a disorder that only affected adult women. It is a syndrome that can be avoided by raising awareness among patients and healthcare professionals. Starting with its diagnosis, pathophysiology, repercussions, and treatment options, there are numerous areas of disagreement(1).

Automatic summarization is the act of computationally condensing a set of data to produce a subset (a summary) that captures the key ideas or information within the original text. To do this, artificial intelligence algorithms that are tailored for diverse sorts of data are frequently created and used. Ten research articles considering databases like IEEE, Scopus, and Springer Nature have been considered. The paradigm shift that AI has created in the field of Automatic Text Summarization is discussed in detail.

"The article is devoted to the scoring system of lending in second-tier banks. Namely, a decision-making system in which the integration of two scoring models (banking and insurance) leads to an assessment of the reliability of the borrower and the potential risks of the bank, as well as the use of social networks to determine the riskiness of the client. Bank and insurance scoring is compared. The article includes methodologies, processes and data that are used in the development of credit scoring. The advantages of creditworthiness assessment methodologies are described without using the data of insurance companies. Analytical methods are used, receiving data from social networks. Statistical theory is applied to real situations of credit scoring. The article is useful for working people working in the financial sector. These include credit officers, risk managers and analysts, IT-specialists. IT infrastructure, operating procedures, business processes are being studied. An overview of IT tools for the implementation of the scoring system product is given, which increases the efficiency of the scoring system. Keywords: system, scoring, model, risk, income, borrower, lending."

Economic development in an area requires encouragement between the local government and the community to grow regional economic activities by managing existing resources, establishing relationships between government and private agencies, and encouraging development related to regional economic activities. The development of a region cannot be separated from national development because regional governments and local communities need coordination to utilize, create, develop, and produce existing resources. This study aimed to describe the potential of the economic sector in Surabaya in the period of 2015-2019. This research uses secondary data for five years in the 2015-2019 period. The analytical method used analyzes location quotient, shift share, and Klassen typology. The results of the study revealed that five sectors were included in quadrant I, and two sectors were included in quadrant IV. In conclusion, Surabaya has fifteen economic sectors that drive economic progress, ten sectors grow relatively fast, nine industries have outstanding location advantages, and five sectors belong to quadrant I. This research is used as a reference in making policies related to economic progress in Surabaya, which impacts increasing employment and per capita income.

The Recent Technology in banking has become even more evident in recent years, as phone, online and mobile banking has revolutionize the way we take care of our finances. In fact, one might argue that the impact of technology in the banking sector has meant that we now have no excuse for being overdrawn or not paying credit card bills on time. In previous generations, the only way to find out how much money you had in the bank was to keep a detailed log book or pay a visit to your local branch and ask the teller to check the ledger for you. Then along came the innovation known as the ATM machine, which allowed us to withdraw cash from hundreds of convenient locations and check our balance while we were at it. Nowadays, with mobile banking apps it’s possible to check your balance any time any place. So before you splash out on a new stereo or home entertainment system you can check your account then and there just to be sure you can afford it. Another one of the Advantages of technology in banking is that it allows us to pay bills quickly and without physical visit. We can arrange to have them paid by direct debit every month at a time that suits us, or we can make one off transfers as and when the bills come in. This means that there’s no longer any need to pay a physical visit to your branch to pay bills and it also means we are more likely to pay them on time. People who find it more difficult to manage their money can arrange for as many bills as possible to be paid just after their salary clears their account so that they know the essentials are covered before they can spend on luxuries. In this paper has been made an attempt on “Technology adoption in Indian Banking Sector.

Knowledge on the dynamic properties of bridges improves condition assessment, maintenance scheduling and emergency planning to better serve the public. The Crack inspection is an important task in the maintenance of bridge and it is closely related to structural health of the bridge. It is done through a very manual procedure, an experienced human inspector monitors the whole bridge visually and try to detect cracks on the bridge and marks the location of the crack. The proposed system is implemented using a real time wireless sensor network for bridge monitoring is of lossless data transmission over several minutes continuously. Overall information about the bridge will be stored on the cloud. The cracks will be detected automatically and send the real time data with location of crack to the PWD department. If bridge will be collapse because of any accident or a flood that time the barriers will be open and the Vehicles will stop at some distance, and also notify about the accident to the nearby government departments like Police station, Hospitals and Society. After the accident detected by system up to previous and next 5KM the alert notification will shows another route for travellers.

The aim of this research is to investigate the effect of economic, social and institutional factors on adoption within the national program for the propagation of high-rank seeds of wheat crop. 170 questionnaires were collected, 50% of them were participants in the program from Wasit and Babil governorates. Probabilistic regression models were used to know this effect, and the possibility of adopting Farmers of improved seeds produced from the national program for the multiplication of seeds of higher grades using the (ADOPT) program. The adoption rate was 0.12%, and the total number of adopters were 12 farmers, at a rate of 14.2%. Tobit model was estimated to find out the impact of the profitability of the dunum, capital, farm size, education, type of tenure, and the number of adoption cycles expressed by 1 in the case of adoption and 0 otherwise. It was found that the increase in the size of the farm and the profitability of the dunum by one unit, the probability of adoption will increase by 5.6 and 2.3, respectively. The results of the ADOPT analysis showed that after the first (5) passes after the launch of the program’s seeds and the farmers’ knowledge of its existence, the adoption rate is expected to reach about (90.9%) of farmers, and reaching the top of adoption, which reaches about (95%) of farmers, requires a period up to about (7.4) years. The size of the initial cost of the investment in relation to the potential annual returns from the use of the technology: The size of the initial investment is (low) cost, the seeds produced from the program had a lower cost than the imported seeds and are competitive with them in quality, purity, and desirable genetic characteristics , so the cost the initial investment for these seeds is small compared to the expected return from the seed output.

The electrical efficiency of solar photovoltaic (PV) panels depends on their temperature. One of the significant problems consists in the overheating due to the total radiation energy, the ambient temperature, and the low capacity to dissipate this thermal energy. To improve the efficiency of solar panels, a numerical study was carried out using the ANSYS-Fluent 2021 commercial software in which the heat transfer between a solar panel with and without heat sinks was modelled, determining the incidence of fins in power generated by the photovoltaic cell. For the development of the study, initially, the theoretical calculation of the heat transfer and the generated power that occurs in the cell with and without a heat sink was carried out. Therefore, numerical simulation was conducted to analyse the effect of the geometry of the heat sink on the efficiency of the photovoltaic cells; different arrangements of rectangular fins were taken, varying their height (10 mm, 25 mm, and 50 mm). For the model’s configuration, boundary conditions corresponding to physical phenomena such as solar radiation and forced convection were considered. Results show an increase on the solar PV panel efficiency of 0.36%, 0.72%, and 1.07% for the height heat sinks of 10 mm, 25 mm, and 50 mm compared to the commercial PV solar panel without heat dissipation, respectively

Any information that has been produced with the intention of deceiving readers and disseminating a concept for the purpose of gaining monetary advantage (usually political or financial) is considered fake news. Knowledge acquisition and dissemination are quick and almost free in today's society. More people than ever are using the internet. Internet channels are therefore ideal for spreading knowledge to a larger audience. What was once restricted to a select few may now instantly spread across the globe. This benefit came at the same time that its users started using false news more frequently, which is not good for a healthy society. Consequently, excellent algorithms are required to immediately identify and remove fake content. This study plans to track down an answer for the issue via mechanizing the most common way of distinguishing counterfeit news in view of its substance. Measures like the f1-score, exact grouping exactness, accuracy, and review are utilized to assess the methodology's presentation. With an accuracy of 96.7 percent, precision of 96.2 percent, recall of 97.5 percent, and f1 score of 96.9 percent, the machine learning(ML) technique played out the best in the ISOT dataset.

A distributed system is a software application that makes use of a collection of protocols to manage the activities of numerous processes running on a communication network, so that each part cooperates to finish a single or a condensed number of related tasks. Representational State Transfer (REST) API is also integrated into this, making it simple to access online services without the need for additional processing. The server sends a client-side representation of the requested resource whenever a RESTful API is used. It is also integrated with microservices through the open-source message-broker programme RabbitMQ. With little developer involvement, the configuration file must automatically network new messaging services that join the distributed system.

The first paracetamol was created in 1877, or even 1852. In both the US and Europe, it is the most frequently prescribed drug for pain and fever. It is listed as one of the Essential Medicines by the World Health Organization. Tylenol and Panadol are just a couple of the brand names for the generic drug paracetamol, which is also sold under numerous names. It received more than 5 million prescriptions in 2020, ranking it as the 118th most popular drug in the country..Acetaminophen, usually referred to as paracetamol, is a drug used to treat fever and mild to moderate discomfort.Tylenol and Panadol are examples of popular brand names.The benefits of paracetamol use for fever are unclear because, at a typical dose, it only modestly lowers body temperature; in that regard, it is inferior than ibuprofen. Acute mild migraines may be helped by paracetamol, however recurring tension headaches may only be minimally relieved. Ibuprofen is superior to paracetamol in terms of effectiveness for post-surgical pain management. The medicine of choice for lowering fever is paracetamol. It’s unclear whether paracetamol has any advantages in musculoskeletal problems like osteoarthritis and backaches.When used to treat mild to moderate pain, paracetamol can help with headaches, muscle pains, minor arthritic pain, toothaches, and discomfort from the common cold, the flu, sprains, and dysmenorrhea. It is particularly advised for acute mild to moderate pain because there is little evidence to support its use in the treatment of chronic pain. At a dose of 4gram per day, paracetamol may marginally elevate systolic blood pressure in hypertensive patients. Although it’s uncertain whether paracetamol is the real reason of this increase, studies show that children of mothers who used paracetamol for an extended period of time during pregnancy had higher rates of asthma, developmental, and reproductive issues. A maximum of three to four grammes per day is advised for adults. Toxicity from higher doses, including liver failure, is a possibility. Most drug overdoses in the US, UK, Australia, and New Zealand are caused by paracetamol poisoning, which is the leading cause of acute liver failure in the Western world.

Kecambah kacang hijau (Vigna radiata L.) berasal dari kacang hijau merupakan salah satu jenis kacang yang relatif banyak dikonsumsi masyarakat sebagai makanan fungsional. Kacang hijau berasal dari famili Fabaceae. Kacang hijau dan kecambahnya bermanfaat banyak bagi kesehatan. Kecambah kacang hijau mengandung Vitamin E sangat tinggi sehingga baik bagi peremajaan kulit dan juga membantu wanita terhindar dari kanker payudara, gangguan menstruasi dan gangguan akibat menopause. Penelitian ini bertujuan untuk mengetahui aktivitas sitotoksisitas ekstrak etanol kecambah kacang hijau dan juga senyawa metabolit sekunder yang terkandung dalam kecambah kacang hijau. Pengujian skrining fitokimia dan evaluasi sifat digunakan pada penelitian ini. kecambah kacang hijau. Pengujian sitotoksisitas ekstrak kecambah kacang hijau menggunakan beberapa variasi konsentrasi metode Brine Shrimp Lethality Test. Hasil uji fitokimia terhadap ekstrak kecambah kacang hijau (Vigna radiata L.) mengandung senyawa alkaloid, flavonoid, glikosida, saponin, tanin dan steroid/triterpenoid. Hasil pengujian karakterisasi kecambah kacang hijau pada kadar air 8,66 %, kadar sari larut air 36,54%, kadar sari larut etanol 24,62 %, kadar abu total 7,655%, kadar abu tidak larut asam 1,078%. Hasil analisa probit pengujian sitotoksisitas ekstrak etanol kecambah kacang hijau dengan metode BSLT diperoleh nilai LC50 262,7241 µg/ml, dengan kategori toksik dan berpotensi sebagai antikanker.

Kanker adalah pertumbuhan sel yang ganas dan abnormal sehingga dapat merusak jaringan tubuh yang dapat menyebabkan melemahnya kondisi tubuh dan juga mengakibtkan kematian. Indonesia memiliki beragam tumbuhan yang dapat digunakan sebagai pengobatan. Daun bambu (Dendrocalamus asper (Schult.f.) Backer) merupakan salah satu yang dapat dimanfaatkan yang memiliki metabolit sekunder, salah satunya flavonoid. Metode BSLT digunakan dalam ini agar mengetahui sitotoksisitas ekstrak etanol daun bambu sehingga dapat dilakukan penentuan nilai LC50 Uji yang dilakukan diantaranya skrining fitokimia dan uji sitotoksisitas ekstrak daun bambu menggunakan metode BSLT. Metode BSLT dilakukan dengan cara melihat jumlah larva yang mati akibat pengaruh ekstrak dengan melihat hasil nilai LC50, konsentrasinya 100, 200, 300, 400, 500, 600, 700, 800, 900, dan 1000µg/mL. Hasil yang didapatkan dari uji skrining metabolit sekunder diketahui mengandung tanin, steroid, flavonoid, saponin dan alkaloid pada ektrak etanol daun bambu. Nilai LC50 sebesar 364.5860 µg/mL diperoleh dari uji sitotoksisitas yang menunjukkan bahwa ekstrak etanol daun bamboo berpotensi sebagai antikanker namun bersifat toksik.

Objective: The aim of this study to synthesize, characterize and screen some new isatin derivatives for their antimicrobial activities. Method: A library of isatin derivatives were synthesized, structures of newly synthesized compounds were deduced based on spectral data and elemental analysis. Results: Antibacterial activity was screened against gram positive and gram negative bacterial strains. Conclusion: The novel isatin derivatives produce significant antimicrobial activities.

There will be no computer-editable text in the image file. The method of optical character recognition (OCR), which can read handwritten or printed text in images, turns that text into a file that can be edited on a computer. English has a well-developed OCR system. OCR is currently required for Indian languages in order to process application forms, categorise books in libraries, and preserve historical records that are mostly written in Indian languages. The Telugu language presents challenges for OCR since each character is made up of a single consonant, a single vowel, or a compound of vowels and consonants.

Image classification is a part of computer vision, in which the digital system categorizes the entire image. Deep Learning (DL) models are widely used for image classification. However, creating DL models is resource-intensive and time-consuming, and requires extensive knowledge in the DL domain. Google Teachable Machines (GTM) is a website that outputs a trained model given the dataset, however, GTM uses only the MobileNet model and does not balance the image dataset which affects the model’s accuracy. This paper proposes a tool that automates the steps in building and training an image classification model. Using this tool does not require any extensive knowledge in DL. The tool automates the image data pre-processing steps, model building, model training, and model testing to output the best model for the given image classification dataset based on the test accuracy. The tool is tested on two datasets (each balanced and unbalanced dataset): a custom construction dataset and a Minet dataset. Both datasets are also used to train models using the GTM website. Due to the automated pre-processing steps, the average increase in the accuracy is 14.55% in the construction dataset and 3.91% in the Minet dataset. Comparing to the GTM models, the tool produced model with 8.33% more accuracy on the construction dataset and model with 14.07% more accuracy on the Minet dataset. The models trained by the proposed tool have better accuracy compared to the models obtained using GTM. Thus, using the image classification model selector facilitates the creation of an effective image classification model for the target dataset.