Identification of Phishing Attacks using Machine Learning Algorithm

Phishing is a particular type of cybercrime that allows criminals to trick people and steal crucial data. The phishing assault has developed into a more complex attack vector since the first instance was published in 1990. Phishing is currently one of the most prevalent types of online fraud behavior. Phishing is done using a number of methods, such as through emails, phone calls, instant chats, adverts, pop-up windows on websites, and DNS poisoning. Phishing attacks can cause their victims to suffer significant losses, including the loss of confidential information, identity theft, businesses, and state secrets. By examining current phishing practises and assessing the state of phishing, this article seeks to assess these attacks. This article offers a fresh, in-depth model of phishing that takes into account attack stages, different types of attackers, threats, targets, attack media, and attacking strategies. Here, we categorise websites as real or phishing websites using machine learning techniques including Random Forest, XGBoost, and Logistic Regression. Additionally, the proposed anatomy will aid readers in comprehending the lifespan of a phishing attack, raising awareness of these attacks and the strategies employed as well as aiding in the creation of a comprehensive anti-phishing system

IOT Based Smart Farming Application

Smart agriculture is one of the Internet of Things' most important uses. Water, fertilizer, and crop yield waste are all reduced via smart agriculture. The manual detection of specifications like temperature, moisture, and humidity in the existing agricultural system drives up labor costs, and continuous monitoring is not possible. The irrigation procedure is carried out automatically in this study utilizing various sensors, which reduces manual work. It is suggested to utilize a sensor-based monitoring system for crop fields. It would entail gathering information on the soil moisture, humidity, and temperature. Automation of irrigation is possible by keeping an eye on all these variables. Unquestionably, smart farming is a key facilitator in providing more food with less resources for a growing global population. While this is essential to feeding the world's expanding population responsibly, smart farming also offers producers and communities throughout the world additional advantages. Farmers may raise yields and improve environmental management by using these strategies. By monitoring the field, IoT-based smart agriculture enhances the overall farming system. The Internet of Things in agriculture helps farmers save time and lessen the usage of resources like water thanks to sensors and connections. electricity, internet-connected temperature monitoring

Smart Wearable Gadget for Miners Using IOT

Safety is the most important part of any kind of assiduity is safety. In extreme circumstances, safety-related negligence could result in the destruction of expensive clothing or the loss of human life. Every min-ing diligence adhere to a few basic preventative measures in order to avoid any generally unwelcome wonders. The most important component at this time is communication in order to continuously monitor various pa-rameters and take the appropriate actions as a result to avoid any risks linked with the product or the management of mortal funds. A stable and wide-range effective communication system between personnel in the mine and the control centre must be built in order to increase safety in un-derground mines. The cable communication network technology is inef-fective within underground mines. Here we can tackle the matter of acci-dents which end with death of several people per annum. It is discovered that the speed of fatality within the coal pit industry is almost six times the speed for all private industries. And most of those accidents are because of toxic gases, fires, and a lack of rescue systems. By implementing mine surveillance gadgets, which may be used within the mine and detect the number of various gases, fall, emergency detection and report to them. This article focuses on the design and analysis of the smart wearable gadget for miners in the mining industry using IoT

IOT Based Real Time River Water Quality Monitoring and Control System

Water quality monitoring systems currently in use are manual and involve tedious processes that are time intensive. This research suggests a system with sensors for water quality monitoring. Access to real-time data may be obtained through remote monitoring and the Internet of Things. A wireless sensor network (WSN) contains a micro-controller for data processing, a mechanism for communicating between and inside nodes and many (IoT). Using Spark flow analysis with Spark MLlib, deep on it. The agent will receive a warning SMS automatically if the detected value exceeds the threshold. Our plan to develop a high- frequency, high-mobility, low-power water monitoring system makes it special. As a result, the Bangladeshi people will find our proposed approach highly useful in raising awareness of and putting an end to water pollution

Noise Level Notifier

This paper involves utilizing an Arduino Uno as the primary hardware to measure the sound level in a library. The amount of noise in the region is measured using a sound sensor. The sound sensor signals are amplified using the operational amplifier function of the integrated circuit LM 567. There are two types of output available: audio and visual. The audio output takes the form of a personalized message that is played over speakers. LEDs are employed to offer visual feedback, with white LEDs used in noise-free environments (sound level 45 dB, yellow LEDs used when sound levels are above 65 decibels, and red LEDs used when sound levels are significantly above 80 decibels). A TIP 220 transistor is used to amplify the signals. A TIP 220 transistor amplifies the signals to create an output for the speaker. There is an audio message that corresponds to each sound level

Comparison of California Mastitis Test and somatic cell counts for detection of subclinical mastitis in crossbred cattle

Mastitis is a major constraint that severely affects milk production in dairy animals. The California Mastitis Test (CMT) is a reliable and rapid field test for the diagnosis of subclinical mastitis (SCM) which gives an indirect estimate of somatic cell count (SCC). Based on the results of CMT screening and SCC of the milk of 105 crossbred animals located in different farms in Wayanad and Calicut districts of Kerala state, the present study attempts to find the estimates of sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV), false discovery rate (FDR) and false omission rate (FOR) of CMT relative to SCC as the reference standard. The correlation and agreement between CMT and SCC were also analysed. The estimated Spearman and Kendall Tau b correlation coefficients were 0.88 and 0.76, respectively, which indicated a strong positive relationship between CMT and SCC. The sensitivity and specificity values of CMT were 1.000±0.000 and 0.510±0.071, respectively. These values indicate that the probability for an animal with mastitis to be identified using CMT is 100 per cent and the probability of correctly identifying an animal without mastitis animal is 51 per cent. The high sensitivity value of CMT in this study indicated that CMT could be used to find out the true prevalence of SCM in crossbred animals. Analysis of the data also revealed that CMT had a PPV of 0.700±0.051 and an NPV of 1.000±0.000. The calculated accuracy of CMT was 0.771±0.041. The estimated FDR and FOR were 0.300±0.051 and 0.000±0.000, respectively. Kappa statistic was used to determine the level of agreement between CMT and SCC and the kappa coefficient value was 0.53±0.07 which indicated moderate agreement

Biogas Production from Kitchen Waste Using Flexible Balloon Digester

Flexible balloon biogas plant based on kitchen waste was designed and installed for environmental-friendly disposal of around 250 kg waste generated per day in the kitchen of student mess at Sainik School, Chittorgarh, Rajasthan. The digester balloon having volume of 25 m3 was fabricated with high tenacity rubberized nylon fabric coated with hypalon on outer and neoprene on inner surface. A stirring unit was provided for mixing the digested slurry inside the digester with the compressed biogas. Average biogas generated was 0.0439 m3.kg-1 dry matter having methane and carbon di-oxide about 67.70% and 32.30%, respectively

Microbiological Characterization and Antibiotic Susceptibility Pattern of Haemophilus Influenzae Isolates from a Tertiary Care Centre in South India

Haemophilus are fastidious Gram negative bacilli, which require factor X (hemin), factor V (NAD), or both for their growth. Haemophilus influenzae is the type species, and is considered to be the most pathogenic. They are associated with many invasive infections including meningitis, epiglottitis, pneumonia, and otitis media. Serotype b is most commonly associated with infections. Haemophilus species isolated from patients in a tertiary care centre in South India were studied. Identification, serotyping and biotyping were done and antibiotic susceptibility test was performed. The incidence of H. influenzae infections in our study was 65.3 cases/100,000 persons. Serotype b was the most common (66.67%), followed by non typeable H.influenzae (NTHi) (25%). Most isolates from adults were type b, while all isolates from pediatric population were non typeable. The most common biotype was type II, followed by type I and type III. Three of 24 isolates were β lactamase producers (12.5%). One isolate was β lactamase negative Ampicillin resistant (BLNAR). Resistance to ampicillin was 16.67%. Resistance to cephalosporins and fluoroquinolones was low (4-10%). Co-trimoxazole resistance was found to be very high (75%). All isolates were susceptible to azithromycin, tetracycline, chloramphenicol and meropenem. No isolates of H.influenzae type b were obtained from the paediatric population which may be due to the introduction of Hib vaccine. The increase in resistance to commonly used antibiotics is worrisome, especially penicillins and co-trimoxazole. Use of co-trimoxazole in empirical therapy of upper and lower respiratory tract infections has a high chance of failure in the current scenario

IOT Based Smart Farming Application

Smart agriculture is one of the Internet of Things' most important uses. Water, fertilizer, and crop yield waste are all reduced via smart agriculture. The manual detection of specifications like temperature, moisture, and humidity in the existing agricultural system drives up labor costs, and continuous monitoring is not possible. The irrigation procedure is carried out automatically in this study utilizing various sensors, which reduces manual work. It is suggested to utilize a sensor-based monitoring system for crop fields. It would entail gathering information on the soil moisture, humidity, and temperature. Automation of irrigation is possible by keeping an eye on all these variables. Unquestionably, smart farming is a key facilitator in providing more food with less resources for a growing global population. While this is essential to feeding the world's expanding population responsibly, smart farming also offers producers and communities throughout the world additional advantages. Farmers may raise yields and improve environmental management by using these strategies. By monitoring the field, IoT-based smart agriculture enhances the overall farming system. The Internet of Things in agriculture helps farmers save time and lessen the usage of resources like water thanks to sensors and connections. electricity, internet-connected temperature monitoring

Identification of Phishing Attacks using Machine Learning Algorithm

Phishing is a particular type of cybercrime that allows criminals to trick people and steal crucial data. The phishing assault has developed into a more complex attack vector since the first instance was published in 1990. Phishing is currently one of the most prevalent types of online fraud behavior. Phishing is done using a number of methods, such as through emails, phone calls, instant chats, adverts, pop-up windows on websites, and DNS poisoning. Phishing attacks can cause their victims to suffer significant losses, including the loss of confidential information, identity theft, businesses, and state secrets. By examining current phishing practises and assessing the state of phishing, this article seeks to assess these attacks. This article offers a fresh, in-depth model of phishing that takes into account attack stages, different types of attackers, threats, targets, attack media, and attacking strategies. Here, we categorise websites as real or phishing websites using machine learning techniques including Random Forest, XGBoost, and Logistic Regression. Additionally, the proposed anatomy will aid readers in comprehending the lifespan of a phishing attack, raising awareness of these attacks and the strategies employed as well as aiding in the creation of a comprehensive anti-phishing system